diff --git a/code/estimark/parameters.py b/code/estimark/parameters.py
index 87d92c5..e0767ea 100644
--- a/code/estimark/parameters.py
+++ b/code/estimark/parameters.py
@@ -6,6 +6,10 @@
 # income uncertainty doubles at retirement
 # only estimate CRRA, Bequest params
 
+import warnings
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
 import numpy as np
 from HARK.Calibration.Income.IncomeTools import (
     Cagetti_income,
diff --git a/code/notebooks/Portfolio.ipynb b/code/notebooks/Portfolio.ipynb
index 8019e95..7f2e5a0 100644
--- a/code/notebooks/Portfolio.ipynb
+++ b/code/notebooks/Portfolio.ipynb
@@ -33,7 +33,7 @@
     {
      "data": {
       "text/plain": [
-       "(6.320605981087387, 1.0)"
+       "(6.374030002146488, 1.0)"
       ]
      },
      "execution_count": 3,
@@ -103,7 +103,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -123,7 +123,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -202,7 +202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -229,7 +229,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/code/notebooks/SCF_notebook.ipynb b/code/notebooks/SCF_notebook.ipynb
index a588ea0..09c21cc 100644
--- a/code/notebooks/SCF_notebook.ipynb
+++ b/code/notebooks/SCF_notebook.ipynb
@@ -162,7 +162,38 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
+      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  dfg = df.groupby(\"vec\").agg(np.sum)\n"
+     ]
+    }
+   ],
    "source": [
     "moments = get_weighted_moments(\n",
     "    data=scf_data,\n",
@@ -181,18 +212,18 @@
     {
      "data": {
       "text/plain": [
-       "{'(25,30]': 0.6576324884792626,\n",
-       " '(30,35]': 0.9736088481325308,\n",
-       " '(35,40]': 1.781723874904653,\n",
-       " '(40,45]': 2.389100631076241,\n",
-       " '(45,50]': 3.236815284741492,\n",
-       " '(50,55]': 4.244881311689372,\n",
-       " '(55,60]': 5.328767471857411,\n",
-       " '(70,75]': 8.802984212552946,\n",
-       " '(75,80]': 9.853136010240991,\n",
-       " '(80,85]': 8.755303437164338,\n",
-       " '(85,90]': 11.361794223826717,\n",
-       " '(90,95]': 9.975607096117496}"
+       "{'(25,30]': array([0.65763249]),\n",
+       " '(30,35]': array([0.97360885]),\n",
+       " '(35,40]': array([1.78172387]),\n",
+       " '(40,45]': array([2.38910063]),\n",
+       " '(45,50]': array([3.23681528]),\n",
+       " '(50,55]': array([4.24488131]),\n",
+       " '(55,60]': array([5.32876747]),\n",
+       " '(70,75]': array([8.80298421]),\n",
+       " '(75,80]': array([9.85313601]),\n",
+       " '(80,85]': array([8.75530344]),\n",
+       " '(85,90]': array([11.36179422]),\n",
+       " '(90,95]': array([9.9756071])}"
       ]
      },
      "execution_count": 4,
@@ -212,18 +243,18 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axis.XTick at 0x7f277d91e780>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d91e2d0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f2884502000>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277dc04410>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d95aea0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d95b7d0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d738200>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d738bc0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d95b050>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d7394f0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d739e80>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d73a7b0>],\n",
+       "([<matplotlib.axis.XTick at 0x7fb5524c86b0>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb5524c8650>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb5529db680>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb5529e6000>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552500bf0>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552501550>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552501e80>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb5525027b0>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552503170>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552502150>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552503a40>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb5525283e0>],\n",
        " [Text(0, 0, '(25,30]'),\n",
        "  Text(1, 0, '(30,35]'),\n",
        "  Text(2, 0, '(35,40]'),\n",
@@ -514,11 +545,11 @@
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axis.XTick at 0x7f277d95a090>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d759d00>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d959ee0>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d7ab560>,\n",
-       "  <matplotlib.axis.XTick at 0x7f277d7abe90>],\n",
+       "([<matplotlib.axis.XTick at 0x7fb552537290>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552536360>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb55252bbc0>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552346900>,\n",
+       "  <matplotlib.axis.XTick at 0x7fb552347290>],\n",
        " [Text(0, 0, '(70,75]'),\n",
        "  Text(1, 0, '(75,80]'),\n",
        "  Text(2, 0, '(80,85]'),\n",
diff --git a/code/notebooks/WarmGlowPortfolio.ipynb b/code/notebooks/WarmGlowPortfolio.ipynb
index e9e8e71..444359f 100644
--- a/code/notebooks/WarmGlowPortfolio.ipynb
+++ b/code/notebooks/WarmGlowPortfolio.ipynb
@@ -33,7 +33,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "(4.497998813498684, 46.24342040168151, 16.449904656848112)"
+                            "(4.705614650734349, 46.46325681615148, 16.964074896995744)"
                         ]
                     },
                     "execution_count": 3,
@@ -71,7 +71,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -91,7 +91,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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oSSI9LVJKLQiCIAjvALJPwOjDBYSt22hmJgJzYgVSitGlypx71IvApLrxduoqymIEIgR9qstvtFhFYCp61diC5pBaU2lLKbUgCIIgvBPIOgHj6s7QP3AMB83QcWwVgTldCqnGi8A0dQ9PIbW6BratRE8o18J1kwwGywHI7/Z6wfiVf0aMvIIgCILwzuCsBMxdd91FXV0dwWCQhQsXsmnTptOuTSaTfOMb32DcuHEEg0FmzZrFk08+edY3jJERMJqrYZuAbqRTSKcTMKM9AXO0WwmQlIm3y3bpj6kqpWC+i+uEGURFXIIdqhdMsxf1qdHaOSICRhAEQRDeds5YwDz00EOsXr2aW2+9lS1btjBr1ixWrlxJe3v7Kdd/7Wtf46c//Sk//OEP2bNnD5/73Oe4+uqr2bp161ndsHuCPnFM0AwD11WP4jdP/UgpD0xbf5xY0qbIZ1JoqpO1WKr7rlGYxHXChKMqIqO1dqI5Lh1ugrimIjBHpReMIAiCILztnLGAufPOO7n++utZtWoVU6dO5e677yYnJ4f77rvvlOt/+ctf8tWvfpUrrriCsWPHcsMNN3DFFVdwxx13nN0dD+nxorkOruGiGRqOqyIwpq6dcltRjo/8oBImmTSSisK0u8rzYuTFcJ0wfT0WmCZYFiNjapRAi9cL5kinlFILgiAIwtvNGQmYRCLB5s2bWbFiReYEus6KFSvYsGHDKffE43GCweCwY6FQiPXr15/F7YJjZky6hp3ANV10w8D1BIzPOPUjaZp2UiVSysjbpatuvOQO4jr9RAeS4A11nBxXvWBaTJMavZ0GicAIgiAIwtvOGQmYzs5ObNum0mv0lqKyspLW1tZT7lm5ciV33nknBw8exHEcnnnmGR599FGOHz9+2uvE43H6+/uHfVJoPl/6ezAWxTFAM3RcxxMwp4nAAIz2esGkKpFSQx27zRFqQU4Y01S/xasnAzA2qmYlNZsmI+iipasfW0qpBUEQBOFt5ZxXIX3/+99nwoQJTJ48Gb/fz4033siqVavQ9dNf+vbbb6ewsDD9qampSf/mmiaT9u0jrz/MkvUv4Zoumq7heI9yuggMZCqRTu4FUwqAFgqjG6pTb6xUpZUyvWACGJpLidPJ8b7oWb0LQRAEQRDeGs5IwJSVlWEYBm1tbcOOt7W1UVVVdco95eXlPPbYYwwODnL06FH27dtHXl4eY8eOPe11br75Zvr6+tKfpqamzI8+P7O3bee9TzxBYX8/mgEYGnhREdM4fQTmpGZ2Xil1l68AAD3Qj2OraE8kR0WZyrxeMC05qjJJlVKLD0YQBEEQ3k7OSMD4/X7mzZvHmjVr0sccx2HNmjUsWrTodfcGg0Gqq6uxLIvf/e53vP/97z/t2kAgQEFBwbDPkJsYtlYzXBxdR/OyOr7XieyMLlHN7E6MwHRofmxXRzNsfPoArusSMQoBKOhWjeuafWqtmokkPhhBEARBeDs54xTS6tWrueeee3jggQfYu3cvN9xwA4ODg6xatQqAa6+9lptvvjm9fuPGjTz66KPU19fz4osv8p73vAfHcfiXf/mXs7thc7ghGMPF1Q1wlYLxvcEIjOO4jAz4MTRIoNGRUD6YUK4LboxBS10n0N4HQLOmIjHSzE4QBEEQ3n5O3Xf/dbjmmmvo6OjglltuobW1ldmzZ/Pkk0+mjb2NjY3D/C2xWIyvfe1r1NfXk5eXxxVXXMEvf/lLioqKzuqGDTOApYPp9bPTDBfX0CEVgXkdD8zIoiCGrpGwHNrDcaoKg4wK+DkaS9CcrKUq0ExOIfQNhBkYVOZd2jvRHejWE0Q0jRqtne0SgREEQRCEt5UzFjAAN954IzfeeOMpf1u3bt2wvy+66CL27NlzNpc5JabhJ+EDU3X5RzddXD3jgQm8joAxDZ3qohCN3RGOdg1SVRhkdEgJmONOtdqf7+A6YWKRCqxgHmZsgJpYIUdzBjluGtTYHTR0iQdGEARBEN5Osm4Wkqn7sYbctW54AsZN/X76FBJkSqkzPhhl5O1ApZCM/ASGT0VYrFGTAJgUV6MGmr1mdo1dESmlFgRBEIS3kSwUMAHcoRollT3yPDDm65h44eRS6lqvG2+3oVJgel4Mn1/9lqxUlVJjI8r822yaVGi9aHaMll4ppRYEQRCEt4usEzA+I4g95K41Q8PV3EwE5nVMvJAZ6pgSMJlmdmXqfDmDaJqKwMSKVFppZFg1z2sJqL2jtI50MzxBEARBEP76ZJ2AMY0A1pCBjgYaruagpSMwry9gUpVIKQGSKqVO94IJhXG9XjCxkBI15X3KMdwcUsbeGq2dI2LkFQRBEIS3jawTMD4zNMwDY7i6Sh95VUlmysTbsg32PZ5OLaWo9TwwJw50DBsBYgTQ/TGsZBcAEV2JmrwulS5q9sYYSCm1IAiCILy9ZKeAGRKB0V0dZ2gKSdfAceDX18CDH4ftDw7bn4rAdA0mGIhbFPpMikx1wlZrJACu3oXrOgwmlWAJtHm9YLAAJWCOSgRGEARBEN42sk7A+I1TCBjXTUdaDF2DrkMw4A2X3PKLYfvzgz5KclXUpbFr+FTqZms0AIE8C9xBwgOu0kUdXeiOS5+bZMDrBXNEIjCCIAiC8LaRfQLGl3OSgLGHRGB8hgZNGzML2napiMwQMpVISoSkjLzHbWXazSnQcJ0wdtIlmVsKts2YmEonpUqpm7qjWPbw8wqCIAiC8Nch+wSMPzS8Csk1cHDSjewMXR8uYOL90H142DlOrERK+WDaUQMpg/kugZDyvSRP6AXT4jOp1TtI2A4H2wfe4qcTBEEQBOGNkHUCxmeEsIaUSuuujuueGIHZNHxT85Zhf55cieQ1s9PK1TkKLHwBJWAS5WMAGDOkF0wBgxQwyM5jfW/hkwmCIAiC8EbJOgFj+INsnKK+Hy8G3TWwXDddRh20+qBzv1ow4yPq35YTBMyJ3XhTzezMUgDMvDi6HgYgXqg69I4Mq6kLzcFUKXUH24/1vsVPJwiCIAjCG+GsZiG9nZi+AK/MgLZCnfoqjU/2m1hDIjBlPdvVl9LxMOFS2Pnb00ZgMuMElIDp9RXioGHkRLGTvQBEA0rUlPbaALSEVCRmlNbOzmaJwAiCIAjC20HWRWB0M4CpwfaxOuEcDR0De0gVUkn3NrWw5nwYOVd9b90BdjJ9jtQ8pOYeZcQdGfBjamDpBj0Uo4ciJKJeLxhNCZa8TiV2mg3lIK7ROth7vJ+4ZZ/rRxYEQRAE4QSyTsAY/gD+Ic3pNNenIjBeQVBR11b1peY8KBkLgUKwYtCemYhdmR/Eb+hYjsvxvhimrjHKSyO1OVVouoNlteK6DgNx1QvG366iLS1eL5hpvhaStsuBVjHyCoIgCMJfm6wTMKZvuIDRSaWQXEwsCrp2qB9qFoKuw8jZ6u8haSRd1xhVEgKG+mCUkbfFrgHAyInhOmEigw6OZkBHF4btEnaT9Okac3yNAOxo7j2HTysIgiAIwqnIOgFj+PzDIzD4vEZ2MFlrxLCjECyEsolqQfU89e8JRt6TSqk9H0yrPQoAf14SXe/HdSFeUAWOw7h4kTqVaVJjNeAnKZVIgiAIgvA2kHUCxjwhhaRrJpajIjDz9IPq4KjzVPQFoNrzwTRvHXae05VSt3m9YPx5Sfwh1eguWa3E0OSY6gXTnFOE4dpM1JrYIQJGEARBEP7qZJ2A8fmC+IfMZ9QIpKuQ5ute+XTNwsyClJG3fQ8kIunDtaXKnHviUMcOXfWCCea7GIYqpU6UqREDoyOe+bdYdeydrjdwoC1MLClGXkEQBEH4a5J1Asb0mfiGRWB8WIDmwgztiDpYsyCzoWAk5FWCa6tqJI90BMYbJ5BKIXUbJQAECwCnF4BYfiUAI8KqAqk5twiA+f5GLMdl7/H+t/IRBUEQBEH4X8g6AeMzdHxuphOvhh/bBRyXAs0bsJhXldmgaZkozBAjb6qUuvGEFNKgmUuMIL78JMlYDwBRvxI16V4wpmqfkzLySj8YQRAEQfjrkpUCxhySQjL0TAopiNfrxRccvinlgxli5K0pVgKmP2bRG0lQYBqU+FSEpZ1KjJwosYFOXNcl4qq16V4wTgyA0cl6TCzxwQiCIAjCX5ksFDAaxtAIjBbwqpAcAiTUQfMEAXOKCEzIb1CRr6IuqUqkWq+Uup1KdH8Cl0FwowzElLDxtamITHO0A9efj+kmGKe1SCWSIAiCIPyVyUIBo6cFjOa6aIYfywWfa2NoXmjmRAGTisB0H4Zob/rwyZVIygfTYqlS6kBRAtfpJR5zsYwgdHZjWi5RK0rviGkATNMaONgeJpKwzsXjCoIgCIJwCrJOwJi6hukoAeN3XdD92EDAiQ9ZdIKAySmB4jr1vSVTTn26oY7HvV4wweI4pk912o0VjQTXZVJCzUZqLhsLwHnBJhwX9rSIkVcQBEEQ/lpknYDRNA3Du22/C66hTLxBNzPrCDNw8sZ0Gmlz+lB6qONpesHkljiYPlVKnRwxAYAJsUJ1moIKAOb6lZF3u6SRBEEQBOGvRtYJGADdVbftw8XV/VgOhDTlf3HNoKo8OpGRc9S/Q0qpR58YgfFSSJ16GQA5xTaariIr8VI1YiDdC8avojx1ycNoOOw81vtWPZ4gCIIgCP8LWSlgUhGYgOviGj4Stp428Lonpo9SVExR/3YcSB+qPWmcgIrA9JpFOOj4i6I4CWXcjeWqXjCjBpTIOZjsAzOI345Qp7WxQ0qpBUEQBOGvRnYKGFdVBfldF4wAlqtlSqhPlT4CKFMpILoPg60Mt7UlqhtvS1+UhOUwMuDD1MDWDXooRs8bJBHvACDqKwKgsledZm/3PqicDsB07Qj1HYOEY0PSWIIgCIIgnDOyVMB4KSTXBcOH5WjpCIxmhk69qbAWzBDYCeg9CkBZnp9cv4HrqiiMoWnUBDOVSJoGeqAL100waKvz5nl+mSP9R4hUKQFzfugYALuaxcgrCIIgCH8NslLA6AyJwJh+JWC00zSxS2/SoWy8+t6hZiZpmsaYchWFqe9Q1UZ1XhrpWLIOgEBRHNfuYzCq4QIcb6c8VI7jOhwoUmmlef5UR97et+4hBUEQBEE4LVkpYIy0gAHNCGC5+pAIzGkEDEDZJPVv5/70obFleQAc7lBjCGpPKqVOgNuLbUPCX4DV0cH0AjWdeo/PB0Bd8hDgSkdeQRAEQfgrcVYC5q677qKuro5gMMjChQvZtGnT667/3ve+x6RJkwiFQtTU1PDlL3+ZWCx2VjcMoKNmEakIjA/L1Yd4YF5PwCjhQefB9KFx5UrAnBiBSZdSF9v4AkrcxIuUqJnjqH/3JHtA9xG0+qmmU2YiCYIgCMJfiTMWMA899BCrV6/m1ltvZcuWLcyaNYuVK1fS3t5+yvW//vWv+dd//VduvfVW9u7dy7333stDDz3EV7/61bO+6bpkLiW2zdJoFM0MYtsawdQYgdOlkADKPQHTMSQCk0ohdSqRMjZHCZgOsxyAYEkCw1DelkTVOAAmxIoA2NuzP13dNF1v4GhXhL6IGHkFQRAE4VxzxgLmzjvv5Prrr2fVqlVMnTqVu+++m5ycHO67775Trn/55ZdZsmQJH//4x6mrq+Oyyy7jYx/72P8atXk9ypwc1jU2s6ovDL4gtmtkPDDGaaqQYEgK6QC4auxASsAc9iIwY70ITLe/CBcw86M4TjcA8RIVeanxSqkP9x4m5hl5F+cqI69EYQRBEATh3HNGAiaRSLB582ZWrFiROYGus2LFCjZs2HDKPYsXL2bz5s1pwVJfX88TTzzBFVdccdY3bes+Uq3qXF8AyzXw4c0iMv2n31g6DjQd4v0QbgUyHpjeSJLuwQSjQ350IGn46LbL0AwH3XccgFiO6r4bbA9TEizBdm0OFo8EMkbeHWLkFQRBEIRzzhkJmM7OTmzbprKyctjxyspKWltbT7nn4x//ON/4xjdYunQpPp+PcePGsWzZstdNIcXjcfr7+4d9hqGZme++APZQAaP7Tv8AZiAzE6lTNbQL+Q2qi1SJ9OGOAfy6ni6lPhpXKSMt0I7r2kSMAgCslmamlKjU0d6AitiMTR4GkMnUgiAIgvBX4JxXIa1bt47bbruNH//4x2zZsoVHH32Uxx9/nG9+85un3XP77bdTWFiY/tTU1Az73dSc9Hf9RAFjnByBCYf30NW9Xv0xNI3kMfaEUuqUD6bJqgMgUBzHdfoZtNTxRHMzU0unArDH6gNNJyfRSTk9UokkCIIgCH8FzkjAlJWVYRgGbW1tw463tbVRVVV1yj1f//rX+eQnP8l1113HjBkzuPrqq7ntttu4/fbbcRznlHtuvvlm+vr60p+mpqZhvxuam/6u+Xw4jo6J7f1oDlubTPay6dUr2bbtUwwOHj6lkTdTiaSMvGM8H8xx1yulLkrgOr1EohqOZpJsbmFKqYrA7Ok5mK5ummE00NwbpWtgyGRsQRAEQRDecs5IwPj9fubNm8eaNWvSxxzHYc2aNSxatOiUeyKRCLo+/DKGofq4uK57qi0EAgEKCgqGfYbdR0qsAD4DLwKTEjDDIzBt7U+kv3d2rT11L5gTjLyTclUlU7Oh/C05xRaaptJYsWAxdmcnUwK1ABzsPUiiagYAF+Q2A8hcJEEQBEE4x5xxCmn16tXcc889PPDAA+zdu5cbbriBwcFBVq1aBcC1117LzTffnF5/5ZVX8pOf/IQHH3yQI0eO8Mwzz/D1r3+dK6+8Mi1kzhRTGypgXGxXx6ed2gOTmmUE0NX1/P/SC0ZFYKblKU9MW0CZdv1FMXz+MADJaiWAihp6KPAXYDkWh7zqpHRHXkkjCYIgCMI5xfzflwznmmuuoaOjg1tuuYXW1lZmz57Nk08+mTb2NjY2Dou4fO1rX0PTNL72ta/R3NxMeXk5V155Jf/xH/9x1jdtkEk9mbqD4xpDUkjDBUzSyoiJ3t7XsCaOUg8dPg6xPggWpiMwjd0RkrbDlNwgGjDoz6HXLqLI14svpw17AJK1U+DQeuK79zC1ZiqvHH+FPcEgU4GxljLyig9GEARBEM4tZyxgAG688UZuvPHGU/62bt264RcwTW699VZuvfXWs7nUKRlq4vXpLrZr4E+beDMCJpwIk0z2pv923SQ9kd2U51XBQKuKwoyaT1VBkBy/QSRhc7QrwviKPOpCfo5EExxMTGFBcANGqBkGIFE2GoDY7l1MmT2FV46/wl5bRW7yYscpIszO5tfpRSMIgiAIwpsmK2chmUM8MIZuYzta5piXQtp4fCNLfrOEw9071WFdiYqu7udPMvJqmnZSJdJUL410xJqgrhPqxHVdosFSAKK7d2cqkXoPQclYAGboDbT1x2nrP/tRCYIgCIIgvD5ZKWCMoSbedAppeBn1X478BReXcFT1p6msvApQPhi3TImSYaXUXkO71EiBlA+mUasDwF8YBXeAQVsJoeTRRqb4lJH3QM8BklUzAbioQDW9Ex+MIAiCIJw7slLAHCleCkC3m4ehuziuhl8bXka9pX0LALobBaCifCWa5icWayZSouYcnaoXzOF2FYFJCZhmU1UihYriuHYf4Z4kvupqAEoae8nz5ZFwEtSXKjGT6cgrAkYQBEEQzhVZKWD2jLiazyZuYmX8v/CbJ0RgdB/dsW6O9B0BIEdXpdqBQBXFRecB0BVUFUWn7AXjRWBSKaROfwkWJsHiJI7TQzxiYUybDUBiz95MP5igWj/OOgTAzmO95+DJBUEQBEGALBUwhunjKec8Oigmz6/huNqwTrxb27Z6K11C3hP6fIWUll4IQJddrw72HAFLNZ070QMzKuCj0DRwdINjbjVG0CKQq0qyrbGq70t0167MSAFXeV4KIo3kEWFnc99p+9wIgiAIgvDmyEoBM1QX5AbAdvQhjezMdPqo1J+H6U19NM1CSksvAqAnvA07mA+uA91KzIwpUwKmxxvqqGkaU7yGdoeTkwEIFit/S7JSGXZju/dkjLz99VCg+sHMMBrpHEhwvE+MvIIgCIJwLshKARNJWunvIRNcd+goAT9b2pSA+ciE9wFgueBqfnJyxhEMVqty6holNlJppBy/mR7qmIrCpHwwR5zxAPjzlCE4mqsa3CUbG5nsU+fZ370f2zPyLvOMvNIPRhAEQRDODVkpYAbj1pC/bBxXT6eQIq7L3u69AKysvQCAqAOHeg+haVo6CtNVoqIrpzTyniBgmjTV+8XM7QJgYBB8o5RwqWgaJMfMIWbHOFJWB8CCgNeRt7n3rXpkQRAEQRCGkJUCJhLPlFG7rqU8MF4V0vbYcWzXZkTuCEp8quQ54mjs7FT9YEpLPB9MoA8XXneoY8rI22KOwAX8BRFcJ0Z/Z5Tg9OkAxHfvZnKJSjHtzVH7pSOvIAiCIJxbslPAJIYLGHdIBGbLoBqoOLdyLsmkEhARR2NHxw4AiosXo2kmUfqJB/TTRGCUgJmUG0QHImYOvRQTKIrjOr30d8YITlPel2E+GJQhuHCwniBxMfIKgiAIwjkiKwXM5TOqABhXnovr2jhDOvFuGTgKwNyKuVhWSsCQjsCYZi6hUB0AgzmGGifgqNEE6WZ2XgopZOiMDanGeEepw59noRnthLtiBKZOAyC2a1emlDp8FHIr0FyH6eYxeiNJmrqj5/RdCIIgCMLfIlkpYK6aNZKHP7eIRz+/xEshqSqkJLBjQPlP5lXOS89BijgaR/qOEE6o/i85OXXqeI4frCj0NQEwrmL4UEeA6fk5ANR7IwWChU3YlkOsTFUiJY8dY7Kh/DD7uvfheEbeSwo9I6/4YARBEAThLScrBYymaSyoK6Ew5MN1bVwXfFjsCfiJOUmKAkWMLRxL0uoHQDfycXHZ1bkLgNwcJT4ixcXqhF4aKTXU0XJcGrsjQMbI2+CMU3vLVCVSZ5eDr1Z13606NkjQCBKxIhwtV+deEFCiSEYKCIIgCMJbT1YKmKE4bhLH1TGx2BVQ6Z7Z5bPRNA3Li8AUhkYAmTRSTs4YACJ5wyuRNE1L94NJjRRIGXmbdCVW/PmqmV1bQzjtg0ns2cfEEjUgcm9uPiBGXkEQBEE4l2S9gFERGFWFdMSnJlGPK1LRkqTngSnPU2XQOztSAsaLwPi9cuzXGSmQisB0mGUk8GPmdQPQdqSP0DTPB7N7N1NLPCOvlgSgeOAgPix2HOslYTlv9WMLgiAIwt802S9gHEsJGCwaPAFTV1gHgOVVIVUXKP/Kjs4duK6b9sDEGMTWOGUlUsrIW+k3KQRcTecYNfjyBnDdJF3Ng5iTM0beVCXS3sFmCBaiOUnOy2tjMGGz8UjXuXwFgiAIgvA3R/YLGNfCdcDEpslUk6hHF6iISyoCU1s8FVM36Y51c3zwOD5fKaZZALhEQ8awCMxYLwKTKqXWNI0pXiVSI3X485OEcvtxHZeB1HWam5liKiPv3q59uCOUkfeDVZ0APLOn7Vy+AkEQBEH4m+PdIWC8CExEV4OP8n3Kh5LqAxMKlDKpeBKgojCapmV8MCEDot0wqMTGuBMiMAAzSwoAOOKMRdOhoFr1munssPGNVt6YEcci+HU/4WSYY+Uq4rMwqIy8z+5pk34wgiAIgvAWkvUCxkkLGJuEpgSM31ARk1QfGJ9ZxIwyNUE67YMJeQKmtEydqE1VKJ041BFgeoEqpW5wlXcmVHRMbWnoT/tgknv2M7FYGXn35BUBMDJ6kKBPp6Uvxp7j/W/1owuCIAjC3yzZL2AcC7wIzFAB47o2lldGbfoKmVHuCZgTK5GKCtWJWr3jfpORhao66cShjsf0GlzACGYETHCaGikQ270709BOV0319LZdXDi+BIBn97Sfg6cXBEEQhL9Nsl7AWI4FjoOhOViegPHpPiwrnF7jMwvSEZg9XXtIOslMM7ugoRa17kqvH1cxfCbShJwAhusS00N0Uo7rU56W/o4ojPNGCgzpyLs3chz8eWBF+WCN6sT77F7xwQiCIAjCW0X2CxjXwedaJIcc8xv+dBdew8hB1/2MLhhNvj+fuB3nYM/BTARG97wuXgQGYKyXRjrYrkSQX9ep8cYNHKUOzd9FUaVKK/XnKvNusqWFqV5H3j3de3GrVGRmSW4TmgY7m/s43idjBQRBEAThrSD7BYxj43OS6fQRKAGTTh+ZKkWka/owH0wo5FUQOQNYhgad+8FSwxinjVR7djZnmtBNCarJ1o3UYeZGKK9Vf3e2J/GPVueqPhbD1E364n0cr1LemPzm9cytVR1/n90raSRBEARBeCvIfgHjOvhce5iA8em+dATG5ytMH08LmM6dmGYefn854PlgHCtdTj2rpkitO9aH7ajqoTnl6jxH3THohkuwUHXaVT4YJVasvQeYUKQqkPZWKMMvB5/m0snKKPyslFMLgiAIwltC1guY5AkRGFMz0TU93QMmFYEBmFaqhMae7j3AECNvRY1a4KWRxlfkkeM3GEzYHPaMvLPLiwA4itpjozwz7Q39BNJG3owPZrdmQ7AIot28r0SVU2843MVA3HprX4AgCIIg/A2S9QLGch1M1yaZMvAaqhtvqgvv0AhMqltufW89MStGTqgOGDLU0SulNnSNGdVq37amXrU3NVJAryBKkHhiH7qhEQ0nsUYr0RIdMlJgb88+mHApANVtz1NXmkPCdnjxQMdb/g4EQRAE4W+NrBcwtheBSXoZpFQPmOSQHjApKnIqKAmWYLs2B3sOEkpVIuWkKpEyRt7ZXhppuydgyvwmhUllFW5iNAm3nrJRqlqpL6iGRVotx5miVwOq2smdsBIA7cCTXDq1EoBnpBpJEARBEN40WS9gLNfB7w7pAaN7Tey8CIzpK0iv1TQtU+rcvTddSh3Vveqg1p3gdcxN+WC2H+tN76+zVH+XRuoIFPdSXKXWdrYm8Nepc41qiWNoBt2xbtqrZ4NuQud+3jsqBsDafe1Ytgx3FARBEIQ3Q/YLGMfFdK2TuvCeKgIDZKZGd+3JpJCSbbi6CbFe6FdjAlICZt/xMLGkEi5T/OrcR90xmEEbI6hSTm0N/QSnKx+Ms+8gY4uUgXfP4DGoXQTAjMENFOX46I0k2Xy05618BYIgCILwN0f2CxjXHhaB8enKA5OqQjKHeGCATLfcrj1eKbWGZYdJVo5XC7w00sjCIGV5ASzHZXeLKsmeXarOdcQZp67BawB0NIbxT1UG4eiuXUwpyUR5mHQ5AMbBJ7l4UgUgTe0EQRAE4c3yLhAwLqZrk+AEE6/XB8ZnDhcwKSPvwd6DOBgEA8q/EqlSvVxSHXk1TWN2jdqb8sEsHK18LM36KBw0ku4+fEEDK+EQr54MQGz3nvQ19nZlBAxHX+Y9E1Tzu2dkuKMgCIIgvCneBQLG68SbMvF6HphMH5iiYetH5o6kwF+A5Vgc6j2UMfIWq5lFtO5Ir501Su1N+WAmFOViOg4J3U87Vei5xymvUV17e81K0DSs48eZOsTIS8lYKJsEjsWF2jb8hk5DV4TD3pgCQRAEQRDOnLMSMHfddRd1dXUEg0EWLlzIpk2bTrt22bJlaJp20ue9733vWd/0UGxPwJw8iVpFYHp+eC9uIpFef7KRV/V1ieaoyE2qlBqGGHm9CIypa4xKqEqko24d/vwEeaWqx0vH8XjayFvbnERDoz3aTme0Eya9B4Bg/dMsGlcKqCiMIAiCIAhnxxkLmIceeojVq1dz6623smXLFmbNmsXKlStpbz91m/xHH32U48ePpz+7du3CMAw+/OEPv+mbh5SJN5nuA3NiBCby9MsMvvrqsD2nNPIaqkqI7iMQVzOQZo5SKaSGrgi9ESWCUkbew0mVMiKwETjZyDumcEz6GkzyxNrBp7l0sjedWnwwgiAIgnDWnLGAufPOO7n++utZtWoVU6dO5e677yYnJ4f77rvvlOtLSkqoqqpKf5555hlycnLeOgHjuvjJjBLwGT5sO47jKEGiRyDy2mvD9qQjMF2ZCEwkeRzyqgAX2lSn3qIcP2O8wY7bj6mqpvkj1ViAes/Ia+sqYtPdMog5WRl5Y7v3ML1MiZnNbZth1HzIKYNYH5cXHAFgS2MPnQPxt+QdCIIgCMLfGmckYBKJBJs3b2bFihWZE+g6K1asYMOGDW/oHPfeey8f/ehHyc3NPe2aeDxOf3//sM/psFx3WArJp/uwvBJqHNBiEH11uIBJmWz39+zHH1QTpCORhvQEadoyDe1mjRpu5J1Tpbr2NhvK5+IGjpJb6Md1XCKVkwCI7d7NBdUXAPBc03OgGzBRpZFKj61lenUBrqt6wgiCIAiCcOackYDp7OzEtm0qKyuHHa+srKS1tfV/3b9p0yZ27drFdddd97rrbr/9dgoLC9Ofmpqa0661XdUHZmgn3lQPGC0CmqsR3bEDZ4gPpia/hlxfLnE7TmvCQtMMHCdKvEpFVYZ25D3RBzM1LwhAv7+AQXLxF0QoqVEpp169TBl5W1tZFJyCqZsc6TvCkb4jmWqkfY9z6WT1/mS4oyAIgiCcHX/VKqR7772XGTNmcN55573uuptvvpm+vr70p6mp6bRrT4zA+HU/sbYGAPSIhl5YiJtIENuZESW6pjO5RHlY9nYfIOhFYaLFymCbKqWG4R15XdelyGdS4ahOugejKmITKFYem/aWGP6xqomdcaCBhVULAS8KM245GAHoPcp7R/QC8OLBznSTPEEQBEEQ3jhnJGDKysowDIO2tuGRg7a2Nqqqql537+DgIA8++CCf+cxn/tfrBAIBCgoKhn1Oh32igDH8DO5VgsJ0Q+Sefz4AkRPSSEObzaVGCkTyAurH9j3gKGExdUQBpq7ROZCguVeNHJiWq/q57E+oVBR+VXrd3tBPcJo6Ft21i+U1ywFY27gW/LkwdhkA47pfYGRhkGjS5qVDnf/r+xAEQRAEYThnJGD8fj/z5s1jzZo16WOO47BmzRoWLVr0unsffvhh4vE4n/jEJ87uTk9D0kX1gSHjgRk8rKItvmApOfPnAycbeYc2mwulKpH0CJghSEagux6AoM9gyggloLY3qdTUgsoiAA5oyvPi+tXa/s4Y2sQZgDLyLqtZBsCOjh2qnHryFQBo+//CCm+4o1QjCYIgCMKZc8YppNWrV3PPPffwwAMPsHfvXm644QYGBwdZtWoVANdeey0333zzSfvuvfdePvCBD1BaWvrm73oItuviG1KF5Nd9RJsPABAoriZngRIw0S1bcC0rvS8tYLqHCJjoUahQkZnhPhjPyOs1tLuoJB+AIzm1OOiYeX0UV6tzh0snABDbtYvK3EpmlM3AxWVd07q0kZfm17i8Tr36Z/e24zjSlVcQBEEQzoQzFjDXXHMN3/nOd7jllluYPXs227Zt48knn0wbexsbGzl+/PiwPfv372f9+vVvKH10plgOmGRMvEUdUSxbVS0Fy+sITJiAXlCAE4kQ27svva+uoI6gESRqRelHGXMjkQaoUhGUYQLG68i7zTPyzsrPIddxifsC7I/OQNOgsHo7AD1OsTLytrdjdXSk00jPNT0H+VVQPQ+ABYlN5AVMOsJxdjT3veXvRRAEQRDezZyViffGG2/k6NGjxONxNm7cyMKFC9O/rVu3jvvvv3/Y+kmTJuG6LpdeeumbutlTkXC0YX1gyncfx1UWFXzBEjTDIGfuXAAimzNpJEM3mFSiUkCNEdUzJhptxK1UvVyGduSd7Rl5dx7rw7IdTF1jYVD5ZTYnlSHZzFMCpqMlin+cMvJGd+/m4tqLAXil5RUGk4PpaiTz4F+4aFI5INVIgiAIgnCmZP8sJEfDJGPiLd3dgpMSMN4cpFQa6aSGdp6Rd09fK5rmx3UTxMrUcMehlUhjy/PIC5hEkzaHOgYAuGSUami31/SMvMGDgNeR15tMHdu1m7GFY6nNryXhJHip+SWYpHww1K/jPRNUKkrGCgiCIAjCmZH1AiZpa5iaisBojkvR7iacHOUpMb1J1DnzVNom+tpmXK8EGob4YHr2EwrVAhDJ9SqRwi0w2AWAoWvMqB7e0G5ZuTL2NoaqiePHyOnECMaJD1pY42cBqqGdpmnpKMxzTc9BxVQoqgUrxnL/HgxdY39bmMauyDl5P4IgCILwbiTrBUzC1fBjkdQ06trAHIjh5qvH8vmU6AhOnYoWCmH39pI4fDi9d2glUrqU2mqDYvV9WEdeL420zatEGhsKUGbZOLrBrvhcNB3Kx6lzh4u8FNLOnbiOk/bBPH/seZKulY7C5DU8zYI61dlXqpEEQRAE4Y2T9QJGpZBsksDYVhV5cYtVFCUVgdH8fkKzVVRkaBppbNFYfLqPcDKMbajqqEik/pRG3tk1wyMwmqaxJKDMv1ss5YPJKdsGQI9diJ6Tg93ZSWzXLmaVz6IkWEI4EWZL25ZMGmn/k1w2xfPBiIARBEEQhDfMu0LA+DwPTMibFuAEVBM605dpgJfuBzOkoZ1P9zGxeCIAnY4SI+HwHqhMCZiTO/LubwsTTajzX1qrxMdeU3lp9Nz9ALQ3RchbdpE639NPY+gGF41Sf69tXAujF0OgECKdXF7cDMDGI930RZJv9nUIgiAIwt8EWS1gXNfFcvR0H5igJ2BcQ/lcDD2YXpszfwEAkc2bcd1M35XUZOpDUSUeBgb2nrISqaogSEV+ANtx2d2i0kgXjVTpn7ZgBX0UYuQcRzPidDSFyV1xGQD9Tz+D67rDfDCubsIEVZE1ovU5JlbmYTsu6w7IcEdBEARBeCNkuYCxsVwDMyVgkkqYOLqKkOh6IL02NGsm+HxYbW0kjx1LH59equYZvdrdiK6HsO0IkSKVLqJjHyRVibWmaUN8ML0AlPt9jI4p1bQjOQ9Nd8mrasBOOsTHzUULBEg2NhLfv5/zR5xPyAxxfPA4+7r3DRnu+AQrpqgeOlKNJAiCIAhvjKwXMLZr4tOUiTeYAFd3QfciMEYmAqMHg4SmK7EyNI00p2IOADs7d5OXpwY8humC3ApwLGjOrJ2dHuyYaTy31PPBbLVUiqq4VkVtOtqS5F6wVJ3v6acJmkEWj1wMwNqmtTB+BegmdO7nvdWqAun5/R0krEyVlCAIgiAIpybLBUwSyzXxeZ14A0lwfZnfh0ZggFPORRpTOIaiQBFxO07Cp3rA9A/shjEXqAVHXkivTXXkTRl5Ad4zWg2x3Oubggv4C/cCarBjwWWpNNLTAJmuvI3PQagIRi8BYEr/S5TlBQjHLTYd6T7LtyEIgiAIfztkuYCxsR1jmAfGNTO/nyRgTtHQTtM0ZpfPBqAlqTaHw7thzIVqwRABM9OrRGrsjtA9qFJHi0eXYjgO/b4CWhmBkXsMTU/S1tBP3rJl4POROHSYeH09F426CF3T2d+zn2PhYzD5veo+DzzJiikVgFQjCYIgCMIbIcsFTNLzwFgZE69f/aZpPjTNGLY+NGcO6DrJxkaSbRnD7OyK2QDs7FepoXB4N64XHeHYa5AYBKAg6GNceS6QGeyYaxpMisTVMWsemmETLD5K9/FBbH8OuYvOV+d8+mmKgkXMq1RN9YYNd2zcwOVj1Y0/s6dtmMlYEARBEISTyXIBo0y8viEm3lQE5sToC4CRn09wsvK5RDef7IN5oeMQuh7AtgeIhkwoGAVOEhpfSa9NG3kbe9PHlvpDAGx3lDgpHLUXXOhoDJ82jbS2aS0Uj4bK6eDaLHK3EvTpNPdG2XO8/82+GkEQBEF4V5PVAsZxLBzXUB4YNOWB8avoxakEDJw6jTStbBo+3UdXrAd/SHXR7R/Ydco0UsbI25s+dsVo5Z3Zb07ERienQvlg2hr6ybv4YtB14nv2kmhqSguYLW1b6I31pquR/If+wrKJKo304Kams30lgiAIgvA3QVYLGNdNYjmGNwuJYR4YwwiSiFlseOwwPa2D6T0hby7S0EqkgBFgWqnq/RLWVG8X5YPxjLwNL6bXDjXyplI988eVEUomiRtB6hmHr6ABNJv2hn7MkhJyFqgeNOGnn2FU/igmFk/Edm1eaH4hU059aA2fOk8Zgh/ZfIzeSOKte1GCIAiC8C4jywWMjePq6VlIwSS4ngdG1wNsX9PEliePsv63B9N7UpVI8YMHsXp60sdTaaQjMVXGHA7vgjpPwLRshZjyx0wekY/f0OmJJGnqjgJg+gymDah+MTvsuWhGkmBxI631/biuS/5lqmld2EsjpZvaNT4HI+ZAXhUkBjhf38uUEQVEkza/euXoW/6+BEEQBOHdQpYLGJVC0rBwNA2/xRAPTJDG3WqadPPBXqykN16gpAT/uHEARLdsSZ8rZeTd3NsJeEbewlFQMhZcB46+DEDANJgyUo0o2DYkjXSBT/lgdjhzAcitPMBgb5yOxjD5K5SAiW7fTrK1NZ1GeqnlJWJOIh2F0Q78hc9eqFJY973UkB5ZIAiCIAjCcLJewNiugaupqInPykRgNHy0HVFmWDvp0Ho403zuVHORUgLmtZ5mNM2HZfUTizVlojBHMmmkOZ4PZmN9V/rY+2qr1TLfGGIEKa7dB8DBV9vwVVaoCigg/MyzTCmZQlVuFVEryivHXxky3PEvvG9GFTUlIboHEzz0auObfUWCIAiC8K4kqwWM41rYjg6ahea4+GzAVL6URNRkaDVy095MuigtYDZvTh8rCZZQV1CHjYbrHwlA/2n6wVw0UQ1xXLuvPe2DmTy+lKJoFFsz2MdUjLxDoDkc2tyO67jke9VI4aefRtO0TFO7pufUNXw50N+M2bGLf7xQRYjuefEISVs68wqCIAjCiWS1gFERGCVgfF62JdWJNz6oAZBXrKqRmvZmOtzmzFdG3tiePdgDGYNvygfT5eYBng8mJWDadkJEnWPRuFJCPoPjfbF0ybOR62dGv+eDcWajGTFyy48z0BOntb6P/EtVGimyeTNWV1faB7OuaR224YNx6m/2PcGH542iLC9Ac2+UP2xreUvelSAIgiC8m8huAeOVUbu6gz/pHfMETMTLGJ13pfKUdDSFiQ2oRb4RI/BVV4NtE922LX2+lIA5MKiESLh/F+RVQLnqHZOqRgr6DJaMLwNgzd5MQ7wLvdlLO111nlEzmwE4+Fo7/lHVBKdNA8ch/Owa5lXOI9+fT3esmx2dO4akkZ4g6DP4zNIxAPxk3SEcRxrbCYIgCMJQslvAuBaOq+NqNn7LOxZUj5SMGRg+nQnzKygZmQsuHNt/ijTSa6+mj6V8MK/2KlHSH96lUkSnSCOlWv+v2ZcRMO8bPQpclxZzJD0UESrbA8ChLe04J6SRfLqPC0ep865tXAsTVwIatO6AvmN84vxa8oMmhzsGeXpP61vxugRBEAThXUPWCxjb0XFx8HkChqAaH+DaPqonFGH6DWomlwAnpJFO0dCurqCO4kAxR2MWaCaW1Uss1jxEwGSMvBdPVgJme1Mv7V7qqGZ8OSMGBgDYzUwSzg4COQbR/gQtB3rS5dSDGzdi9/VxcY1KG61tXIubUwq1auwAW/8f+UEfn1pUB8CP1x2W8QKCIAiCMIQsFzD2SREYQqqO2rV91ExVwmXUFNWcrmlvd1oIpCIwse07cOJqlpGmacyumI2NRsJQKaJweJc3NVqDzv0QVtGQioJgeqzAs14aySgOMKNX9YbZ6c7CIczYBUrcHNzcTmDMGAITJoBlEV77HEuql+DTfTSGG6nvq4fzrlfPsPEnEB9g1ZI6gj6dHcf6eOlQpuJJEARBEP7WyWoB47hJHFfH1ty0gHECKgLj2D5qp5UCMHJCEbqhEe6K0dehBIZv9GiM8jLcZJLYjh3pc6Z8MC2WqsfuD++EnBKomqEWDInCXDa1EiCd4tE0jQs1zwfDbFygfKIqhT68pR3bdoalkXJ9uZw/QkVd1jauhakfgNLxEO2BzT+nNC/ARxfUAvDjdYfempcmCIIgCO8CslrAuI6F4wB6JgITy1VN5nz+EMVVOQD4gyZVYwsBOLZP+WA0TTtlOXVKwOwKq+qicP8u9UMqjdSQ8cGkBMzLh7oYiKsbuHxUNYZt06cX0cwobGMroXwf8UGLY3t70gJm8KWXsAcGWF47pJxaN2Dpl9XJX/4hJGNcf+FYTF3j5cNdbGvqfdPvTBAEQRDeDWS3gHFtNBcSmobfUqmhWFCVQBeWFqJpWnptzZA0UoqceSc3tJtaOhW/7mf/oIrUvJ6Rd3xFHmPKcknYDs/v7wCgfGIltX29AOxiJt1dGxg3V0WCDr3WRmDiBPx1dbiJBAPrnmd5zXI0NHZ27qQ90g4zPqKmYA+0wbZfUV0U4v2zVZO8Hz8nURhBEARBgKwXMEl0xyWpaWkTb8Kv+r4UVRYNWztqivLDNO/vSZclp428W7fiWuoEfsPP9LLptCR1XIyMkbd2EWgG9DRAr0oLaZrGpV4U5hkvjeSrymVmj/K97HRm42r9jJiu1tdv68C2HPLfsxKA8FNPURYqY0a5Sk+ta1oHph+WfEnd9EvfBzvJDcvGomnw9J42DraF36K3JwiCIAjZS5YLGBvDsb0IjDrmKAsM+SUFw9ZW1ObjD5nEIxbtR1V6KDBhAnpBAW4kQmzv3vTalJF3QFNpp3B4FwQLoFrNORrqg0kJmLX72knaDpqhcaGrRNRebToWBoOJP5BbFCARs2nc3U3BSiVgBl54AWdwcFg1EgBzPwm55Uoo7XyE8RX5rJyqJlX/ZN3ht+LVCYIgCEJWk9UCxnEtDNchqYHfAlv3gamUTDCUO2ytbuiMmqTSSMe8sQKarpMzT3XlHZpGSvlgGuIqUtMf3ql+SM9FyqSR5tYWU5rrpz9msemISk8tr64mmIwT1wMcZgLdvc8xbr66n0OvtRGYPBlfbS1uPM7ACy+kfTAbWzcykBgAXwgWfUFdYP2d4Dh8frkaL/CH7S00dUfe5JsTBEEQhOwmqwWM61oYjkMClUJK+nLRdK/bbiB00vpT+mDmn9wPZnb5bAD2ej1dTjLyHnmB1KAlQ9e4xGtq98yeNgBKJlZR161E0tb4PNCSlE3Yrrbu6MRKOBSsVGbe/qeeZmzhWOoK6rAci/XN69U15n8GgoXQeQD2/YmZo4pYOr4M23G558X6s3xjgiAIgvDuILsFjGNhOk46hZT05aMbSsAYXlv/oaR8MK31fSRiKlKT8sFEN2/GddTgxKJgEWMKx9CUUK8nbeStWQiGH8It0J0REZd56Z2nd7fiui7+2gJm96Z8MOr8vQOPUlAWxEo4NOzsJH/lewAYeP55nGg0HYVJp5GCBXDeZ9X3F74Drsvnl6kozEOvNtERjr/Z1ycIgiAIWctZCZi77rqLuro6gsEgCxcuZNOmTa+7vre3ly984QuMGDGCQCDAxIkTeeKJJ87qhofiuha66wmYJCR8uWiegNH1wEnrC8tD5JcEcWyXloO9AASnTEELhbD7+ogfylT5zK2YS0tSx0HPGHn9OTBqgVpw5Pn02qUTygj5DFr6Yuxu6Uf3G1yZr7wxR0OjGHRCROK7GLtATZw89Fo7wWlT8VVX40ajDLzwIitqVwCwpnGNqkYCOP8G8OWq8QKHnmXRuFJm1RQRtxx+/tKRN/3+BEEQBCFbOWMB89BDD7F69WpuvfVWtmzZwqxZs1i5ciXt7e2nXJ9IJLj00ktpaGjgkUceYf/+/dxzzz1UV1e/6Zt3XQufY5H0yqiTvrwhAkZFYAa6u+hqbgJU1VAqjZT2wfh85MyZDZyQRvKMvD2OSkWFw6dII3kEfQYXTFCde1NppCWLp1AcGcTVdDb3qWZ1BbUvAXB0VxfJmE3+ykw10oyyGcytmEvCSXDfrvvUiXNKYP4q9f3FO9A0LR2F+eWGo/THkm/m9QmCIAhC1nLGAubOO+/k+uuvZ9WqVUydOpW7776bnJwc7rvvvlOuv+++++ju7uaxxx5jyZIl1NXVcdFFFzFr1qw3ffMnmngtM5ROIelGgGQizq+/9hV++f99kb52JSxSaaSmfUPnIqmoSmTDK+ljKSPvoWgCUGkkYIiR98W0DwbgsmleGskTMIG6Qub1qTTPel35Xbr7HqO4KohtORzZ3kGBV049sG4dbjzO52Z9DoBHDjxCR0T1lWHRjSpt1bgBGl7i0imVTKjIIxy3+NUrR9/U+xMEQRCEbOWMBEwikWDz5s2sWLEicwJdZ8WKFWzYsOGUe/74xz+yaNEivvCFL1BZWcn06dO57bbbsG37tNeJx+P09/cP+5wK17UwvTJqnwW2EUAzlOAw9CB7X1xHuKsDO5nk0KtKnNRMLgENulsGGfQERu6SJQAMbtiAm1QCqDa/lhG5IzjqWU3C/V4l0qj5YIYg0gntmdLriydXoGuw93g/Td0RNE3julIlavYWjKcrUYLldFC3QAmcg5vbCc6YgTliBE4kwuBLL3H+iPOZUzGHuB3PRGEKRsCcT6jvL34HXde4wYvC3Lf+CLHk6d+jIAiCILxbOSMB09nZiW3bVFZWDjteWVlJa2vrKffU19fzyCOPYNs2TzzxBF//+te54447+Na3vnXa69x+++0UFhamPzU1Nadc5zoWppvpA+PofjRdmXN13c/+lzNpnvqtrwIQzPNRXpMPwDGvGik4bRpGURHOwABRby6Spmksr1l+spHXDGSmRg9JI5Xk+llQp6I7z+5VImXpwnHU9vbh6DpPDX4AgEC58s407e4mHrEouCxVjfQUmqalozAPH3g4E4VZ8iXVRO/wWmjewpWzRlJdFKJzIMFvX2s67XsUBEEQhHcr57wKyXEcKioq+NnPfsa8efO45ppr+Ld/+zfuvvvu0+65+eab6evrS3+amk79P2nXtTDdTBWSbfjRvQhMPJKkaffO9Npje3YRj6j+KSkfTKMnYDTDSEdhBl7MNKlbXruclqSO7ZIx8gKM8dJIDZm1kGlq9/RuJWDMogBXD6g004u5F+Kg0zfwNKW1Jo7jUr+1I+2DGVj7HE4iwaIRi5hVPou4Hefnu3+uTlxcBzM+rL6/eAc+Q+ezF40F4KfP15O0ndO+S0EQBEF4N3JGAqasrAzDMGhraxt2vK2tjaqqqlPuGTFiBBMnTsQwjPSxKVOm0NraSiKROOWeQCBAQUHBsM+pcFwL0zPx+iywTRPNUCmVo9t24LoOFWPGUTxiJI5tcXTnVgBqp6rZRE17unG9sQK5FywFYPDF9enzz6ucR46/gJakmqmUMfJepP5teBGcTAonVU69qaGb3oh6ts9MnUgwmaA/kM+r0QW4xBk9fz8AB19rIzR7FmZlJc7AAIMvvYSmadww6wYAHt7/MJ3RTnXyC1YDGuz7M7Tv5SPzayjL89PcG+VP21tO+X4EQRAE4d3KGQkYv9/PvHnzWLNmTfqY4zisWbOGRYsWnXLPkiVLOHToEI6TiRIcOHCAESNG4Pf7z/K2Fa5rY7o2SbxOvL6MSDq4UaWMJp6/lLFzlUm3frM6VjW+EF/QIBpO0t6oZgvleRGY2O7dWF1dAPh0HxeOujCdRurt86ZWj5gN/nyI9akSZ4/a0hwmV+VjOy5r96mqrPKZlSxqVRVPzzjvB0DLewZQc5miA1Z6QnX4qacBWDxyMTPLZxKzY/x8lxeFKZ8EU65U39d/l6DPYNWSMYAaL5Ca7yQIgiAIfwuccQpp9erV3HPPPTzwwAPs3buXG264gcHBQVatUuW+1157LTfffHN6/Q033EB3dzdf+tKXOHDgAI8//ji33XYbX/jCF970zbtuEp9rpU28bsBM/9a0aw8Ak85fyti55wHKB+M6DoahU+NVIzXuVmLFLC8nMHUKAIMvvZQ+z8U1F7MvpoRRe/uTygdjmDB6sVpw+Llh95QZ7qiiVJrP4NN+lbLamzuRDsoZjG6hakIc14XDW9rTXXnDa9fiJhLDojC/3f/bE6IwwM5HoPsIn1w0mvyAycH2gbTvRhAEQRD+FjhjAXPNNdfwne98h1tuuYXZs2ezbds2nnzyybSxt7GxkePHj6fX19TU8NRTT/Hqq68yc+ZMvvjFL/KlL32Jf/3Xf33TN++6NiYpE6+L408dN3Adl4q6cRRVjaB68lT8oRyi/X0cP3QAgNHTVBrp6K6u9Pnylipvy8CQNNKS6iUcjAeJORCPt9Dfr9JQTFKddNn58LBy6pSAef5AR7pC6MLFkxnd0w2axtOD7wXNZcQsdZ6Dr7URmjMHo7wMp7+fwVdUtdSSkUuYUTaDmB3jgd0PqJOPnAPjV4Brw0vfoyDo4xOLRgNw17rDSlwJgiAIwt8AZ2XivfHGGzl69CjxeJyNGzeycOHC9G/r1q3j/vvvH7Z+0aJFvPLKK8RiMQ4fPsxXv/rVYZ6Ys8WxT6hC8lJIrq08KxPPV2khw/RRN1sNbazfotJItdNUBKatoZ/ogPKr5KV8MOvXp8cK5PpymT9iEbui6txtbY+ri0+7GowAtO8ZlkaaUV1IVUGQSMLm5cMqcuKvyed9HWq0wAuB5VgYWL4nAZfjh/sY7E9ScKlXjfTkUwDDKpIe2v8QXVFPaF3wz+rfbb+G/hY+vWQMAVNne1MvGw5nxJggCIIgvJvJ6llIlmvhc5NeJ15w/Eq42AkViZi4aGl67biUD2aLGnuQVxyktDoPXGXmBQjNno2em4vd00Ns95703uW1y9kaSaWR/oLrOhAqhkmXqwXbH0yv1TTt5DSSprFq7HhCiRgDvjxes84jaTczalYbuHBoczv5XlO7/r/8BatTCZ8Lqi9geul0olY0E4UZvRhqF4OdgJd/RHl+gGsWqDLzH687/Fa8VkEQBEF4x5PlAsbB59okvE68eALGtTXK68ZSXDUyvbZu9jzQNDqOHqG/U/VXGT1dRWFSaSTN5yN3sTIjD64fUk5ds5z9MZOoA/FEG7293siBWR9T/+58GOxMW//LpqUETHvaXDvi/DoWtqjrPm0pM27ZpI0AHHytnZwFCwjOmokbjdL505+p+9E0bpitvDAP7n8wE4W50IvCbP45DHZx/QVjMXSN9Yc62XGs9yzfpiAIgiBkD1ktYGzXyURgkuD41HHH0hk9Y/awtTkFhYycMBmAI1tTaSTlg2kcWk59Ch9MWaiMqeUz2RlNRWG8QZTjL4GcMhjsgEOZyqyFY0rJD5h0DsTZ2tQLgJHv5++tELgu+0OTaGUEcdaiGwnaG/oJd8Wo+PKXAeh98EGSzarnzCmjMOMuUZVQyQhs/Ak1JTm8f5YSaz9+TqIwgiAIwrufrBYwluNFYNDw2eB6Asa1NSrHjj9pfbqc2vPBVI0rxB80iA0kaT/qlVMvVb6Z6Pbt2ENGGFxcczFbI6rKqb3jL7iuDYYv02Bu+2/Sa/2mzrLJFUAmjQSw7LwZ1HWrKMwzyZW4RKidryZgH3ytjdzzzydn0fm4ySQdd/0YOE0URtMyXpiNP4NYH5/zxgs8taeVQ+0DZ/wuBUEQBCGbyHoBY6LKqP1JIBWBsXVKq08ePzB2niqnbty5nWQ8Nqyc+ugu5TvxVVfjHzcObJvBIcMdl9cuZ39MZ9DRSCQ66elR6R9me2mk/X+BaE96/WWprrx7MiMW8qaV857jSii9YFxMAh8Fo1XJ9qHNqm9MxU03AdD32GPE6+uB4VGY+3ffr042+X1QNgniffDq/zCxMp9Lp1biunDnM/ulIkkQBEF4V5PdAsZ18LsWtquhu+CaqnLIsTQCObknrS+rGU1BeQVWMkHjru0A1E73yql3Z6ZT5y1NVSNlfDBjC8dSWzCGHRH1ytravWqkqplQMRXsOOx+LL1+2aRyfIZGfccghztUREQzdT5WMZq8WISIkcurnE+CV/Hl9NHZNEBP6yChWbPIu+QScBw6fvBDtW9oFGafF4XR9UxfmA0/hkSEL10yAUPXeGJnKw9vPvam3q0gCIIgvJPJagGTdB1M18K1wdF96UnUrq3hC4VOWq9p2kldeVNjBdqP9hMNq/25F2R8MEMjGaoaSaWROjqewnGSKp0z66NqwZBqpPygj0XjyoDhaaQxSyczr0X1yXnaugJwqV2gyrAPvuZ17/3SF0HTCD/5JNHduwEVhUn1hUl3553+ISiqVZOxt/yC6dWFrL50IgC3/mE3h9rDZ/ZCBUEQBCFLyGoBY7vgwwJvkKNmqF4rjq3jDwZPuSfdlXfLJlzXJa84QOkoVU7d6JVT5yyYjxYMYrW2kjh0KL334pqLORTXGbA1kskeeno2qB9mfAQ0HZpega6MiTZVTv3U7kwayV+dx4f7DDTX4ZBvIk3UEKxcB7gceq0N13UJTpxIwZXvA6Dje98HGNad96H9D6nuvIYJS25SJ375B2AluOGicSwdX0Y0aXPjr7emm+kJgiAIwruJrBYwlmvjc21cR8PR/ei6EjDYOobpO+WemqkzMAMBBnq6aW9QHpMTu/LqgQA556lIzdBqpJnlMykOlrEt1dQulUYqGAFjl6vvOx5Kr79saiWGrrG1sZedx/rSxy+cM4MxnUrUrHEuw6aRnLImelojdDWrdFP5jTeCaTL44otEXlXRoqXVS5lZdsKMpNl/D3lV0N8MOx5E1zXuvGYWZXl+9rWG+dbjmX42giAIgvBuIcsFDJiuhWZpwyIwaJkhkVZvjK5f7iFe3wuA6fczesYcINPULtUPpmlPd7pvS2qswFAfjK7pLKtZlm5q19HxNI7jTdRO9YTZ/hvwuvhWFgS5cuYIAO5+IROZKV1Qw/JjyvD7oracGAGq56pBkVufbgTAX1tL0Yc/BED7d7+H67rDvDDpGUm+ICz+J3Xi9d8F26IiP8idH5kNwK9eaeQvOzOjHQRBEATh3UBWCxjbdTBJYtgatu5H0+MAaGSiL/1PNhDd3UXvn+vTx8bNS6WRVGSjcmwhgRyT2GCS44d6Acj1xgpEXn0NJxJJ77249mLq4zoDjoFl9dPd7UVoJr9XTajubVSpJI9UefNfdh6noXMQACPPzwdyqiiIDhDTQ7zCUnzFL6LpSQ682kZ3i1pX9rkb0AIBolu2MPjCC4CakZSaVH3frvvUReb9g+oM3F0Pex4D4MKJ5dzgXftffreDpu7MMwiCIAhCtpPVAibpOGiahd8C2wigGUrA6F4Exu6LE9mhyqOTLYMk25QwGDNnPgCthw4w0NONYeiMmV0OZMqZ/XV1+KqrcZNJBjdtSl9z4YiFBM0ctgyqrr/p2Uj+HJj2fvV926/T6ydXFbB8UjmOCz97MSOipi6dyawWVSn0jLMSxw0zdvFRcGHDYypa46usoPgTfw9A+/e+j+s4aJrG52d9HhgShQnkwfnqGC/ekY4Arb50InNriwjHLP7pN1tJ2s7Zv2xBEARBeAeR1QLGckDTkmoSteFH01U6R/MEzMCG4+BkqogiW5Q4ySsuoWq8qtY5/JqKloyfpxrPHd6i2v9rmpaOwoSffTZ9joARYGn10kwaqfNZbFsJp3QaafdjkIym99ywTDXVe2TzMdrDKs2VM6WUK9stdMemwRhHPeMom7gRTddo2NHJsf0qxVR63XXoeXnE9+4l/OSTACweuZhZ5bOI23Hu3Xmvush516sIUPseePV/APAZOt//6BwKgibbmnr5ztP7z/pdC4IgCMI7iawWMEkHNM3GZ4Gt+9G9MmrDCOIkbAY3Ke9HyIuuRLa1p0cGTDhvMQAHN6lKolGTiwnkmETDSVoO9gJQcMUVAIT/8uSwNNLymuU0JHTCjg/bHqCre536oXYxFNZCIgz7Hk+vX1BXzNzaIhKWw89fagBAM3SWTpzG2I4WANZwKX0DLzLtwhwAXv7dIVzHxSwupuTTqwDo+P4PcC1rWBTm4QMP0xHpUCmki/9NXfDpf4Pjqs9NTUkO3/7QTAB++nw9zx/oeDOvXBAEQRDeEWS5gNFwtaSXQvKn+8DoepDIlnaciIVREqT4gxPQggZ2X4J4vaoGGr9ADW1s2r2D2MAAhqEzdo4SOoe9NFLOggX4ampwBgcJP/NM+roXjroQXTN5bVCJoXQaSddh1jXq+wkTqlNRmF9tOEp/TA1+rFk6kSXNSlC8zIVECDBq3g78QYOOxjAHNqlKpZJrP4VRXEzi6FH6HnsMgEUjFzG7fDZxO57xwiz8HEy6Qk2qfvgfIKZGIbxn+gg+ef5oAFY/tI32/tibfPOCIAiC8PaS1QIm4YKu2WqQ4xABYxhBBl5SwxDzFo9E9xvkzPSiMFuVOCkZWU3pqFoc26beG+44fq6XRtrajmMrv0nh1R8AoPd3j6avWxgoZH7l/HQaqbNzLbbtRWhSaaTDayCc6f9yyeQKJlTkEY5b/HqjqjTyVeWy0i2meLCfhBZgPRfS2fMH5l1eB8Arf6jHStgYebmUfvYfAej40V048fiwiqR0FEbT4P13QWGNMvT+8Z/Aa8T3b++dwuSqfLoGE9z00DZsR0YNCIIgCNlLVguYpK3jahY+G2w9gGZYAOTYxVgdUbSAQe581UwuZ44SJ9FdnTgJ1dxtwnkqCnPISyNVTy4mkDs8jVT0gQ+AphHZtIlEU1P62strl9OY0Am7ARwnSmfnc+qH0nEw6jxwHdj5cHq9rmt89iJVFXTv+iPpBnOzzpvD9JajAKzhMsIDexmzoJ+8kgADPXG2r1XXLP7YxzCrqrBaW+l9UEV3Fo1YxJyKOcOjMDkl8KGfg+5TFUmbfgZA0Gfwo4/PJeQzePlwFz9Zl2nQJwiCIAjZRpYLGA28CIxt+NANlZrJi6i+LrkLqtCDqvW/f3QBRnEAN24T26sa1o33fDBHtm1OD3ccd0I1km/kSHIXKaHT9/vH0tdeXrMc0NgUVpU96aZ2cMrRAgBXzRrJiMIgHeE4v9+qIkTF86u5tDWGaVsc00ZzgEkcbfoB579fiZ3NTx4l0p9ADwQo+4LyvXT+9GfYA4PDuvP+dv9vaY+oe6ZmAVz2TfX9qX+DY6+p563I45sfmA7Anc8c4NWGzPwnQRAEQcgmslrAJFwdV7eHlFGrCExwMA80lT5KoelaOgqTqkaqqBurhjsm4jTs2ArA+HkqYnN4aweOV3Zc+MEPAtD72O9xvRLlkXkjWVq9lC1eGqmrax2WpbroMu1qMPzQtgtad6bvwW/qXHfBWAB+9kI9tuOi5/hYMGI849uVoHnWXUlX11rKx9dTMTqfZMzm1T8fAaDo6qvxjx6N3d1Nzy9/AcD5I85nTsUcEk4iE4UB5YeZchU4SeWHiSix8ndzq7l6TjWOC1/8zVZ6I4k39d9AEARBEN4OslrAWI6Gqzn4LdQoAU/A6I6PwIRizJLh85BSAiZ2sAc7nEDTtLSZ99DGlwGonlREMNdHbCBJs5dGyl9xCXpBAVbLcSKvZJrUXTv1WpqTGp2WjuPE6exc612oBCZdrr6fEIX56IIaCkM+jnQOpmckTb5gJvNalIDZyGLC5HPg4H+w6INjANi9voXu44NopqkGPQJd996H1dOjKpJmexVJ+x+mbdAbHKlp8P4fQclY6GuC338WvD4y3/zAdMaU5XK8L8ZXHt4xbGClIAiCIGQDWStgXNfGcg0czcZvuV4VkhIwmuPHKPCftMdXnoOvJh8ciGxX1T8pH8zhLZuwLQvd0Bk7d3gaSQ8GKXivKqnuffT36fOdP+J8JhZPYvOgeo3D00iemXfHb8G20odzAyafWqQqgu5+/rA3vLGECyP5lIV7sXUf66yLGYzsQ8tfw5hZZbiOy4ZHlWcl/z3vITB5Ms7AAN33qh4wC6sWMrdi7slRmGAhfOQXYAbh4NPw0ncByAuY/PBjc/AbOs/ubeOBlxvO8O0LgiAIwttLFgsYC8s1cTVH9YEx/OiGMsZqjg89ZKbXDgwM0N2tUii5qTSSV400ctIUQgWFxAcHadqj0j2paqT6LZk0UtEH/w6A8DPPYPer8mRN07h26rVsjaprdXWtIxJpUBcdvwJySmGwHQ6vHXbvn1pcR9Cns+NYHy8f7kLTNebMnM30FrX3GftyHDQOHfw2511Vga5rNOzs4ti+bjRdp/ymLwHQ/av/R7KtfVgU5pEDj3C0/2jmYlUz4IrvqO9rvwVH1Gyn6dWFfPWKyQDc9sQ+djVnhk0KgiAIwjudrBUwjmORdExczU6nkLSUgLH9afNuLBbjpz/9KT/60Y9oaGggNLMMdI1k8wDJ9gi6bjB+wfnAkGqkiUUE83zEBpM07+8FIDh9GoEJE3DjcfqfeCJ9H5ePuRzLrGBvVMd1LQ7X36F+MHww48Pq+/bfDLv30rwA18yvAVQUBmDk0vFc2Qo+K0lXsJxNA+dj2b10R+5l2oXVALzkNbfLu+giQnPm4MZidN79EwDOqzqPhSMWknAS3PTcTUSSQ2Yfzf2kmlrtOvDIpyGs0kyfWlzHpVMrSdgO//SbrQzELQRBEAQhG8haAeO6NrZrYnseGNsIoJsqWqI7PrSgMteuXbuWcDiM4zisWbMGI89PcGIxkDHzTkj5YF57Bddx0A2dcXNSaST1P3tN0zJm3iFpJL/h52OTP8Yf+/y4LrS3P0FPz0b1YyqNtO9xiPYOu//rLhiLoWu8eLCTncf6MMtCrJh7PrNaVI+YX/o+Qxw/TU0PMO1iB3/QoLNpgAObWtE0jYrVX1b38vAjJJqa0DSN25beRlmojEO9h7jl5VuGe1uu+A5UTFMRod99BmzV0fe/PzSTkYVBjnQO8vXHdokfRhAEQcgKsljAJLFcE2dICkkzlIDRbB960KSlpYVXX301vaepqYn29nZyvBRRarRAzfRZ+EM5DPZ0c/yQmheUmo1Uv60TO1WNdNWVYJrEduwgfvBg+rwfnvhhepwcXh5UUZ/9B27FcZIwYhaUTwE7fpKZt6YkhytnjgDg7hdUFKZ85XhuPKaRF4vQGyzmN/2fAGwam/9zWHO7ZMImZ8ECcpcuBcui80c/AqAip4I7l92JqZs81fAU9+++P3NBfw585AHw50HDi7DuNgCKcvz84GNzMHSN329t5pHNx97UfxdBEARB+GuQxQLGJun4cNIRGBPdUNEDzfFBQOfPf/4zrusyffp0Jk2aBMDWrVsJTSlBCxjYvXESDX2YPh9j5y4A4MAr6wEYOaGIUH4qjaQGK5qlpeQtuwgYHoUpDhZz1bireLzPR9z1Mzh4kKZjD6hKoPOuU4tevAPiA8Oe4XPLVK+Xv+w8TkPnIHrA5IL3LuG9B5Rh99mCy2mwR9Pd8yLVsw9mmtutUc3tym+6CYC+P/6J2IEDAMypmMPN590MwPe2fI+XW17OXLBsAlz1w8z9HHgagPl1JXx5xQQAbvnDbg61D79PQRAEQXinkcUCxhoegTG19G+642db4x5aWloIBAKsXLmSuXPnArB9+3ZszSU0owyAQS+NNGnxhQDseXEdtpX00kgqCpOqRgIo8tJIfX/8I24ymT7+yamfJOJoPKq0DkeO/IBYvBXmXAvFdSp1s/Enw55hclUByyeV47jwsxfrAciZVcFnzTrGdLTgajo/TfwTDhoHD32L899fC8AWr7ldaPo08leuBNel4wc/SJ/3wxM/zAcnfBDHdfiXF/6FY+EhUZXpH4Tz1FgCfv+P0KvE0A3LxrNkfCnRpM2Nv96S7hQsCIIgCO9Esl7A2JqL33Jx/Zn/4UYdhxe2vgTAJZdcQn5+PuPHjyc/P59IJML+/fvJ9dJI0Z2duEmbsXPmk1tcQrS/j/rN3mykVBppa0c6jZR3wQUYZWXYXV0MvPBC+pp1hXUsG7WMTYMGYb0c2x7k4MH/ANMPF39dLVr/fRjsHPYcqSGPj2w+Rns4hqZpTPjQPFbt78BnJWnMGcPTifcQjzcRrHxSNbeLZ5rblX/xn0DXGXh2DdEdOwDl1/nqwq8yvXQ6ffE+bnruJqJWNHPRy74FI+dCtEc1ubMSGLrGdz8ym9JcP/taw/zH43vfiv9MgiAIgnBOyFoB4zgpAeOoYY6mV0HjGLxmHiGeTDBy5Ejmz58PgGEYzJ49G4AtW7bgryvEKFKjBaJ7u9ENg2kXXgzAzrVPATBiQhGhAj/xiMWxfSq0ovl8FF51FTA8jQRw7bRrcdG4rzUO6LS3P0F390sw7YPKD5MIw/P/NWzPgrpi5tYWkbAcfv5SAwC+ylyumrWQJUeUH+e3xifooZj6I99n4dVFQKa5XWDcOArf/34A2r/73fR5A0aA7y7/LiXBEvb37OffN/x7xqBrBpQfJlgEza/BM0pgVRQEufMa9Y5++cpR/rLz+Fn8lxEEQRCEc0/WChjXTWLbOpYOPhtcn0rnaI6PRl1FOS677DJ0PfOIqTTS4cOH6e3rPWm0wPTllwLQsH0r4a5OdF1LVyPtXd+SPk/RB68GYOD557E6MxGV+ZXzmVIyhSNxi97gHAD2H/i/OCRV1APgtfugMzNIUdO0dBTmVxuO0h9Tz1GxcjzXt4Wo6O8mbgb5efx6HDdK2LnnpOZ2ZV/4Avh8RDa8wuCGDelzV+VWccdFd2BqJo/XP86v9v4q8wKLauGDatAjG++G3UqMXTSxnM9epMYd/MvvdtDUPaQcWxAEQRDeIWSxgLHBgYSm4bNMNEPN9NEdH3FUNKa0tHTYnuLiYsaOVf9z3rZtW2a0wIEe7IEExSOqGTV1Oq7rsHvdswBM93qw1G/roK9D/c88MH48wVkzwbLo++Of0ufXNI1PTv0kAPc0t+HzlRGJ1NPY+HMYcyFMfA84Fjx767D7umRyBRMq8gjHLX69UZVR6wGT86+6gA/sP4TmOmwOLmQrc2lt/R2zLo8Na27nH1VN8TXXAND679/A7ss0pZtfNZ+vLPgKAHe8dgebjm/KXHjiSliqyrH5wz+lhdVXLpvE7JoiwjGLLz64laSXPhMEQRCEdwpnJWDuuusu6urqCAaDLFy4kE2bNp127f3334+macM+wWDwtOvfKK5roXsCxnT8aLoXKbB94Pl5Q6HQSftSUZitW7dilAXxVeeB4xL1RgvMWH4ZADufewbXcSitzqN2WimuC9vXZMywRVd7Zt7fPzqsd8p76t5DRU4FzZFuwgXqXEcafkQs1gIr/h00Hfb9GY5mqoN0XeOzF6mKpHvXH0kbaHNmlvPRvMnMbFJl1vdZnyVGgOb2/2TqhWpQZaq5XdkXPo85YgSJhgaav/zlYQbjj0/+OFeOvRLbtfnK81/h+MCQ1NDyr8HopSq99fCnIBnFZ+j88GNzyA+abG3s5TtP7z+T/zSCIAiCcM45YwHz0EMPsXr1am699Va2bNnCrFmzWLlyJe3t7afdU1BQwPHjx9Ofo0ePnnbtG8V1rXQExnD9aHpc/eCoXiymaWKa5kn7Jk+eTCgUor+/n8OHD6ejMIPeaIEJCxfjD+XQ39FG425lip1zqeqau/flFmIDShgUvPcKtECA+MFDxHbtSp/fZ/j4+OSPA3Df0d0UFS7AcaIcOPhNqJgMcz+lFj79NRgifK6aNZIRhUE6wnF+v1UNdlSG3vn8fX2YvFiEbl8ZjzjXEB7YzpiFO9PN7fZvasUsLqbmJz9Gy8lh8OUNtN3+n+lza5rGLYtuYUrJFHriPdy07iZiVkz9aJjwoXsht0JNz35CRWtqSnL49t/NBOCnz9fz3WcOSJM7QRAE4R3DGQuYO++8k+uvv55Vq1YxdepU7r77bnJycrjvvvtOu0fTNKqqqtKfysrKN3XToEy8hgMJQHf9aKZXZeMJmFNFX0AJm1mzZgHKzJszuxx0SB4bINkRwRcIMmXpMgB2PfcMANWTiimrycNKOOx6QUVhjPx88i9TEZbe3/1u2DU+NPFDhMwQB3oOEi/5OzTNoKPjaTq71sGym8GXC82bYfej6T1+U+e6C1R666fPH8Z2lFjwVeSwcsFiLjmwG4Cn9PfRQB1HGr/N3MvVe9zoNbcLTp5M9beVSbjn17+m+9e/Tp8/aAb53vLvURQoYk/XHr75yjczgiS/SokYTYetv4Kt/w+Ay2eM4CavP8z31xzkX3+3U9JJgiAIwjuCMxIwiUSCzZs3s2LFiswJdJ0VK1awYYh59EQGBgYYPXo0NTU1vP/972f37t2ve514PE5/f/+wz4m4roXmuCTQlIDRVUTB9QTM66Wp5szxDLb79xMlQXCCN1rAi8LMuFgJk4ObXiY6EEbTNOZcqnqw7HjuGJaX4kmZefv//Pgw30lhoJAPjP8AAL88vJaaUf8AwIED/46dUwRLb1ILn/13sOLpfR9dUENhyEdDV4Sndremj5dfNoGPhksY296Moxn8j30DCbuH/DF/Oqm5Xf6KFZSvXg1A23/cxsCL69PnGZk3kv++6L/RNZ0/Hv4jD+4f0h14zIWw/Kvq++P/DG3qv9FNKybyH1dPR9fgodea+PT9rxKOZdJTgiAIgvB2cEYCprOzE9u2T4qgVFZW0traeso9kyZN4r777uMPf/gDv/rVr3Ach8WLF3Ps2Olb1t9+++0UFhamPzU1NSetcV0L3XaxHQ3XCKAZSgi8EQFTWVnJqFGjcBxHmXnnZiZUu45LxZhxlI8eg51MsvfFdQCMm1dBXnGAaDjJgY1qPlLOwoUEJk7EGRig6557hl3jE1M+gYbG+ub1UHIlAX8l0WgjjY0/g0VfgLwq6D0KmzL7cgMmn1pcB8B3nzmQ9sLofoPzrrqQqw4ewW8lOWKO51lW0nTsXhZcpZ4z1dwOoPT66yj8wAfAtmn+8peJH8pUPZ0/4nxWz1MC59ubvs3mts2Zm176zzD+UrCi8NtrIaaE498vHM3PPjmfkM/gxYOdfPjuDbT0DukrIwiCIAh/Zc55FdKiRYu49tprmT17NhdddBGPPvoo5eXl/PSnPz3tnptvvpm+vr70p6mp6aQ1rmtjuA6OrWHrfnTPA+PYaojj6VJIKebNmwfAK6+8gm9ioRot0BMncbQfTdOY7pl5d619Ctd1MQydWZcoIbXt2UZcx0XTdcq9oYrdv/glyeMZc2xtQS0X16q+Mv9v/+8YP0G19284+hOidjdc/DW18IX/hkh3et+qxXWU5fk52D7Af/5lX/p4zowyriqdyXn1ewB4yP17usknEfxZurndJq+5naZpVH3j3wnNn4czMEDT527A6s5c49qp13J53eVYrsU/r/tn2gaVIEPXVWl1wSjoOgR/+mLap7NiaiW//ewiyvMD7GsNc/WPX2J3SybqJAiCIAh/Tc5IwJSVlWEYBm1tbcOOt7W1UVVV9YbO4fP5mDNnDoeGRAVOJBAIUFBQMOxzIq6bRLddHEfDMfzpCIzjKAHzv1U6zZgxg4KCAgYGBti+eyeh6d5oAW/69JQLlmH4fHQ0NtBWr+516tKR+EMmPa0Rju7qAiDvoovImT8fN5GgwxuqmOLaqdcC8KfDf8JXsJji4kU4TpwDB74Bsz+upkPHetVcIo/iXD///SHl0bn/5QbW7stMw57wofn83bEkFf3dxPQQv+DTdHU/y+z3KXGyZ30L3S2DAOh+P6N++EN8tbUkjx3j2BduxInH0+f6v4v/LxOLJ9IV62L1utUkbBW9IacEPnw/6D7VG2bTzzLvbFQhv//8YiZW5tHWH+cjd2/guX2nN28LgiAIwrnijASM3+9n3rx5rFmzJn3McRzWrFnDokWL3tA5bNtm586djBgx4szu9AQc18ZwHLA1NYlaT3j388YiMKZpsnjxYgDWr19PaI4SMNEdnTgJm1BePhPOU7/vek4NPfQHTaZdoMqXtz6j+rVomkbFV/4ZgL7fPzYsXTOnYg7TS6eTcBL89sBvmTTx39E0H51da+noXgeXfUMt3PQz6D6S3rd8cgWrltQB8JWHd9Der/w9vvIcLl54ASv270JzHV7VFrGF+XT0f4cxs4pxHZfnfrUXK6FST2ZxMTV3/wS9oIDo1q0c/9rX08bdHF8O31v2PQr8Bezo3MFtG2/LvJyaBXDZN9X3p/4Njr2W/mlUcQ4Pf24xi8eVMpiw+cwDr/KrV958VZkgCIIgnAlnnEJavXo199xzDw888AB79+7lhhtuYHBwkFWrVgFw7bXXcvPNN6fXf+Mb3+Dpp5+mvr6eLVu28IlPfIKjR49y3XXXvakbd50khuvgeimkVCM7y1GP9EZ6zcydO5ecnBx6e3s5MNiEURrETdhEd6juuqnOvHvXP08yrkTEzOU16LpGy8Fe2hqURyQ0ezb5l64Ax6H9u99Ln1/TNK6dpqIwD+57EDM4itqaTwOwb99XGRwxBsZdDHYC1nxj2L396+WTmTKigO7BBP/88HYcryqp/LLxXJmoZlaTEkr3u9fRE29h/LLN+EMmrfX9PHPfnvT6wNixjPr+98A06f/Tn+j8SWagZE1BDd++8NtoaPzu4O94+MDDmRtY+DmYchU4SeWHac/MRioM+bh/1Xl8aN4oHBe+9tgubn9ib/qagiAIgnCuOWMBc8011/Cd73yHW265hdmzZ7Nt2zaefPLJtLG3sbGR40O8ID09PVx//fVMmTKFK664gv7+fl5++WWmTp36pm7cdS1M28G1wTH86IaqjLHPQMD4/f505Gj9+vXkeMMbBza04LoutdNmUlhRSSIa4cArajhkXnGACeepZ93mRWEAym+6SQ1VXLOGyJYt6eMrRq+gKreK7lg3v93/W8aMuZG8vCkkEp1s3vIxwktXAZoqqR4S6QiYBj/82GyCPp0XD3Zy73oVodH9Bgs/cCErj7SQHx2kSy/nEa6hufWHrPzHGgxTp35bBy88mOnbkrtoEVW3qHlHnT/4If1PPJG+zpLqJXxx7hcBuG3jbWxr36Z+0DR4/11QNhH6m+F/LoUDT2Xenanz3x+ayepLJwLw0xfqufE3MsVaEARB+OtwVibeG2+8kaNHjxKPx9m4cSMLFy5M/7Zu3Truv//+9N/f/e5302tbW1t5/PHH02XMbwZl4rXRrFQERgmYVATmf0shpViwYAGBQICOjg6OlYTRfDrJ5gHi9X1ous70ZSoKk+oJA6RLqg9vaae/U1XjBMaNo+jvVHfe9jvuTIsHn+7jH2f+IwA/3PpD2qK9zJn9C/LzppFMdrG58ev0zHuPOvEJze3GV+Rzy/umAfDtp/ax85gyzYamlbGyah4XHNwOwJO8j3q3nIh2H5d+eqrSQy80s/kvDelzFX/kI5T8wz8A0PKvNxPdti3922emf4ZLR1+K5VisXreajojqSkywAFY9menU++tr4KUfpO9R0zS+eMkEvnvNLHyGxhM7W/n4Pa/QNZApDRcEQRCEc0HWzkJy3CSmo+Yh2YYvI2DcNx6BSa0777zzAHhp08uEvOGNAy+qbrjTlq1A03SO7d1Fd4s6VlqdR+3UEm+8QKZCquzGG9ECAaKbNzPw3Lr08b+b8HfMrZhL1IryjQ3fwOcrZu7c/0dR0UJse4BtedvpqMiDxg1qzMAQPnZeDSunVZK0Xb744FYG4xaapjHxQ/O5rNPHuPZjuJrOvXyOpuaHqJjYzYXXqKjIxj8eYc+QIZQV/+cr5F18MW4iQdMXbiTZnOn4+80l32Rc4Tg6oh388/P/TNL2er3klsInfw/z/gFw1eTqP3xhWP+aq+eM4pefWUhB0GRLYy9X//hl6jsG3tD7FwRBEISzIWsFjOva+BwVgXF0P7qhBjgmHTUI6Y1GYADOP/98TNOkpaWFjjoLNIjt6ybZHiG/tIy62Wp+0ranMuJitheF2fPycWKD6n/2vspKSq5Vwxzb77wD1/b6uGg6/3fx/8Wv+3mp5SX+VP8nTDOf2bN+TnnZpThugh2TQ7RUBeCZW8HONIrTNI3//OBMqgqCHOkc5Bt/UmXUZlmI5edfyPJD+/BbSeq18TytrWTf3luZflE1894zGoB1v95Pg+fp0QyD6v/+NoHJk7G7umj63A3YA0po5Ppy+d7y75Hny2Nr+1b+69X/yrwg0w/v+x5c/m3VrXfb/4MHroSBjsw7HFvKo59fTE1JiMbuCB/8yctsOpIp3RYEQRCEt5IsFjAWpmujp6qQThAwZzIwMjc3N90XZsOOTQSnqCnWA+tVhGLeFR8AYMezf6G/U5UNj5pcTOmoPKy4zW4vWgNQet116IWFJA4dpu+xx9LHxxSO4YbZNwDwn5v+k45IB4YRYPr0HzFyxEcAl70T82nIbcF9dfhYhuJcP9+9Zjaa1w338R3KY1R+6XguZgzn16uuuQ/zcY6E62lvf4KF7x/L5EVVuI7LU/fsorVepZ/03FxqfvJjzPJy4gcP0rx6Na6l3l1dYR3/eYGaofTQ/of4/cHfZ25C02DhZ+HvH4FAITRthHuWQ2tmDtT4inx+//klzKopojeS5BP/s5E/bMu8G0EQBEF4q8hyAZNEt3jTERiAxYsXo+s6DQ0N9E1S5xjc0oY9kKB2xixqps3Etiw2PKLa7w8bL7D2GHZSzQgyCgsp++xnAej4/g9wopmOtf8w7R+YWjqVcCLMt175Fq7rousmkyffxujRnwPg8JhcDtX/F260Z9j9LRpXyueXqYnV//roDo71RND9Bud/4CIubO6msq+LmBbiF3yGXTv/DwMD+1j2icmMnl6KlXT4813b6WlVPWJ8I0Yw6sc/RgsGGXzhRdr+69vp61xUcxGfn/15AL71yrfY1blr2H0w/hK4fg2UjIO+Jrj3MtibiUyV5QV48PrzWTmtkoTt8KUHt3HXc4dkEKQgCILwlpK9Asax0Jwkfgssw4duKAFhv8FGdidSWFiYHvL4yqEt+GrywXIZ2HAcTdNY+lGVGtr9/LN0t6gxCOPnq/ECkf4E+zdlRikU//3HMUeOwGpvp/sXv0wfN3WTbyz+BqZusrZpLU81qKoeTdMYP+7/MGHsvwLQWKWx5+WP4DjDZw7dtGIis2uKCMcsvvzQNizbIWdaGcurF3Dhge3ojsNr2vm8ps/k1Y0fI5FoYeX106moKyA+aPGnH2xnsFd5V0IzpjPyP1W0peeXvxw2+PGzMz/LspplJJwENz13E53RzuEvq2yCEjFjl0FyEB76e3jhO2lzb8hv8OO/n8d1S8cA8N9P7ZdBkIIgCMJbSvYKGNfCRAkYRzcx1AgkHMdA13V8Pt8Zn3Pp0qVomsbBgweJzPADMPhKC27SZuTEKYyddx6u4/Dyb9W0ZsPQmXmxN17gGTVeAEAPBKj40pcA6LrnHqyeTDRlUskk/nGGqkq6beNtdMcyPpHauuuZWvxJNNelVatnx5Z/wLYzERyfofODj84hL2DyakMPdz13WJ3zQ/NZMpDPrGNebxjneiJ6klde+hCu1sv7vjCTwooQ4e4Yf/rhduJRFa0qeM9Kyr+sRiEMHfyoazq3Lb2NuoI62iJtfPKJT7K/e//wlxUqVumk89SzsPab8Oj1kFT3a+gaX3vfVL7x/mnDBkH2yyBIQRAE4S0gawWM41ponoCxdA19iIAJhUJomnbG5ywtLWXaNFW2/NSeF3CKDJxBi8Etyvey9BoVhdm/4UXajijxMG3pSPxBg57WCA07M5GKgiuvJDB5shr0ePfdw65z3YzrmFg8kZ54z/AOuMCI2bcys6MG3Xbp6n+Frds+RTKZmTlUW5rDNz+g7vH7aw7wWkM3ZmmI5YsuZFHDYQqig/QYpdyfvA5b72DD+g/jCyW56ouzySnw09U8wF9+siOd8ir9x+uHD348eBCAfH8+P7z4h1TnVXNs4BifeOIT/Ll+eIUUhg+u+G94752gm7DzYbj/vRDORKOuXVTHPdcOGQT5kw00yyBIQRAE4U2StQLGdS10kvgtF0vXh0VgzjR9NJQVK1aQk5NDa2srL+YfwMVl4MVmXMelfPQYJi+5CICXHlKpIX/IZPpF1QCsf/ggSa+Nv6brVHzlKwB0//o3JIZM3/YZPr655JsYmsFTDU/x7NFnMzegaZRd+D3m7ApjJh36+jazectHiccz86eunjOKq+dU47jwpQe30RdNUnHpBN5buoCLDmwH12W9fzm/sD5NkqNsXP9x8koM3nfjLHxBg+YDvTx7/x41kPJUgx+71JynusI6HnrfQywZuYSYHePmF2/mvzb9F8kTUlss+IwqtQ4WQfNm+NlyaNmW/vmSKZlBkPvbwlx910vsapZBkIIgCMLZk/UCxmeBrWvohkrfpCIwZ0tRUREf/ehHMQyDgx0NbA4eweqMEtunUj2LP/L3aLrOka2vcWyfqv6Zd3kdecUB+jtjbPpTZqZR3tIl5C5eBMkkHUNGDABMLZ3Kp6ersQLfeuVb9MZ6Mz+OnEPR4v9g7o4+/HGHwcEDvLb5I0QimXN/4/3TqCkJ0dwb5d9+vxMMjTnXL+czTh3L9m8D4Gnfe/mN8wmizi5effk6ympyufxzM9ANjUOb21n/8EFlJB46+LG5mWM3/lN68GNhoJC7LrmL62dcD8Cv9v6K65667mRfzJgL4fq1UDYJwi1w33vUMEiPGaMKeewLS5hYmUd7OM5HfrohPahSEARBEM6U7BUwjgVeCsnWQTNVSuTNRmAAamtrueqqqwDYxhEO6scJv6giKMVVI5mx/DIA1v/mF7iuiz9octHHJwGw/dlGOhrD6XOV/7Ma9Nj/+ONEd+0edp3PzvosYwvH0hXr4tuvfnvYbyz8R/KnrmL+9l5CUYdY7Bivbf4Ivb1q3EB+0Mf3PzoHQ9f4847jPLL5GHrA4LzPXcoXwqUsPaC69D5uXM3v3GsYSLzE1k1fZtSkYi75hykA7HjuGFufVuMQThr8+P+zd97hcVRn2//NbK/aot7dJbnJHYPBNqaGZiD0GtqXQirpIQRC3pBGQugtdEIHU0w3trGxce/dVu/SFm2vM98fs9rVSjKYvCTAm72va64508+clfbcez/tVzemI4dUoorvTf8edyy8A5PGxOaezVzw+gWZsgMDcI6Ba96DsSdCIgwvXAkr/pB27i2zGXjxW0czb2w+oViSax7fyJNrm/6VjyiHHHLIIYf/cnxlCUxCSqIacOIVQFBlCMz/RoEZwNSpU5k3bx4AqzR7aGlqIdaqEJOjzr0QlUZD+95dNG1T6h5VT85n3MxCZBk+eHIPUirixjBxItYzzgCg5/a/ZIUT61Q6bj3mVkRB5PWG1/mw7cPsTpzyRwyl85mx1YMlJBCPu9m0+QK2bbsWn2870yvt6VpEv3ltF419QUSjhqO/fQrf9pg5+uAOAF4Rz+dVzsETfIOdW3/H+FnFHPP1sQCsfeUQez9W8spkFX584w367r03qzuLKhfxzGnPMDpvND3hHr7xzjd4ft/z2SHS+jy4+DmYe72yveI2ePEbEAsBYNVrePQbszh/plII8tev7uJ/lu7OFYLMIYcccsjhM+ErS2BichJIKCYkQUJQKb4nn4cCM4Djjz+empoaJEHmfe122j9QInEsznzqTzoNSKkwkkJW5p0/Hp1RTV9rgK2DSgwUfP/7CBoNobUfE1z9UdYzphRM4bJaxTn4ljW34Iv5MgdVajjvMXR545i+pY8SnwkQ6XN9wIaNZ7Nt27VcPC3EUaMdhGJJvvfMFmIJCZVZy/xvn87/69UyJ5Xk7nnhEpZyBj2ex9i3617qT6hMZxNe/sRemncpfi9ZhR/vupv+pUuz+jsqbxT/PO2f6dpJt358KzetuYlIIpI5SVTByf8DZ94NokYxJT16CvQrSe00KpE/njuFH5+kkK+HVjXynX/mCkHmkEMOOeRw5PjqEhgJBEHxgZEEGdIKjPpzIzCiKHLOOedQ5CwkIsR57dBygl2K8+nsxeeh0RvoaTrEgfVrADBatWllY8PrjfT3KtE22vIy7BdfDED3H/6AFAplPef6addTZa2iJ9zD7Rtvz+6EwQYXPYtaa6NuazNzPdMoLlrMAJHZvPlsfjjjQSYVtLOjvZ/b31NIlsqqY9G3z+KaTpjZtAeAfwpX8i6n0tp1O00HnuPos8cwfnYRkiTz9oM76WlWyJP9/PNxfOMbAHT+4pcEP/44q0smjYnb59/OD2f8EFEQWXJwCVe8fQUdgY6s85h+GVzxGhid0LlNydybqrgtCALXHz+Ov19Yj1Yl8tbOLi588GOa+oL/wieVQw455JDDfxu+sgQmISeBJNqECpn4IAVG/FxMSAPQarVcfPklGEU9HjHIi888jyRJGK15zDx9MQAfPfcUUqruUc3cEsom2EnEJVY8vTdtXnF+8/+hys8ndugQnTf9Jsvsolfr+e3Rv0VA4OUDL7OmfU12J5xj4IKnQNRg3PEOE3uczD3qXYqLzwZEAv0f8sNpf+R70x7gnS3LWX1AcbBV2/Sc8s2z+UZrjGktCrF5XLiG5cKJHGz6FR1t73H85bWU19hJRJO8cfc2vD0KuSr88Q3pwo8tV11N3333pWs7gUJArpp0FfefcD82nY3drt1c8MYFrO1Ym933qqPh2uVQWAeBbnj0a7D9hfThs+rLePLq2eQZNGxt9XLi31Zy82u7cAdj/+vPLocccsghh/+7+MoSmJgkIwhx1JIWQczkFfk8FZgB5OXlcd4JZ6GSRRr723n1pSX4fD5mnHY2erMFd0cbuz/8AFAm9gWXTEClEWnb62Hfx0pOFLXdTvnf/goqFb433sDz5FNZz5heNJ2Lai4C4Oa1NxOMD1EiqufB6X9T2iv/iPHQJibW/SWLyEwt2MWvj7qdbduvobVLUTo0+UbO+H9f54qmAFNalUR3j3Adq1Tz2b3netyuTZz6zckUVFoI++O8fudWQr6YUvjx9r+Qd/bZIEn0/v1OWq6+hnhPT1a35pbO5bnTn6PWUYs36uWb73+TR3Y+ku0XY6+Cq9+F8adCMgovXwPLfgsp09uc0U6WfOcY5o8vIJ6UeWxNE/P/vJz7Vx7KmZVyyCGHHHIYEV9ZAhOXZGQhiSahQVBl/C+Syc/HiXcoqufWsChPKfi4bdd2/va3v/Hyq69SvfBkZGDNi/8kEVfyo9gKjcw+XUmjv/rFA4R8ippgnDWLop/+BIDuP/2J0KZNWc/4/vTvU2YuozPYyd82/W14J6ZfBkd/V2kv+Ta0bsBoHJUmMgWFi5FkgRr7DvbvvoCt267B59uOtsjE2ddewOUNbia1NyALIg/yHT7WzGXLlisIhw5y+vVTsebr8fVFeOPubcQiCUSDgdLbfk/JH25DMBoJffwxjWefQ+CjbD+eUnMpT5z6BGeNOQtJlvjbpr9xw8obskmYzgIXPg3zlMy/rLodnr8Moko17FH5Jh6/ajZPXj2b2hIr/kiCP7y1l0W3r+SVLW05J98ccsghhxyy8JUlMAlZITBqOaPAyLIAiJ+7AgOKsjLzgvmcGJ9CkZSHLMvs27ePDQebCI+fSh9q1r35Wvr8qSdU4Cw3Ew0mWP3CgfR+++WXY/3a1yCRoO0HP8hSNIwaI7ccfQugVIPe0LVheEdOuAUmfE1RMp69GLyKs7DROIopk26naMwrrO2YgyQLuFzL2bDxbLZuu4aouYGvX3UBFx/oorajCVkQuY/vsUk7jXVrz0cSezjju/XozRp6W/y8/cAOkglFIbEtXsyoF19AN2ECSZeL1muupeevf0tXsQbFDHbrMbfy66N+jVpU817ze1y89GIa+zO5axBVcMLNcPYDoNLC3jfgkZPB25I+5dhxBbzx3Xn85bypFFv1tHvD/PC5bZx5z2rWHBySeyaHHHLIIYf/WnxlCUwkKYOQRC1pEFVK0jUpqaTj/XcQGABtmZm62VM4IzaT843zmTN7Dnq9nqRKQ6ygjPc2b+fpp55i//79CAIcf1kNggAHNnTTvFOJ8hEEgZLf3Ypu3DiSvX20/+CHyLGMv8eckjmcN/48AG766CZC8WyHX0QVnPMQFE2CYA88cyFEM3lnplRPprT6f/jVR7/io/bZyLKYJjJ7PTdw2oWTuWh/K+O7WpAEFXfzQ7YZalmzYjH6vAinXz8VtVakdY+HD57Yk67vpBs9murnnsV24QUgy7gefJDmy68g3tmZfrYgCJw/4XwePflRCg2FNPQ3cNHSi1jWsiz7HaZeCFcuBVMhdO+Eh46HlnXpwypR4Oszyln+4wX85OQJmHVqdrb7uPjhdVz12AYOdPvJIYcccsjhvxtfWQITlUAWJFSSFkTFhCRJyuv8O0xIA8g7qRrRrMHqVnOMcRI33HADZ511Ftp4FASBAwcP8s9//pM77riDbfvXUT5Hi4zEin/uJRZRFAvRaKT8rjsRzWbCmzfT/ac/Zz3jRzN+RLGpmLZAG3/ZmJ07BgCdGS56NkMAXroWpIyvyDeOqeaiucfwyK5L+eVHv6QjthBQ4XItZ2fnlUw7aTXf8r/E0X3rSKLi7/yYncYqVr13Jo4yFaf8v8mIosD+9d2sfeVQ+r6iXk/JzTdTdsff0n1vXHw2/g+WZ3WvvrCe5854jumF0wnGg/xg+Q+4c/OdJAf1kYrZSube4skQ7IXHT4ctT2fdx6BV8Z2FY1nxkwVcPrcKlSjwwd4eTr7jQ37x8g56/BFyyCGHHHL478RXlsBEkiAJSURZm1ZgkpIK+PcpMACiQU3eqYp/i39ZC0IwybRp07jgnMUYD+1E4+pCq9Hg8/n46KOP2Nz0Pq6itbRLm3jpsbdxuVxK9t7qakr/pGTf9Tz1FP2vZcxPZq2Zm+feDMAL+1/gjs13DCcxtgq48J+g0sH+t+D936QPCYLAD08cz+8WT6I3XMivV5zNm11/pbDwbARBhS+6kcKxq/mO80/cL32D67iHDRzFAZOVlW+dTXmNlYWX1wCw5b0WVr94IF38EcB6yimMeuVl9JMmkezvp+3b36b7ttuylKR8Qz4Pn/wwl9ZeCsBDOx7iO8u+Q390UA0kWwVc9Q7UngHJGLz6bXjq69C5PetV8806fnvWJN794XGcVFeEJMMz61tY8OcV3PH+fkKxBDnkkEMOOfx3QZCHzYxfPvh8PvLy8ujv78dqtQLwwKqf4974KjOXTKJhusDYMzYRClnYvOlsbrrppn+pGvWRQpZleh/YTqzJh2GiE+dldQB88NgDbHnrdXRmK3OuuZ7Wzi4OHTpEMJgdUWSz2Rg7dixjxozB8s47+O9/AEGvp/rZZ9DX1KTPe2bvM+lq1ddOvpbvTvvu8Pfa8SK8dLXSPvMumH551uG3dnTy/We3EktKzK52cPcFRYT9H+Bxf4THvR6JbBUjihZd3MKk+ltp3VrOuleUekXOcjMnfqMOZ5k5Mw6xGD1//Rvuxx4DQD9pEmV/vR1tZWXWPd9oeINb1txCJBmhzFzGHQvvoMaReU8kCVb+ET78M8gplWbSubDwV0oI+RCsb3TzP2/uYVurF4BCi44fnTie82ZWoBL/fZ97DjnkkEMOnw0jzd+fF76yBObOlb/Av+Vl5rwyk+bZEapP3UogYGfvnvP42c9+9m/vU7wrSPedm0GC/G9MRD/BQSIe59mbfkJ3w0HKauo4/6bbQBDo7u7m7efW0NHdQlznAzJDLggChZEI5dt3MCEeZ8wLz6PKy0sff2r3U/xxwx8B+PbUb/Ot+m8N78zy3ysEQFTD5a8qIdeD8HGDi2sf34g/mqCm2MLjV82myKpHkqI0f/wOH+17kliZhwp1MyLSoCtFdKpauveNxtc6gVj/WOacOYGpx1cgDCIK/g+W0/mLX5Ds70c0mym59bdYTz01qw/73Pv4/vLv0x5oR6fS8Zu5v+GMMWdkv4frkPIuO19MDY5KIWTzfwrW0qxTZVnmje2d/OmdvbS6FSfu8UVmfnFqLQsmFPxbCWwOOeSQQw5HhhyBGWEA/vjBr4htf55jlsyjba6XshO34/M5aW25lO9973v/kX5532ggsLodlVNP8Q9mIGhEvF2dPPnz7xELh5lz9vnMu1BRRML+GP+8eR3hUITRx+khz8/Bgwdxu93p+5n9fmYmEhz3178iqlTp/Y/vepy/bPwLANfXX8//m/r/sjsiSfDSVUrKfoMdrlk2TLnY3eHjikfX0+uPUmYz8MTVsxlToKgpnasP8diyl3hjygRsed3Us4l5rERLPPsxCS2h3nFo5OnMWHgWhaVTEQTFChnv7KT9hh8T3qzUhrJdcAFFv/g54iBzXn+0n5+t+hkftSth2BfXXMyPZ/0YjajJfp/O7fDBrXDgXWVbrYfZ1ykh2EZH1qnRRJIn1zZz1wcH6Q8r/T1mrJNfnFrLpLI8csghhxxy+OKQIzAjDMCt790I25/lqKXH03dMD4ULd+D1FuHqu5LrrrvuP9IvKZqg6/ZNSL4Y1hMqsZ5QBcC+tat4444/giBw7i9uoXrqdGX/x528/9geVGqR8345E2epGY/Hw549e1i9ciWhqOLLU6BSc/JFFzJmzJi0kvDIzkfSuWG+P/37XDP5muzOxMNKltuOzZA/Hq5+TylDMAit7hCXP7Kexr4gdqOGR66cxbRKOwBtH+zl8VWv8uKUGXTa8jHJfm7iV4xjNHlFDtyej4jFssOYRcFGfsExOBzH4LAfg15TTO/dd+N64EGQZXTjx1N2x9/QjR6dviYpJblv2308sP0BAKYXTucv8/9CgbFg+AA3r4H3b4HWVCkDnRWO+R7M+ZbiyDwI/aE496w4yGMfNRFLSggCnF1fxg0nT6DM9u9z6s4hhxxyyOHwyBGYEQbgxrdvQrfjaWa/eyq+eW04jt2B211KOHQtl19++afc8fNDaFsv7mf2glqk+IfTUTuVyfL9h+9h23tvYbDmcfmf7sJsdyhmj7u20bLbjdmu45yfzMDiUBSKaDTKikceZUNbKwmNokhUV1dzwgknUF5eDsBD2x/izi13AnDDjBu4ctKV2Z3xdykhyb52GL0QLnlRKQg5CK5AlKse28C2tn4MGhX3XjqdhRMKAWh6ayePb3iDl6fOptvqwCL38yvpN9TJpzNz0Y8IhQ7Q3rKChj3voTbvRtREs+6t11dgMo1F7dUQfe1jxJYQar+esm/+Csfi87POXd6ynF+u/iWBeIACQwF/XfBX6gvrhw+wLCtKzLLfKhFXAKYCOO4nMONKUOuyTm91h/jzO/t4bZtSl0mrFrl63ii+tWAMVv0QpSeHHHLIIYd/K3IEZoQB+OmbN2Pe/gQzlp9FbH4D1qN20tdXgSh8h/POO+8/1jdZlun7x06iB73oaxw4r6hDEATisSjP/OoGeluaqJg4ha/feCuiqCIciPHKXzbj6QphKzJyzo+nY7Bo0/druPlmPt67j4PjxiGpFPNMbW0txx9/PAUFBdy37T7u3XovAD+Z+RMunziErHVug0dOgXgIZl0Dpw0pDgkEowm+9fRmPtzfi1oU+OO5Uzh3hkKSDry2mSe3vcMrU4+i12InT/bwy/hvsO4fx6ILb8NaUEgyKbHhzQPsWb8SY+FuLKV70dkbgMOn/VdH9JjyazGYqzDoKzEYKvBKWm7deB87vM2oRQ3X11/PJbWXoFePEEUmSbDrZfjgd+BJJcezVcKCX8KU85X8OIOwrdXL/7y5h/WNionOYdLyvePHcvGcKrTqr2zwXQ455JDDVwo5AjPCAPzwtVuw73icqR99HXHhPowzdtLdPQqL+fucccYZn3LHzxfx3hDdd2yGpIzzsjoME50AuDvaeOrnPyAejTD36xdz9HlKReqAJ8JLf95EwB2loNLC4h9OQ2tQlBIpFqP5sstwHTjInmOOoSHfiSzLCILAtGnTmD9/Pk82PJk2wfx89s+5pPaS7A7teQOeuxSQYeGNcNyPYYhTaywh8bOXtvPKlnYAfvm1Gq47TvGb2fPiep7es4xX6ufiMtuwyy5+za9R75aYUPNLpiw6A0EQ6Gro571Hd+PrDSNqwkxcFKB6eoJYrI1wuJVwuIVg/0EkVbZSMxRJVPTEJVwJgSAmJhQew1GVp5NnHotBX4FKNYjQJOOw+QlY+ScIKHWmKKiF42+EmtOy3lOWZd7f08Ntb+2hoVeJBKt2GvnZKTWcMqk45+ibQw455PBvRo7AjDAA33v5Jhy7/snkdedhOGEPuik76OwYT3HxDZxwwgn/8T72v92Ef0UrKpuOoh/NQNQqisDuVct56+7bQRA478b/oXLSFAA8XUFeuX0zYX+csgk2JQOuRrkm3tVF47lfJ+lykTjrTHbW17Nvn1JNWq1WM3v2bHZbd/OPff8A4JdzfpkuBJnG6jsyuWHqzoKz7lHqEQ2CJMnc9tYeHlqlKBrXzBvFL79WiyDAjmfW8GzDKl6pn4vHlIdT7uWX3ILN4yLWejyLLv0N1vxCYpEEH714kN2rFZONs8zECd+YSH55xkfFt345LX/9FTGxj2SRCs0JU5BKtYTDrUQi7ZAV+TQcOm0RekMFBkMFBkMlBn0FBk0hhj0r0H70IELEq5xYNhNO+A2MOi7r+nhS4tkNrfz9/f30BZRcNTOq7Pzya7XMqLJ/4rNzyCGHHHL415EjMCMMwHdfuAnHzmeo23Y+thN3oq7dSVtrHWPG/Ix58+Z9yh0/f0ixJN1/3UTSG8WyoIK8U6rTx965/+/sXP4eJruDy/94J8Y8GwA9zT6W/G0L8UiSUVPzOeW6SYgps1Fw3XparroKkkmKbryRwHHH8v7779PSotQN0ul0MAqeCz9HUkwOJzGyDBsehrd/AVJccey94CkomDCs7w9+eIjfv7kXgMX1pfzp61PRqAQ2P7GS59s+Zkn9MfQbLWjlKOfzNCcl38K1o4i6qb9gyvFfQxAEGrf1svypvYT9cUS1wFFnjqH+hEy4dcLjofPnvyCwciUAllNOoeTW3yKY9EQiHYQjrQSCjezsXEGzeyNGguSrZQxHYO1RyWrUsRjqhIQ6IaPW2VEVTEFjqUKttqBWW1CpLSRlI+/tDfLqtn68ER3hhIF546v4wYn1jCr4fP+xcsghhxxyyBGYEQfg+md/jWPXs9TsuojCU7YgjN1Nc/MUpk65kRkzZnwh/Qzv6sP15B4QwHlpxpQUj0R4+lc/wtXWQtWUaZz7i1sQRGVmbt/n4fW7tpFMSNQeXcLCy2rSpg3XI4/S86c/gVpN2V/+guXkkzhw4ADLli2ju1tJMCfoBDaZN9FkaeIXR/1iuBLTugGevxz8HaA1w1l3w8Szh/X95c1t/PTF7SQkmWPH5XP/pTMwalXseXsTz21exeu1k+iwK5FC4+S9XMu92HpcJNpP4IQrfoU1v5CQL8byJ/fQtEOp+1Q23saiK+vSjsqyJOF+7HF6/vpXSCTQVFRQ9tfbMUyenNWXuBTn9UOv8+D2B/CE2nGqJKqMJk4omcp4i4N4tCOl3nTwaerNkSIhGzDo8tBqLKjV1jTxUastqFWD2kOPDRAklSkdUp5DDjnkkIOCHIEZYQC+8/SNOHc9z9j9F1N+2gbkqr00NEzn6Lk3M3HixC+kn7Is433lIMH1XQgakfxrJ6OrVPrb19rM07/8EYlYlHkXXs6cszNROQ1be3n7gR3IMkw7sZKjzx2bvl/HT36K7403ACj8yY9xXHUVsiyzY8cOli9fjtfrBcCn8bHdsZ2rF149nMQEeuHFb0DTKmV77vVKVWhVdlTOin09fOupzYTjSaaW5/HIlbNwmnWEeny88cQSXrQkWTt6EnG1BrUc5xye42vx1+jbXMLk2T9jyvFK8rrdqztY/cIBEjEJrUHNcReOZ/zsojQxC2/bRvuPbiDe3g4aDYU3/AjHFVcM80mJS3HeOPQGD25/kLZAGwAOvYMrJ17JBRMuQK/SkEj4SSR8qbWfRH8DiV3PkejYQEINCbWKROEYEoVjSApJ5Zykn3jcRzzhBznG5wMBtdqMKBoQRQ2CoEYQNIiielBb2S8KaoT0OerUfk1qf+pcQTOoPbBfjShoEcTUPVLnZNqpc1P3FwXNoPuPtD91XY545ZBDDv8mfOkIzD333MOf//xnurq6mDp1KnfddRezZ8/+1OueffZZLrroIs466yyWLFlyxM8b0YT0xK+w7XyR0U2XUH3GWqSy/Rw8MJsTTvgdY8YMTz//n4KclHE9sYvIPg+iUU3Bt6aiKTACsGP5u7x7/50Iosj5v7mN8poM0dqzpoMPnlDMOHPPHsP0k6tS90vS/fvb8DytFDq0XXABxb++EUGtJpFIsGnTJlauXEkopFSt7jB2cPSCo7ly9pXZHUsmlORwH92hbFcdA19/FCxFWadtafFw1WMb8ITijMo38cRVs6lwGJFlmT3vbebZTat4a1wNrc5iAKrlQ1zHPTja+kh2n8iJ3/gZ1vxCvN0h3n9sN92NPgDGzixk/kUT0JsU0pT0+ei88df431WS1ZkXLqTk9/+D2j7cJ2WAyDy04yFa/a0A2HV2rph4BRfVXIRRYxz+QXTvViKW9i1VtlVamHUtHPsjMOWnT5OkKKv2NXHf8m10el0Y1WGKLQmOHq1japkapzFOMhnIEKTBZCnhJ5EIIH9uJOiLgpgmWgPl0RQymVkGb2e1EVKO09ntzDapa8QR7jf82OBrhh8j+/7pY4zQD3HY/iPr72HaqX4JpJ6ZdUzpl4B4+PcfdGzwNcOPMfz+qfccfCzTJ3Hka4adN/IxQdQgijpUoh5R1CmLSp/ap8vsE3WIoh5RzE7LkEMOn4YvFYF57rnnuPzyy7n//vuZM2cOd9xxBy+88AL79u2jsLDwsNc1NTUxb948Ro8ejcPh+F8TmOsf/SWOnS9R2X4JY89aRbKogX17j2bx4j9SWlr6KXf890KKJul9aDvxtgAqh57Cb01FZdEiyzJv3X07e1avwOzM5/I/3onBkvlAt7zXwpqXDgKw8NIa6uZl3sP9xBN03/YHkGVM8+ZRdsffUJkVR9lwOMzKlSv5eN3HIEOSJPm1+Xxz8TcVX5nB2PM6vPItiPnBXAznPQZVc7NOOdQb4PJ/rKfdG6bAouPxb8ymrlTpZ8jtZ+njS3hFF+OjsZOJarSo5ARn8jJnRF+hd30p9fN+wpTjT0aWZDa93cyGpU3IkozJpmPRlbVU1CjZdGVZxvPMM/Tc9gfkeBzRasV+ycU4LrsMtSM74y5AQkqwtGEpD2x/IE1kbDpbmsiYNKbhH0breiWHzID6pDUrCtTc74A+M/ZJSeblzW3c/u5+unyZ+lBjC80sri/lrPoyKhwjECUgmYymiY0khZHkBLIUR5YTg9rxVDuR1ZbkGHK6rRxT2vFB7dQ1qXvKqXtKqbaUur/STgxqD5wfT+1X2jnk8K9CEFSDyIz2sORHFLVHQIr0iCrdkHtoU/fWIaqy751TCr+a+FIRmDlz5jBr1izuvvtuACRJoqKigu9+97v8/Oc/H/GaZDLJcccdx1VXXcWqVavwer3/ewXm4Z/j2LmEsu6LmXDOChLOZnbvms8ll/wFxwiT338ayUCMnnu3kXRH0JSZKbhuCqJORSwc4qlf/BBPZzuVk+tZ/NNfo9FmSMbaVw6x+Z1mBAFOvnYSY6ZnSKF/2TLaf/wT5HAY3YQJVNx/H5qSkvTxnp4eHnzhQRK9SnVmlV7FmaeeyZQpU7LNM30HlTDr3j1K/aSTfgdzvpkVgtzti3DFI+vZ2+XHolPz4OUzmTvGmT6+Z9kWnv94OW+Pq6GxQCFa5XKzosYc6kH0nMwJV/0Ya34B3Y0+3nt0F/09Ss2iqcdXcNTi0ahTkVqRPXvo+OlPiR5QyJug12M791ycV30DTVnZsLFNSAnebHyTB7Y9QItfcWr+RCIjy3DoA4XIdG5V9hmdcOwNMPNq0GTCtKOJJMv39vLq1naW7e0hlsj42MyosnNWfSmnTS7BaR5CDL8ikGU5Q4KySM7AIjPgV6S0lUVGVsZxSPtIj33SNcqzpBGOoWwPO8agtjTyNcOOMeQekrJWHp7Z/6nHjvz9B1/zycdARsrcO32M1PaQY+lrhrzj0LHJGjuG3V+WJYXcJqNIUpSkpKyVJTKo/eVQGAVBmyFNKv0QZUirkKMh+1WDSdEwsjRoySJLmeMqlQ5B0OZSLvwv8KUhMLFYDKPRyIsvvsjixYvT+6+44gq8Xi+vvvrqiNf95je/Yfv27bzyyitceeWVn0pgotEo0Wgmd4jP56OioiI9ALIs8YOHfoF9+2sUeS6i7uvvksjrZMeORfy/6/6OwfDlSB0f7wvTe99WpGAC/QQ7zssnIqgEepoaeObXPyERi1JeO4nFP70JnVH5dS/LMiue3sfu1R2IaoHTr5+aViwAwjt20vqtb5Hs60NdWEjF/fehr6tLH5ckidvfup3uLd2YE4pCU15ezqmnnkrZYDIQDcDr34OdLynbk86FM+7MStHfH45z7RMbWd/oRqsS+fuF9Zw6OUOYwp4AS594lVdVQVaNm0JEq0eQJb7Ga5wVfAHX2nJmnnADkxaeSCIm8dFLB9n1oZJ3xl5i4sSr6iioUEK7ZUnCv2wZrgcfIrJjh/IAlYq800/DcfXV6MePHza+CSnBW41v8cD2B2j2NQOQp8vjijqFyJi12eUGkGXY/apiSnMpZAlrOSz4GUy9eFjW4v5wnHd2drFkaztrG1zp+VElChw3Lp+z6ss4aWIRRm1OVs/h/y5kWUKSYlnEJpkcTHAGEaCs/RGSQ66TklGS0vBrpWRkCIFSFllOfNGvDzACsckoUFn7B8iU2oRWk49Wl49Wm49OW4BWm49G4/yvM8N9aQhMR0cHZWVlrFmzhrlzM2aHn/70p6xcuZJ169YNu2b16tVceOGFbN26lfz8/CMiMDfffDO33HLLsP0DAyBJMX704K9xbHsNp/8i6i5YimTqY9vWk/nBD+5GFL88UmO0xUffQzuQ4xLGmUXYzx2HIAi07dnJK3/8LbFwiKLRYznnF7dgtCrFByVJ5t2HdnJoSy8anYqzfjiNourMBx9vb6f1m98keuAggtFI2e1/wbJwYfq4LMv8fePfWb1mNTXeGtSy8g8zbdo0Fi1ahDllekKWYd0D8O6vQEpAQY0Sap0/Ln2vSDzJ95/dwju7uhEE+Ob8MVy/cCwmXeafcO/KbbywahnvjB3HwaIKAIrlDq7jHpy7u1AHT+bEq3+INb+Aph19fPDEHiXcWiUw58zR1J9YiZgKt5ZlmdC6dbgefIjgmjXpZ5gXLsR53bUYp00bNsYDRObB7Q/S5GsCwKq1csXEK7i45uLhRCaZgG3/hBV/UMouADjHKcnw6s4alvQPFEXq9W0dvLq1gx3t/en9Bo2KkyYWsbi+jHnj8tGovjx/eznk8FWHJCVGUIViCglKDleLkoMIUWbfIMKUjH7i/uSg+6WVq88VAhqNHa02m9hkltS2rgCtxo4gqD79ll9yfGUJjN/vZ8qUKdx7772ceqoSofJ5KDDJZIif3v8b7NuWkhe+gIkXvYKs97Nzx9l8//t/OdLX+Y8hvNuF68ndIJNV9LG74SAv/f4mwn4fjrIKvn7jrVgcioNpMi7xxj3baNvrQW/ScPaPp+MoyZhGkn4/7d//PsE1a0EUKfrVL3FcksnIK8syd265k6e2PMUk9ySqgsozdTod8+fPZ/bs2ajVKRLS8jE8f4WS2VZrgbPvg9pMNuOkJHPTqzt5ep1irimy6vj5qTUsri9LS6sRX4g3n1jCa7KPD8dNJaQzIMgSJ/IWi/ufx72mjDmn/oBJC08kEoiz/Km9NG5TikOWjrOx6MparM5s5Sy8cxeuhx5SHH1Tf6bGmTNxXnctpmOPHSbrJqUkbze9zf3b7s8iMpfXXc7FtRdj0WYn8iMeUXLlrLodwqmq4CX1cNS3YcKpWT4yg3GwJ8BrW9tZsrWDFncovd9h0nLa5BIWTytleqU9JzvnkMNXFIqpNf4JytInE6JEwk8s1jdkcfHZ0j6IaLWOQcTGmSY4Q4mPRmP/0voIfWkIzGc1IW3dupVp06ahUmVYpCQpH6Aoiuzbt++IIoaGDkAi4eeX99+CbfObmOLnMemyF0AdZd/uS/n29cOVmy8DAus68b6imC3s547DNEuJ4nG1t/Li724k4HZhLSjivBt/h61YMdPEIgle/dsWepr9w4o/AsjxOJ233EL/i4oZyHHFFRT+9CcIqfGWZZmHdjzEvVvvJS+cx0zPTCwRZRJ3Op2ccsopjBuXUlv83UqodfNHyvYx34fjb0qbVWRZ5r3d3fxu6Z70pD290sbNZ05kSrkt3af9a3bwwgfv8t7osewtUUhTgdzN1dJ9FG7rQBc/iZOu/QFmh5M9azpZ9fwBEtEkWr2KoxaPofbokrRvzACijY24H3kE75JXIa44oepqanBeew3Wk09GUGdLskkpyTtN73D/9vtp7FeyDFu0Fi6ru4xLay8dTmQiPlh7D6y9G2IBZZ9KB+NOVHLmjD9lWPXrgTHZ2url1a0dvLG9I53lF6DcbuCs+lIW15cxrsgy7NoccsjhvwuynCQe9xAdIDTRXmLx1DrmIhbrJRbrIxrrJR738FkUIEFQodE4U8QmQ3SyFl0BOm0+arXtP/rj6ktDYEBx4p09ezZ33XUXoBCSyspKrr/++mFOvJFIhIMHD2btu/HGG/H7/fz9739n/PjxaLVaPg1DByAWc/Pr+2/FseldDPK5TLzinyDINBz8Ftde9+PP8jr/UfS/04R/eSuI4LxiIoYJim9Lf083L/7PjXi7OjHZ7Hz9V7eSX1kNMKz44xnfnYo1P6NUyLKM68GH6P3b3wAwL1pE2Z//hGjMRMzs7NvJjatv5JD3ENWBaqb3T0eIK3/A48eP5+STT8bpdCp1ht6/WZnIAaqPVUKtzQXpe0XiSf6xupF7lh8kFEsiCHD+jAp+csoE8lOOrZFAmLeeWMIbcTcrx9cT0CvK0UL5Pc52PUv/mjLmnvldJi44AV9fmPcf3U1XgxJurTdrmHRcGZPml2HKy3aUjXd3437scTzPPYecChvXVFTgvPpq8s5ejDgk4iopJXm3+V3u33Y/Df0NQIrI1F7GJXWXYNUO+WcK9imKzM6XoG9/Zr9aD+NOgknnKGvt8GinRFJi9cE+XtvawTu7ugjGMoUt60qsnFVfypn1pZTkfTn8s3LIIYcvLyQpQTzuTik3A8SmL2t7YFHIzpFDEDQpkpNtttINNWNpC1CrLf9rsvOlIjDPPfccV1xxBQ888ACzZ8/mjjvu4Pnnn2fv3r0UFRVx+eWXU1ZWxm233Tbi9UdiQhqKoQMQjfbym/t+R9H6Fej1pzH+0ucAaG/9GZdfcd1neZ3/KGRZxvPCfkKbexC0IgXXTUFbrvw6D3o9vPg/v6avpQm9ycw5v7iFknFK2v/BxR+1ehULLq1h3Mzs/C2+N9+k4+e/QI7F0E+cSPl996IZFNYeTUa5d+u9PLbrMcSkyAz/DCo8FciyjEql4qijjuK4445Twq53vQJLvgPxIFhK4fwnoGJW1vO6+iP84a09LNmq1ECy6NR8/4RxXHF0ddoP5MC6Xbz43tu8Xz2KXWWjAbDLLq6SHqBoXStm8QROvO77mGwOdqxsZ9v7rfjdSgizqBYYP6uIqYsqs+oqASS9Xtz//CeeJ54kmUrkp8rPx3HF5dgvvBCVJVvxSEpJ3mt+j/u33c+h/kNKfzUWLq27lEvrLh1OZGQZenbDzpeVCtjuhswxjRHGnwwTz1EUGs1wQhKOJXlvTzevbW1nxb5eEpLyLyYIMGeUg7Pqy/japBLyjJph1+aQQw45fBZIUpxY3JVScrLNVtEhZCeR6P/0Gw6CIGgzxEZXgFbjVNYjEB+Vyjwi2flSERiAu+++O53Irr6+njvvvJM5c+YAsGDBAqqrq3nsscdGvPbzIDCRSAe/ve93lK7bgM66gNEXvgxAb8+tXHjhxZ/1df6jkJMSfY/tInrAi2jWUPitqahTvh/hgJ9X/nAznQf2odHpWfzTX1M5aSoAfneEdx/eRVeD8gdYN6+UeeePQzPI3BLavIW273yHpMeDuqREiVCakF37aHvvdm786EYa+xuxxCycEDkB0aUQDqPRyMyZM5k5cybWaKcSat23H0QNnHIbzLpmmIPrpmY3N7+2O+3YOqbAxE1nTGT+eEW1iYYivPXkq7wZ6mHlhHr6DQq5mCev4Oyu5wisLeKYc77DxAUnIEsyDVv72LasJa3IAJRNsFN/QgVVE53p2koAUiiE96WXcT36CImOTgBEsxn7xRfjuPwy1PmZhHUAkizxbvO7PLDtAQ56FWXQorFwSd0lXFp7KXm6vBE+MBm6tiukbufL4G3OHNOaFV+ZiWfDmEVZ4dgD8ARjLN3RyWtbO1jf5M5cqhJZMKGAxdPKOL6mEL3mq++sl0MOOXy5IUnRlLlqKMFJmbFSZq1otJdkMvCZ7i2K+ix/nQE/nWjURF3ddV8eAvOfxlACEw638rv7bqVyzS70xXMoP+dVkkkVoeCfsnxzvqyQogl6799OvDOIOt9A/jWTUdsU80csEubVP/+Olp3bUGk0nP79nzF21lHKdUmJ9W80suntZpCVUOSTr5mIsyyjUMRaWmj9f98k1tiIaDJRdsffMB97bNbzI4kI92y9h8d3PY4sy9Qka5jZP5OwT8nTIooidXV1zJ42iYqNv0fYk/JtmnIBnH4HaLMTuiUlmRc3tfKnt/fhCip+ICfUFnLjaXVU5yvmloOb9vDSW0v5oLKK7eVjkAURq+zlisTDlK5uIs+4iBOuuR5rvkJ8uhr62baslUObe9Lhy7YiI1MXVTDhqOIs4ibH4/QvXYrr4YeJHVQUFkGrJe/cc3BefTXa8vLs8Zcl3m9+n/u23ZcmMmaNmUtqL+GyustGJjKgkJmOLYoqs2sJ9LdmjumsMOFriplp9EJQDzeNtnlCvLatg1e3dLCv25/eb9GpOXlSMYvry5g7xolKzDn/5pBDDl8sksnIIPXmcGYshfgkk8HD3icYlDjrzKYcgRkYgFCokf+571bGfNSIvmoyRWcsJR7TIcu3p6OdvuxI+mL03LuVpDeKYFBjP2ccxsmKYpCIxXjj73/i0MaPEUSRU779Q+qOzYRJt+518/4juwn5Yqg0IseeP466eaVp+S7Z30/bd79HaP16EEWKf30j9osuGtaHrT1bufGjG2n2NSPIAmdZz6LSU0l7a3v6nJKSEuY4/Uza+UfUxKBokmJScg53vu4Px7lz2QEeX9NEQpLRqkSuPnZUOuw6Go7w9lOv87avg5W1U3EbbADMltdwTssz+FdZGTX5OOpPOZ2yCXUIgoDPFWbH8jZ2r+4gFlH8SnQmNZOOLWPygnJMtozfiyxJBFaswPXAg4S3bVN2qlRYTz0V57XXDFOjJFliWcsy7tt2Hwc8BwAwaUycUn0KiyoXMadkDlrVYXy0ZBnaNymqzO4lmXBsAH0e1JyumJlGzx9Wcwpgb5ePJVs6eG1rOx39mcy/BRYdZ0wpZfG0UiaX5eUimXLIIYcvPZLJULaiE80QHbe7g2OOeTRHYAYGIBA8wB/v/y1jP+zGMH4czlPfJhIxYdDfwfHHH/9Fd/eIkXBHcD2zl3ir8mvcNLuYvNNHI2pVSMkk79x3B7tXLQdg0VXfov7k09LXhnwxlj2+m5ZdillizPRCFl46AV3Kr0KOxej8zc30v/IKAI4rr6TwJz9ORygNIJwIc9eWu3hq91PIyBQZi7hh/A1EG6Ps2LGDZFIhDSa9lhnJTcyMr8WqlmDW1TDvh1l1hQZwsMfPb9/Yw4f7ewEotOj4xdcyYdeHtu7j5TdeZ3l5OVsrxyMJImbZz8WxR6lYfhBfk5WCqlHUn3w6tfPmo9HpiUUS7Pmok+3LW/H1pfxkVALjZhYxdVEFBZUZvxdZlglt2IDroYcJrlqV3m+afxz5112HcUi1ckmW+KDlA+7bdh/7PRnnXbPGzLHlx7KochHHlh07cs0lAEmCtg0ZZSbQlTlmsCsh6RPPhurjhiXLkySZjc0elmxt580dnXhDmVT/o/NNnJmKZBpQsnLIIYccvkr40vnA/KcxdAD8/j38+YHfMmGlH8PkUvJOWEYwmIfTcRfHHHPMF93dzwQ5KeF7rwX/ylaQQV1gwHFhDdoyM7Ik8cFjD7L1HaUa9bwLL2f24vPSv8plSWbr+618vOQQkiRjceo56eqJFI9WTCCyLON64EF677gDAMO0aRT98pcYJk8a1o/N3Zv59Ue/TqfmP3fcuXy77tvs27GPDRs24PMpPikiErUcYA5bqFD7EI66Do7+HhizyzfIssyyPT3cunQ3za7hYdexaIx3nn6Nd1ytrKybQq9RKVMwTd7IRa4niG+V6W+wojWYmbTwJOpP/Bq24hIkSaZxWy/blrXSeTDjkFY23sbURRVUT87P8pOJ7N6N6+GH8b39jkI0AMP06TivvQbzggVZCockS6zrXMeylmUsa1lGX7gvfUwrajm69GiOrzyeBRULsOuHF51UbiJBy1rFZ2b3qxDsyRwzOqH2zBSZmQdiNpmMJSQ+3N/Lkq3tvL+nm0g8kzNiaoWNxfWlnD6llALLV7OMQQ455PDfhxyBGTIAPt8Obn/wt9StSKCf4cQ8fzk+Xz4V5fcwc+bML7q7/xIih7x4nttH0hcDlUDeKdWYjykDAdY8/xQfv6xEWs0681yOvfjKrIm3u9HHu//Yia8vgigKzDlrNNNOrExP5L4336TjVzcihxUfl7yzz6bgBz9AU5RdfDOcCHPn5jt5as9TAJSYSrjl6FuYXTSbvXv3sm7dOlpaWtLnl9DNHLYyUdOBZu51SoHElFloANGEEnZ99weZsOvzZpTzk5NrKLDoaNxxgJdefY0Py4rZVDWBpKDGIAdZyPssiCxH3B6hb4+NZFTDqPoZTDv5dKqnTkcQRbqbfGxb1srBTT3IqUifvEIDU4+voGZuCRpdhiDEmptxPfIo/S+/jDyQS2bcOJzXXYv11FOH5ZKRZIntvdv5oOUDlrUsSxM7AFEQmVE0g0WVi1hUuYhiU/HIH6qUVPLq7HwZ9rwGIVfmmKkQ6s5UzEyVRw0jM4Fognd3dbFkawerD/SSej1EAY4Zm8/iVBkDiz4XyZRDDjl8eZEjMEMGoL9/C3996LdMXq7FMNeI/ugP8XqKGT/+fiZPnvxFd/dfRjIYx/PSASK7lYlON86G4/wJqCxaNrz+Mh8+9QgAE+Yey/FXfTNdegAgGk6w4um9HNyo/OKvqLVzwjcmYrQqPhzxri56//Y3+l99DQDBaCT/umtxXHkloj47emZD1wZu+ugm2gJtAJw3/jxumHkDJo2Jzs5O1q9fz/bt29PmJSMhZrKdmdomrMdcpRSGHJLFttsX4Q9v7eWVLYqviEWn5nuLlLBrpATvPbeU9zobWV4zhW5Lxiw1Rd7CCdK7VO7bj2unnYhbj624hPqTTmPi/BPQm8343RF2rFD8ZKIhpXaKzqhm4rGlTF5Qjtmeeb94Tw+eJ57A88yzSEHF8UxTVobj6quwnXPOsLEARU064D3AspZlfNDyAXvde7OOT3ROTJOZ0bbRh/lwE9D0oaLM7HkdwoNyN5iLYeJiRZkpnw1DSmH0+qO8sV0pY7C11Zver1OLzB3jZGaVnRlVDuorbBi0uWimHHLI4cuDHIEZMgAe7wb+/vBvmfKBDeNxAprZH+HqK2fatIczmWW/opBlmeD6LvrfaECOS4gmDfbzxmOocbB92Tu8/9A9yLKE3mxh/mVXM3H+ooxJSZaV7LbP7icRlzBYtZx4ZR0VdYOKQW7fTvfvbyO8dSsAmtJSCn/yYyynnJKl6oTiIe7YfAfP7H0GgFJTKb895rfMKVHC5YPBIJs3bx5iXkpSy0HmaA9SMe9ChDn/b1gG203NHm5+bVc67Hp0gYmbTq9jwYRCAoEA65av5cWWVjYVl9JiL0qHbefLPSziXWZ3ryO0RcTXbEat01M3byH1J59GQdUoYpEEe9d2sf2DVvp7ByKqBMbMKKT+hAoKqzL/PEmfD88/n8H9xBMk3YofkcrhwH7RRVhOOhHd+PGHdaBt87ellZktPVsylYuBUXmj0mRmonPiyPdIxqFhpUJm9r4OkUG5GaxlULc4RWZmDgtbb+oL8urWDl7d2k5DX7bnv1oUqCu1MqPKzowqOzOrHBTnDSdkOeSQQw7/KeQIzJABcHvWctc/fsuUZSWYT4wi1n9MT081xxz9KJWVlV90dz8XxHtCuJ/ZS7xTmaRMc0uwfW00Xc0HeO+Bu+htaQKgYuIUTrz2O9hLMpWm3R1B3nl4J+4O5drpJ1cy+8zRqFIJ5mRZxrf0TXr+8hcSXYrDqWH6dIp+8Yth/jHrO9dz05qbaA8oyskFEy7gRzN+lHZoTSaT7N27l/Xr1tE81LykPcDEeWegOerarNBrSZJ5cVMbf3pnbzr9/qKaQn59uhJ2nUwm2btjN2+u3cz7JiP7SiqIqJWJWC3HmcMajg+vwLS5G8++PKS4ivLaSdSffBpjZ81FEFU0be9j27JWOg54M30am0f9okqqp+ani0dKkQjel1/G/Y9HiLdnIonUpSVYFizAvGABxjlzhmX5HUBfuI8VrStY1rKMjzs/JiFlqucWGYvSZGZ60XTUI1WhTcSgYbliZtq7FGKZ8GryKlLKzDlQOi2LzMiyzO5OH+sa3Gxq9rCx2U23Lzrs9mU2g0Jmqu1Mr7RTU2xBnSs4mUMOOfyHkCMwQwbA5VrF3Y/+lqnLRmM51Q+T1tHZOZYTT3iCoqKiT7/hVwRyXKL/7UYCHynZbtVFRpwX1SDm69i0dAlrX3yGRCyKSqPhqHMuZNaZ56BSKz4RiViS1S8eZNeHyqRcNMrKSVdPzCpDIIXDuB55BNfD//hE/5hgPMhfN/6V5/c/D0CZuYxbj7mVWcXZ2Xk7OztZv24dO7ZvS2efNRJihqaBaXPn4zj22qxkb75InDvfP8Bjg8Kur5o3iuuPH4s5Ve26p6eHD9//iCW+fnaUldNtzpiXquUGFknvMX73dnw7TMR8Wsx2B1NOOJUpJ5yCyWant8XP1mUtHNzQg5TqkzVfz5TjK6g9ugStPlXrKZHA99bb+N58k+DatciRTGizYDBgOvpozAvmY54/PyvD8WD4Y35Wta1iWcsyVrWvIpwIp4/ZdDbml8/nhKoTmFs6F51qBEIUj8ChZYoys++tTF0mAHu1ospMPBuKpwxTZmRZpt0bZlOzJ73s6fSlfWcGYNKqqK+0MaPKwcwqO/WVNqw5P5occsjh34QcgRkyAH19y7nnsVuZ/EEt9jNcSBM20N5Ww5lnPo3NZvuiu/u5I7zPjeeF/UiBOKhFbKeNwnRUCf093bz/8D00b98CgLO8khOv+y5lE2rT1x7a3MMHT+4lFk6gNahZeGkNY2dkT8Dxri56/vpXfK+9Dgz4x1yH48orsnxC1nas5TdrfkNnUMl6e3zF8Vxadykzi2ZmmUqCwSCbN21kw5pV+CIZRcImBBhVVsioGYuoHjMu/cd8sCfArW/sZuWgsOufnlLDWfWl6bIEkUiEres2s2THbj52OjlYWE5CVCZeoxzkOJZzdNda5A0hAh1GRJWG8UcdQ/3Jp1M6voagN8aOlW3sWtVONKj0SWtQUzevlCkLy7OKZErhMMF16wisWEFgxcq0SjUA/aRJmFPqjH5i3YhmomgyyscdH/N+y/usaF2BN+pNHzOoDcwrm8eiykUcV37c8OKSAPEwHHhPITP734Z4puo1jjEKkak7S8nLI46sqASiCba2eNMKzdYWL/5oIuscQYAJRZa0SjOzykG53ZDLP5NDDjl8LsgRmCED0Nv7Hvc+fiuTP6jHfnYn0pjNtDRP5sILn8Fg+L9ZLC/pj+F+YT/R/Yrzp77Wgf3r4xGNavauXsHyJx4m7FN8KaaeeCrzLroCvUnxP/H1hXnvkV3p9Py1x5Qw58zRw4olhrdtU/xjUkngRvKPCcQC3L7pdl7c/2L6ulpHLZfVXcYp1aegGZS0LZlMsm/3LjaseJNmVwiJ7Ik23+lk1OjRjBo1iqqqKj5uCfDbNzJh106TlrPqy/j6jHLqSpU/fFmWaTzUwLIVH/MmEnvKK/EOypw7Sd7K8ZFVFK47gO+AGTkpUlg9hvqTT6PmmONA0LDv4y62LWvF2608RxAFxkwvYOqiCopHZWfhlWWZ6N69BFaswL98BZHt27OOqwsLMc+fj3nhAkxHHZVVRHMACSnBlp4t6fDsrmCGEKlFNXNK5rCochELKxaSbxieV4dYEA68q5iZDrwLiYw6hM4KZdOhfBaUzVT8ZkbIzQNKxuT93f4slWagsvhgFFh0KcdgZZlYmodWnTM75ZBDDp8dOQIzZAB6et7m/sdvofbDueSf24hUuZ3Gxmlc9Y3nEQ/za/T/AmRJJrCmg/63GiEpI5o1mOeVYZpVTEwK8+HTj7Jz+XsAmOwOjr/yOsbNOQZBEEgmJda/3sjmd5QyBCqNyMR5pUw/uSo7m60s43tjKT23357xj5kxQ/GPmTQxfV6Dt4Gn9zzNa4deI5JUJtR8Qz4XTriQ8yach0OfnRcmGvLTsvwxGreupjHuoJNCIPtXfnFxMRVVVRwIGnl2T5iuYHZF53NnlHNWfWm66nV/fz8ff/gxS5pb2VpaRJOtDFlQPn+n3Mvx0nIm795IeItAIqRBbzIz6fiTmHri18grKKJ5p4uty1pp35eJCCqsslA9JZ/KOicFVZa0r8wAEn19BFZ+SGDFCoIffYQUyhAAQavFeNQcLAsXKqam0tLhn6Ess9u1O01mBqpkAwgI1BfWp/1myi3lw64nGlAUmV2vwKEPspWZAdhHKUSmfJayLpo8YmkDgB5fJE1mNjZ72NXRTzyZ/ZWgU4tMLbcxo9rOjEqF1NhNn15FPocccsghR2CGDEBX9+s89NjvGLfmWIrO24tUuofGQ3O45tp/ftFd/Y8g1hHA/exeEj2Kj4WgETFOL8R8TBldvQd576F78HQqvi+jZ8xm0VXfStcYat/v4eMlh9JqjEotUjevlOknV2aFG0vhMK5/PILr4YcVfxBBIG/x4mH+Md6IlxcPvMgze5+hJ6SEcOtUOk4ffTqX1F7COPuQqLB4GDY9RvjDu2kK6WikgibVGHqS2X/YgiBgthfQJVv5uFdNR8JIEhVqUWBhTSHnTi/n+JpCtGqRRCLBru07eXvtZlZaDewtqSSkUjLXquQ4s1nHvJ51GFZ3EO41gCAwetpMpp18OlVTpuHqCLLt/Vb2b+hGGjR5600aKuocVE50UFnnTIekp8coFiO0foNialq+PMsJGEA3YQLmhQuwLFiAfvLkYVmQARr6G5SIpuZl7HTtzDo2wT5BITNVixhnGzfcrJNMKFWz2zdC20YlG3DffoZBpYOSqSlCM0NZ51UM86MBiMSTbG/rT5EaxUHYMyg78ABGF5iYmYp0ml5lZ0yBKWd2yiGHHIYhR2CGDEBH5xIefeR3VG1cROkFW5ELD9LcuICrrv7HF93V/xjkhERoWy+B1e3pSCUA3Xg7xjmFbN35LutffREpmUCj0zPvwsuoP+V0RFGFLMu07fWwYWljOputqBaoO7qU6adUZfmDxLu66Ln9r/he/2T/mLgU592md3ly95Pscu1K759bMpdL6y5lXtk8RGGQOhYLwoZ/wEd3QMhFACNNltk05i+isR/c7kzlZuXBIn6VlYNhI52SlT7ZRJ5RlzYxTSy1IggCHR0drFq+htd9/eypLKXdWJK+RaXcxKLIR1Ss3UL4oA4kAXtJKfUnnUbd/EVISS1N2/to2eWidY87XXtpAPkVZionOqma6KBodF46qgsUZSV28CD+lN9MeMuWdOZfUEK0zfPnY16wANMxR6MyZ4eXA3QFu9Lh2Zu6N5GUM8+vsFSklZkpBVOyx3Iwwl6lRlPbxhSx2ZCdc2YA5qKU2SlFaEqnDQt5H3ivhr4gm5o8aV+aQ73DC7fZjBpFnUn50Uwpz8tV2M4hhxxyBGboALS0v8jT//gdZdtOoeyi9eBoprXxVK68+u4vuqv/cciyTKzRh391O5E9LgZSkqgLDQh1BlZ89BRt+3cAUDR6HCdedz1Fo8akr23f52HD0qZ0uLGoEqg9uoTpJ1dlRSyFt26l67bbiGxTfEAOlz9GlmW29m7lyd1PsqxlGZKsTOLV1mourb2UM8ackV1TKBqA9Q/AR3dCROkDhXV4Z91Ak3oMjU1NNDY2pnPNDCCBSHfSTKdkpUuykF9YxNdnVnJWfRkFFh2hUIjN6zbx+o49bCrJY59zFHFBUVAMcpDjpDVM27sB1vtIRtWodTrqjl1I/cmnU1BZTTIp0d3oo2WXi5Zdbnpb/FnP1+hVVNQo6kxFnQOrM9v3KuHxEFy1SlFnVq1G8g+6XqPBNGsm5gULMS9cgLaiYtjn6o14WdGmhGev7VhLNJkJkc435HN8xfEsrFzIlIIpWLWf8KUgy+BuyCg0bRugeydI2c68CCIU1mVMT2UzIX/8iA7CnmCMzS2KyWlTs4dtrV6iCSnrHI1KYGJpXiofjWJ2KrTmctLkkMN/G3IEZsgAHGx9lpf+8XsKdp1G1cWrIa+DjpZzuezKP33RXf1CkXCFCazpILixGzmq/HoXDGpCRSFWbnia/kAPgigy47TFHP31i9EMUlDa9yuKTPs+L6AkgKuZW8z0U6rJK1AmZ1mS8C1dSs9fbifR3Q0o/jGFP74BQ339MBNCe6Cdf+75Jy8feJlAXAkJtmqtnDf+PC6suTA7BX/EBx/fB2vvgWgqsVvxFDj6u8jjT8EdjNPY2JheQqFs34+YrKJLstAtW6morOLMo2pZVFeERhQ4sP8Ay1etY5kqwe7yKlwaZ/q6ifJOjuvbiO2DPSQ8igOys7ySyslTqZo8jYq6SWgNRkK+GK173Aqh2e0mEsg2q9iLjVROdFI50UHpOBvqQeqDHI8T2rQ5FdW0glhTU9a12jFjMC+Yj2XhQmUch5Q1CMVDrG5fzbKWZXzY9mF6LAdQaalkYv5EJjqVpdZZi0nzCcUf42Ho3JYhNG2bwNc2/DxdXspBeBCpMTmHnRZLSOzu9LGxyZ32pen1D89JU+EwpE1OM6vsjC+yoBJzZqcccvi/jByBGTIAe5v/yesP/x7b/jMYfekHYOqjp/MyLrrk5i+6q18KSJEEwY3dBNZ0kHSnIlZEcGt72XToLdyxTqwFRRz/jesYPX12FvHoOOBlw9JG2vYqZgdBFJhwVDEzT60ir0BRTqRQSPGP+cc/0vlSdOPHk3fO2eSdeSZqR7YDbzAeZMnBJTy1+6l0eQK1oObE6hO5rPYyJhcMKv8Q9sDaexUyM5DUTaWDsYuUDLUTTkHWWenp6UmTmaamJqLR7AkzIqvxCFaKC/OZPr6CSaNKEUWRXdt2sbS1ld1V+ew3j0k7/dplF8dHNjBm7Vrk/QkGHIxFlYqScTVUTa6nako9xWPGIwgiva3+tDrT1dDP4P8itUakdLydyokOqiY6ySvMDkuONjYSWLGSwIoVhDZtgkRGDRHz8jAfe6wSpn3sPFR52VFR8WSc9V3r08rMwHgOhoDAqLxRTMqfRJ2zjonOidQ4atCrP0EB8XUMMjtthPbNMCiPTRqO0alopwEH4UnDHIRlWabNE06bnDY1e9nb5WPoN41Fp6a+0sbMKgczUjlpBvL/5JBDDv83kCMwQwZgW9OTvPvgbVgbFjPm8rcQ9D48vd/i6xf8+Ivu6pcKsiQT2ePCv7qDWGMmXb072c2evrW0h/ZjLyun/qSvUXfcInSDQoA7D/WzcWkjLbsVXxRBFBg/u4iZp1ZjK1LOi3d20nvnXfiWLkWOKRl1UauxLFxA3tnnYD7u2Cw1ISklWdm2kid3P8nG7o3p/fUF9VxadymLKhdlstWG3LDuAdj5IrgOZl5KpYXRC5UMtRNOBYMdSZLo7OykqamJXfsO0NHWqhRSHAGiKGKz2VCr1Lj8QVqsWvYXVNJtzMenNyIIMrOlbZzkacW45QB9h7wMjpbSGU1UTJxM1eRpVE6ux15SSjSUoG2vh5bdCqEJerPJlDVfT2Wdos6UTbCnk+eBUtIguHo1/hUrCK78kGT/oLICKhXG6dMVMrNwIdpR1cNUrv5oP7tcu9jt2s3Ovp3scu3KCtNO30pQMdY2NkupGWcfh1Z1mGiiAQfhtg0Z85PrwPDzVDoorc/2p8krH+Yg7I/E2ZLKSbOp2cOWFg/BWPZnJApQU2xNZw0eV2RmVL4JozZHanLI4auKHIEZMgDrDz3Oqgf/gKX1XEZf8QqCJkLA91POWvz/vuiufmkRaw8Q+Kid0LZeSEXahJJ+9vdvpMG/DbRiygfkNPIrqtLXdTX0s/HNJpp3KgUmBQHGpYiMvVgxUyT7+/G9+Sbel14msjMTSaMqyMd21lnknXMOutHZRQ73uPbw1J6neLPxzXT6/RJTCRfXXMw548/J+HXIsjKR7n5VWXoHFVIU1TB6gZLQreZ0MCrKTyKRoLWtnTU7DrD9UAdutxszEcxCFJVw+D93GQjoDPQbTPgMJgR9nFJtFxPECJWeIM1bOwh5slUJS34BVZOnUTWlnspJUzFYrLg7grTsctOy20XHQS9SIvNMUSVQMjYvRWicOMsy0TtyMkl469a0qSl64GDWszRVlZnyBjNmIGhHJh994T52u3azy7WLXX272Nm3E1fENew8jahhvH28QmhSxGaMbczIJQ9AUccGHIQHSM2A39JgmItTZqeZGQdhbbZJKynJ7O3ysTllctrY5KHdO4LiAxRb9YwuMDEqX1nGFCjEptxuyJVFyCGHLzlyBGbIAKza9ygbHv4j1s4LqL7ynwiiRDTyW772tUu+6K5+6ZH0xwh83Enw406koOLHkZDjdIUa6A430x1pwTa2lPpTTmPMzKNQpRSU7iYfG5c20rQjNREKMG6mQmQcpZnJKbJ/P/0vv0L/a6+liyQCGOrryTvnbKxf+1pWBE5fuI9n9z7L8/uexxNVzFYGtYGzx57NJbWXUGkdUtuqZ2+GzPRkop0QVDDqOIXM1J6RlcytPxxn6fZOXtzYwr7WHixiFKsQwamJMy5PwKaKEvb3E48PDxcejJhGRKWTsKslTOEI/c09EA4jxKMIyQQCUFg9hqop9VRNnkZpTS2ypKJjv5eWXS6ad7nw9UWy7mnM0yq+M3UOKmod6E2ZRICxtjYCyxUyE1q/HnlQ/0SzGdPcuegnT0ZfW4u+rha1c7h/CigmnZ5QD7tcCpkZIDeDswMPQKfSUeOoSZOaSc5JVFmrUIkjRBTJMrgOZaKd2jZA106Qh6hfggiFE7NJjXPcMAfhrv5MTprtbV4a+oK4g7HDfh4alUClw8iofDOjC0yMThGcUQUmCsy6XFh3Djl8CZAjMEMG4N1d/2DXP/6EzXMhlZc9lTrr7yw6/vQvtJ9fJchxidDWHgIftRPvynaIDSX89ESa6RddFMwZz8TTTsRkswPQ2+Jnw9JGGrf1KScLMHZ6ITO/Vo2zLENM5FiMwIcf4n3pZQIffgjJlFOxXo/15JPIO/scjLNnIaQmsUgiwpuNb/Lk7ic56D2YurXA/PL5XFZ3GbOKZw2fkPoOwO4lCpnp2pHZL4hQPS+lzJwBlkx9rEO9AV7e3MbLm9vp7M+QibEFJhZPzufoMi39Pe0cbGrmkMdLLBFFG0ugT3wyuRFlCaJhxFgUMRZFiEfRSElKK6sYM3kq1VOnU1g1iv6+SFqdad/nIRHLRO8IAhRWW9POwIVV1nQivWQgSHDtGoXQrFxJ0jVcUVEXFqKvrUVXV5siNXVoyspGnMhlWaYj2JE2O+3uU0jNUAdhAKPaSK2zNm16mpQ/iQpLxcgEIRbKdhBu3wS+9uHnpR2EZ2X8aYyOYad5QzEa+oI09gZp7AvS0BegoTdIkytIJC4Nv28KFp2aUSnVZnS+mVGDCI4p52eTQw7/MeQIzJABWLLtYZof+TP2yPmUXaAkr9NpH2HevPlfcE+/epBlmXhbgMhBD9GDXqJNvrSJaQCBuIdIXoz8WaMpnjcJtUVLb6ufjW820bClN33emGkF1M4rpXyCHdWg1POJ3l76X3sN78uvEDt0KL1fU15O3tmLsS1ejKasLN2fjzs/5sndT7KqfVX63HH2cSyqXMS8snlMck4argi4DsGe12DXEujcOuiAAFXHZJQZq5IXJinJrDnUx0ub2nhrZ1c6DFgU4NhxBZw7o5yT6ooQ5SRNzc2sONDIxp4O+kSJmKTHFImSFw5gjYQwR0c2faQhSYjxKGopiS0vj/ySUooqq7DanYQ8Et62CL1NYXzdcURJg5AquaAzqamsdVA50UlFnSNd+kGWJCI7dxJct47onj1Edu8h1tzMMC9ZQLRa0dfUoK+tQVdbi762Dt2Y0cMinQAkWaLF16KYnlLmpz3uPVlFKQdg0VrSDsKT8icx0TmRElPJyKSmv31Qsr2N0LHl8A7CA4SmeLKybSoYMeGeJMl0+iIpYhPgUIrgNPYFafOEhhWxHIwiq04hNgXmNKkZXWCm3G5I197KIYccPh/kCExqADo7OiguKeHZzQ/R+48/4xS+TtE5zyFJInbbM8ycOfOL7upXHnJcItrsI3zAjW9bG6JXTk+oA0iYk1gnlWKY4CSoV7PxvVYObelJ56DRGdWMmpLP6OmFVNTa0yHFsiwT2bYN78uv4Fu6FCmYSogmCJjmHkXeOediOWFROkFeY39julzB0MrOR5cezbyyeRxTdsywsgV4mmD3a4o6075p0AEBKuYoDsC1ZyjOpihVsd/c3smLm9rY2JxJ+mbRqzl9SinH1xQyq9qOzaglGAyyv6GBD5raWB920WjJo1NdhDkawRoOkhcJYg0HKAq7sIeDqCOHVwkOBwERQVKnF1FSI8hqjCYD9nwrBWU2CsttGI1G9Ho9er0enSwjtLYi7T9AdN9eorv3ED1wIMvslL6/Votu/Pi06UlfW4tuwgTEEeqIJaUkjf2NWaRmr3svMWm4aceus1OXX8ck56S0CarQOELl7mR8BAfhg8PPA9BawDFKITNDF0vxiOQmmkjS4grR0BekIUVwGlNt1yeYpNSiYpLK+NtkTFMFlpxJKocc/hXkCExqANpamiirqOLR9Q8SfOSvFBrPxHn6S8TjWspKn2fy5MmffrMcPhOkSILuj/fRs2YvKpeATVOQfYIA2goLUpGRZleUPQe9BH2ZSVOjV1E9OZ+x0wupmOhAo1XIjBQO43/vPbwvvUxo3br0+aLFgvX007Cdcw76SZMQBIH+aD8ftHzAqvZVfNzxMf64f9DjBSblT2Je2Tzmlc1jonNitjrjbYE9rytmptbMcwDll37dYqg7E2yKr01jX5CXN7fx0qY2Ovqz/VXGF5mZVe1g9igHM6sdlNkMuN1udjU08l5rK9uTPpqshXRpMvltBFnCFvZTGz7AOL+bYn8ctTtKv9+HLIjIKhWySgUqNbKoGnFC/iwQBCFNavQ6HTpZRhOJoPb5UbldqLq6UAeCaGMxtPEYmlhcaScSmMrKMNbWoq+tUUhNbS1qu33YM+JSnEPeQ4qDsGsnu/p2ccBzgIScGHZugaEgK/JpYv7E4YQTlKiz9s0ps9NG6N0P/a2kWfFI0BiVuk/OkchN6YhJ+PpDcRpdQRp6M6SmoU8hOZ9kkjLr1GknYkWxUUxT1flGLHrNYa/LIYf/duQITGoAGhsOUD1qLPevuZ/Eo/dSaj+RvFOWEIkYGTf2JcaPH/9Fd/X/NCKBALvffZ/OVbswR60UGqqwaIZMcGoRCg24ZYGDnUG6vLH0FKTWilRNymfM9AKqJjnT4cSxtjb6X34F75JXSHR0pm+lGzeOvHPOIe/MM9IOqnEpzvbe7axqW8Xq9tXs8+zLerxNZ+OYsmMUdab0GOz6Qf3rb8+QmZa1ZE2OpdMVM1PdWeAYhSTJrG1w8cb2TtY3ukZMn19mMzCr2s6sUQ5mVzsY5TTS29vDpkONLO9qZrc6SaO5DK8qe4wMcpC65EGmhX1MdceRd3bQ2rpX6Y2oQhZVqIwmCkaNwVE1GmtxKUlBS2+7F1dPPz5PgEQijiQmkMU4kpBAFhPwCRFWRwp1XCE0mlgcbTyGThQxmMwY7TZMhYUYS0vR2O2o1WpUKlV6kQSJrnAXLf4WGgONNPgaaAm0kCSJJEiZBYlCcyF1+XXU5temc9XkDaoonkY8At5mJZPw0MXbAvInqFsqXUq5GTNcwckrhyEmSEmS6fJFUn42GYLT2Bek1f3JJqlCiy6L1Aw4Elc6jDmTVA7/9cgRmNQAHNi3k7HjJ3L3irsQnniCipKjMS16g1AwjylTXqGqqurTb5bD/xqyJNG0fQtb33mDru37KdJXUmiootg4Cr1ozD5ZIxIyaujuj9EXjONPygQkEDUilXUOxkwvpHpKPjqDGlmSCH38Md6XX8H/3nvIA8np1GrMC+ZjO+fcYbllekI9fNT+0aeqM8eWHcvE/ImZGkK+Ttj7hkJmmj/KngxLpqbIzGJwKmUXXIEoG5o8bGhys7HJzc4OH8khs5rdqGFGlYPZo+zMqnYwodBIV0c7axsbWe1pYb9eR6OhipCYHVJsk91MijUxKxShtiuKZ+te+vqzHV/NDqeSTG9yPRUTpxLyq+k44MXVHsDVHsTdEcyQmhShGWhrzQK6PAGNEVQ6CdRJknKcSCRCOBwmEokQix3etPLvgoycJjWIoFKp0Kq16DQ69Bo9GrUmiyRlLaKAKhlBHQ+givtRRftRRT2oIm5lkeOokFCRTC2D2qKIylyAylqMKq8Ula0Mlb0claMKVV45Kq0u61lxSUnMN6DWNPWF0g7FfYHDj5tKHIiSSjkQF2TCwAtzJqkc/kuQIzCpAdixbSOTpszgjvf+jvbp56mqnob+uLfw+53MPep1ioqKPv1mOXyu8HZ3se29N9m5/D0iAT8WjZMS02hGFU/FmnQgJkZwwAQCSRlfUlYIDWAeZaV8VjGj6gvQmzQkfT4lt8zLrxDZvj19rSo/H8uiRZjmzMY4axbqgoxJKy7F2dazjdXtq0dUZ+w6O0eXHT1cnQn0ZJSZplXZZKZockaZyR+XNvEEowm2tHhZ3+RmQ6ObLa2eYSYIvUZkWkVGoakp1NHV3srKpoNsCHXTYLLRqKtK12gaQInUwZRIJ7MCCSqb/HTu2kYgml2QMb+ymsqJUyioHk1+RRX24jKCPhlXW4C+9oBCbNoCBDzDU/oDqDQijhITznIz+WVm7KVGTE4VqJJpYhPyegk0NRHo6CDY00PI6yUaCiEhIImisqgG1mowGpD1eiSNFlmjRhIEkpJEMplML/8nIQhpMU8eWIZ8qyqbwqA2iIKAKChJIpW2kC6tMJjcDLRH2vdlbv+7n3Mk20e67/M6599575G2B0i2Wq1Oq6Kf1D7Sc8URzK//KnIEJjUA2zevYfK0udz+5u2YnnmDUTUTUM99D6+3iEXHv4nNZvuiu/pfi3gsyr6PPmTru0vpbsiEQdu0hVQWTqTYOQar2okqICDHRpb+k7KiziTNGgxVVgqn5GMalUeit4X+V5bQ/+qrWbllALSjR2OcPQvT7OGEpjvYzUcdH7G6fTVrO9ZmhQgLCEzOn5zxnRlQZ4J9sHep4gDcsDI7p4m5SMk2WzZdWZdOB4NNef+kxM72fjY0uVnfqKTQ94ayHWhVosDEUiuzqh2KQuNU09vVxvK2PWxJ+Gg0FdGirkyXNwAQ5CSjpFbqQ25meiXy9/fQ1LCZeDLbPwdBwFZYjLOiivyKKvIrKsmvqMKQV0h/T5S+tmBKrVGWxGE+A1OeFme5GWeZsuSXm7EVGdNRZVIsRvTAgXT0U2TPHiL79iEPqU2lvLAK3ejR6OsUfxrdhBq0E8aD2ZwmNIlEgv5wP/vc+zjoPkiDu4EGbwPesBdRFpUFMdNObatkFVaNFZvGhlVjxaK2YFabMaqMGEQDKlRZxClriYVJxiIk41GSiTjJZIJkUiIpQxKRJCpkcqafHP57IYriv0R8RtoXjUY58cQTcwRm87oPmTb7WP74+h+xPbucUVMqUM36AJerjDNOfwfDCFEUOfxnIcsy3Q0Hadi8nubtW+k8uA9ZGpTrRBSpHlNPdfVUCm3VGBImIu0BJFcE4TCOBrJKQF1kRFdiRAp2E2/ZTXjHGqI7Nw37qXs4QjNYnVnVvor9nv1Z19l19rTvzNGlRyvqTMgN+95UlJlDy0EaIReMc1yK1KSW4kmg1iFJMod6A2mFZsNhMs2OKTAxq9rBzGo7Y6zQ19PEyt597BYlGowVdKlKss7XyDHGx5uZGQxS7xZwtrrp7D6Ay9OKPILDq6hSYS8pSxEbhdQ4y6pAlYenI6SoNW0KqRmaZC9zDwF7sQlnuYn8MgvOchPOMjNGqxZBEJCTSWLNLUT27M4iNkmPZ8T7acrK0qRmIF+NurAw6xdmOBGmI9BBm7+NtkCbsh7UjgwlcENg0pgoN5dTbinPrFPtUnPpyCUUJAn8neBuQHIdIulqJOlqQPK0InuaIREChEGjLGQrK5ZSsFWBrRI5tcZWiWwtB40SWSfLMvGEREd/mFZ3mBZ3kDZ3iBZPiDZ3GHcolqXTDP7NrRIE8s1anGYtTqMWh1mL06TFkV40OI06zPps/57BX/H/qfa/+zlHsn2k+/6T53ze9x74EZBIJEZsf9rxwe1/F6LRKH/4wx9yBGbDmmXMnHs8v3/lNvKfX8uoWU7E+g/p7anivPPeQ6UaIVtoDl8ooqEgrbt20LxjC83bt+LpzPbt0BoMVEycQtWkeiqqJiF7tLh2ugi3+tFGk1hERWofCYJehaiPI4e6ibXuJnZgC8n+dohnHG4PR2iOSJ0pV3xn6px1iPEIdG1XwrIHFk/T8E6JGiWHyWBS4xwLoki7N8zGJjfrG91saHKzv3t40rhiq55ZoxzMqMyjyhCnz3uQtf1N7NcaOGioxitkR/AY5SB10Ram+XxMC2oZHbaQDCbp87XR3rUXj79z2DMA1BotjvKKlFpThbOikrzCcmIRA672QWpNW4BYZGTTj8GiSSs1A2qNvcSIWqNClmUSPT1Edu8msmdPmtjE20dIageoHI50WLeupgbd2HFoykqzsjYPQJZlXBEXbf42Wv2twwhOT6hnxGcMQECgyFR0WILj0DuGS/qyDIHukR2KXQ2ZwqOHg7Us5UQ8xKHYPgp0mXf0ReI0ZUVHZRyKQ7EjM8FpVSIFFh1FVh2FFr2ytuoptOgosuopSrVtRk3ODycHIEOG/rckaKS2z+fjiiuuyBGYjz98hznHnsTvX7iVghe2Un2sEXHiGnq6xnHRxW9/0d3M4Qjg6+2hafsWmndspWXnNiJ+X9Zxi7MglYq/HnvpBDr2RenY3E2sM4hFJWAVBawqAZNK4LBfvUIMKdBJousgkq8DKdSHFOpDDrnRjqoaRmjiUpytPVvTvjND1RmH3pHJO1N6DDa9TTkQdEHH5gyhadsIYffw/ujyoGxaNqmxFOMJxlJ1gNysb3Kzo62fxBAVyqpXM7PawbRyK6WaEO7QPjZHuzmot3NQO5awkO00LcgSBfRSGu+hIhJgdABqIxaqA2YSoTC9/S20d+2jPzzyJK81GHGmlJr8iiqc5ZXozcWEfCpc7YG0Kaq/JzRS3jwEUcBWaCC/3JxlijLbFafVZH8/kT17FdNTSrGJNjSmMzUPhZiXh6asFG1ZGZrSMjRlpWjKytKLymIZdk00GaU90D5MtRlYj5SYbzAMagPllnLKzGVpclNhqUirN8OqesuyYnocidy4D0Gkf+QHDcBclIqWGoHg6K2pR8h0+6J09Ifp8UXo8Ufp9kXo9kXp8Ufp8UXo9kXwhD45Y/RgaFUihVZdFrEpSLcz5CfPkCM6OfzryPnApAZg1XuvMe+EM/ifZ26m+OV9VC4SEMevo6dzEhdd8uoX3c0cPiNkSaKnqYGm7Vto2bGF9r27SQ6RMgdqCxVUTyQcyKdph4fOQ/2IMphVYBUFHEY1TqMakyyjOoxaACDLEnLYmyIzfUjBPkSTiG5sCcZpEzAdPQNNUSFdwS4+ak+pM51rCQ5SdERBZFL+JKbkT2GCYwI1jhrG5I1Bo9IoE5mnKUVoUsSmcyskRjB3WMsyvjRlM6CknrBoYkurhw0pH5pNzZ5hv7p1apGpFTbqS80UawJ4knvYK0TZp8qnQ1NMQBg+oUOK2Mi9lCV6qQiHGBMQqA2ZqQyYiIV99Hqbae/ahz82AgEDDNY88ssr0z42tpIKVGonPpecVmv62gJEgyNL0TqjepBaozgPO0vNaHQqpEiE6P79KWKjKDbx5haSXu9hP8v052GxDCI0CtFRl6YIT1kZotWaNfnKsow74qYt0Ea7v30YuekKdo1oihuMQkNhlmIzuJ1vyB8+2YfchyE3DRAaXhIiC8Z8JRIuHQJeAeZChfSYi8DozMp3E00k6fVH6fZF6fUrBCdDdCL0+KJ0+yPD/LM+CVq1OIjkKMSm0KqjaGCdUnRyRCeHkfClIzD33HMPf/7zn+nq6mLq1KncddddzJ49e8RzX375ZX7/+99z8OBB4vE448aN44YbbuCyyy474ucNDMCKd19i/onncOsTN1L2WgsVp0YQR22it3MGF17y/Gd9jRy+ZIhHI7Tv2UXTjq20bN9Cb0tT1nG1RktZ7URKxk1GECvpblbTdciPNEi1UAEWtUCJQ0e+WYNFLaJNSOCPIX9CojIAWUogx32IBhlNoQXd2DJU5XkcooWPQutZ1rucA94Dw65Ti2pG542mxlHDBLtCaiY4Jii5TZJx6NkzyPS0GXr3jJDDRICCCVA2M01sEvm17O4Js77RzcZUCPfQTLKiALUlimPwlBIjVl2YBl8j+0IuWpISnSoLXerCTyA2SQrlPkrjLipCIcYGVNSGLFT6dYTDbno8TXT0HiAY9454vdmZn6XWmGylSLKN/p44fSnfGm9XKOszGvzKeQUG8suy1RqrU48gCiQDQeId7cTb24l3dBBv71Daqe2hDt0jQTSb0ZRmqzaZ7VJUNlvWpBtLxugMdo6o3rT6W7PI7EjQq/SKcjOU4JjLKbOUYVAP8dMLewcRmsZschP8ZFOYMoYqpdzCYFJjSa0H7zMXgtacjqKLxBWikyY1aVXnf0d0ssxWloy5ajD5sRrUOaLzX4QvFYF57rnnuPzyy7n//vuZM2cOd9xxBy+88AL79u2jsHB42vAVK1bg8XioqalBq9XyxhtvcMMNN7B06VJOPvnkI3rmwAB88OYLLDz169z82M+pfr2bsjP7ESu24eqax/kXP/5ZXiOHrwCCXg8tO7bSvGMrTdu3EPRkT1jGPBsVdVOwl9ag0lXT3yPS3ejD7x6ueKh1KkrLzZQUGXBatVi0KoT+ILFWF0lvFDmpRRA+2YdK0AjINg1efYAurYtGoYWdib00Cm10a1yEVNnPLTYVU2OvYbxjPDWOGmrsNZRZyhAHCh62b1KyzrZvTmWdHdppPRRPSas0ctl0GpKFbGjysL5JITUt7uHRP4UWXSodvpnR+UZKzSoSCQ8dkSYORjy0SAKdKgudqiKCn6DYFMq9lMbcVIXCjPWrmRC0UhFQEQ730u1uost1iGBiBPPIkIgoR2kFGkMhiZgFd1cUV5ufvvYgYd/IOVQ0OhW2IiNmuw6LQ4/ZoU+tdVjsesV5WBSQQqEUsWlPr2Pt7QrR6egg2df3iZ8ngGg0Kmap0rIsJSdtorLb05OtLMv0R/uHqTYD7c5gJ9InJdcD8g35w1SbgXWBsSCTpwgg6h9Eag4pa1+HEvYf6FbMVp+iFmUPrHEQqRlCbszFmX2mAlArTs6DiU63L2WqSpmvegeZsfrDR050dGoxi9gUDiI9A/sLrXqs+hzR+b+ALxWBmTNnDrNmzeLuu+8GQJIkKioq+O53v8vPf/7zI7rH9OnTOe2007j11luP6PyBAVj2+rMcf/oF3PSPGxiz1EfJuV2oSnbj6TmBr1/4wGd5jRy+YpBlGVdbC83bt9K8Ywttu3cSj2YTBntJKfmV1eQVliOq84lH8/C5dPS0BImPYFoy2XQUjbJSVG2lsMqCXR0ltmUPkd2NxFrdSGEZwZCPaMpH0OchCJ8cWhvTJnDr/bSpuhRSo+2jS+OiO7XExQQmjYnx9vFZSs1Y21j04f5sf5r2TSP7ThjsSvh22Qwon0mPpY51PapU+Labfd3+EX1TADSqgVo/SrbYUrMKJDeuRCttSS9tskinKo9OVTFBYbjzLKSIjdRLWcxDVSjCGL+GOn8exUGJcKiHLncDvd7mEYnNQETUgGJjyS9DUDmJhoy4UxFR7s4gUuKTv5JElYDZrsNszxCbwW2LQ49Wr0YKh4l3dqZUm0HqTYrwJHp7P/E5AILBkFJsFFKjzVJwylA5nelJNi7F6Qp00RpoHU5w/G1ZSRZHglbUUmouHdE0VW4px6TJToBIMgGhPoXMDJAaf1emPXj9aU7GQ2FwDCE6QwiPpVhpG+wgCGmi0z3MPydb4fksREevEQ/jhJwhPAWWHNH5suNLQ2BisRhGo5EXX3yRxYsXp/dfccUVeL1eXn31k/1QZFnmgw8+4Mwzz2TJkiWceOKJI54XjUaJRjMJuHw+HxUVFbz76lOceOYl/PrB7zL27QQlFzSgKjhIv+tMzjnvb0f6Gjn8H0AyEadj/940oek+dBB5hF+/okqNvbQMS34pGl0hyaSNQL8ZX68GhuT6EARwlJnTpKYgX0DXupvwhvUEN2wm1tKLaHQiGvMRjAqxUdnLEA0OEHSf2me3up9OTV+a0LjU/XjU/Xg0fkx2K8WF5YwrGEeNvYYJ9vE4w/3ZDsJd2yE5gmphq0yrNKGCqRxUjeWgV0rX+jnUG6DJFfzEWj9WvTqL2KgEF36pE5foo1OlolPMo1NV8qnEpjzqpTIYY5xfS60/j6JglGCwi07XIdyB9hGJzeCIKEdZJXpLEWptAYm4gVB/HL87SsAdwe+OEPRGD0vQBkNnVCskx6HHYs8QG7NDj9muw2TTISTiGdNUxyCSk1JzEj09I1b4znpvne4wJiplnzo/HyHlo5Kl3vjbMo7GgTY6A50j1pIaDLvOjtPgxGlw4tA7cOqz2w69I709zNE4FkwRmgFSM3gZQnikzxBSK2pS5OZwys6gtdZEJJ6kx5dRdAaIjaLsZMiOL3LkfdBrFEWnyKKnIOWbo5CeAT8dReWx6HJE54vAl4bAdHR0UFZWxpo1a5g7d256/09/+lNWrlzJunXrRryuv7+fsrIyotEoKpWKe++9l6uuuuqwz7n55pu55ZZbhu1f+uLDfO3cq7nxgW8x7h0VxRfvRu1oJuS9iDPO+d2RvkYO/wcRCQToOriPvrYW+lqbcbW14GprJR4ZOeJEpdFgyS9FZyxElh2EgxaiYSuCmJf1JafRqyisslI0ykpBgQqr9xDy9vUE128gundvZoJT6zLExlyAuqhaITc6O7JsAOnIvjj9YhC3uh+32kdQH0G0aDHZLDjyCyjOL6ZUHUbj24HQvRGhYxP07WeYGUEQobAOSuqVQof2aiTbKLrUxRzya1IVmhVi09AbpKM/fNh5WhCgNM/A6AITpRYVOtFDSOjFr/bj0gl0qe10iCWEDkNsRDlJYdJFWdRLVTDOeJ+eCX4LhaEggUAnXX2HcIc7CSV8w64VBBGTw4HF4cTiyMfszMdsd6Ix2BBFMxImEjEDQW88RXCiBDwRoqFPn/wEQVHgFOVGlyI2mbbFoUejlkh2dQ0xT2X8cRJdXZ9OcLRaNCUl2QRnkIlKXVCAIIokpATdoe7hvjeptjfq/dR3Ggyj2phNbgyOrLZT70yTHqvOmjFdSRJEvMPJzUjKzkgRd58ErWUEojMC4TEVEJGEtB9Oty/jkzOU/Pg/A9ExaFTZTsiDzVgps1WRVYc5R3Q+V3zlCYwkSTQ0NBAIBFi2bBm33norS5YsYcGCBSOefzgF5vXnHuL086/hV/ddw7j3DJRctgV1Xiex0LWcevqRma9y+O+BLEn4XX30tTanSU1fazPu9jYSscOl2NeiNxchqJzEonnIsh1RlQ+CJf2lZnHoFUJTrCEv0oHZ20iy8RDRQweJHWpACgzP7yJozQhGJ5ry8ahLxqC2lyIYbSAaScYF5GAC4QhJDkBclSRhlFGb1Rh1SXT0IUabUAV2o4o0IgoeVIIbER/C4CKP+jywVyv5R+zV4BhFzFpFq1zIvkgeja5Imtg09AY+8ZewXiNS7TRRZlVh0PSTFD2EtAF8BujVO+gQSwkJphGvHSA25VEfVf4E430Gavxm8oNefIF2ulyN+KJ9hJMBkvLhzQ6CIGKy2TA787E48rE48zFYHai1VgSVBVkyEY8PJjkRAp4oUvLTv/bUOhWWtIqjG+SLk1JxTCKyu3dE81S8vZ14V5dCCD4JGk2K4IxsolIXFiKoVPhjfjoCHbgiLtwRN66wsh7cdkVcuMIu4iMlXPyk9xTU2PX2NOEZSnQGKzsOvSOTBDARg2DvCCpO1xDy0w2fErqeDUGJrvokh+TUOixa6AlknI8H/HQyZiyl/VmIjlYlkmfUkGfQYDNosBk15Bm0qbVm0FqLzZDZZ9Fr0qUgcsjgS0Ng/rcmpAFcc801tLa28s477xzR+QMD8NozD3DGhdfxi3uuZMIHNkquWIva3AeJH7LopOuP9DVy+C+HJCXx9fTQ19aCaxC5cXe0kYyP/OUvqnWoNPlIkh1BdCKonIgqJwhmVCoRZ7lZ8aWptmDRRjH4OqC1gVjjIWIHDxFtaPjEqBlVfj66sbVoq8ejLqlGyivAq4L+cIhIfxA5kEQfVmNPWDBKnyHjtCChUgUQcaGSelOkxo1qgOAIHqWNB0GFYo5KkRvZXk3AWE6zXMT+aD77vFJavWl2BYl/AglwmrRU2NRYdAHQ9BPThggaBTzmPDpUZYQ/jdiE/VQHkozx6qjzmxkVFUGMEkkGCcS8+IJ9eHxdhOL9hBN+otInTJCCgCnPhjlFcMwOJzqTHbU2D0EwI0lGYlE9of4kAY9CcsL+IyMBBqsWy4Cz8VBTlUWFOuQh0Tk8gire3k68s/Ow+W/SUKvRFBcPIjQFqB0OVHYHKoc91bajcjgQtVpkWSYQDwwnN2HXMPLjirjwf1bfGMCitQwjNsO2U2uzxqzka4oFhpivBpGbwfuCPZ9cZXwoVNoRCM5wwhPSOugJi1nEpncEXx1/9F/PSCsIYNVnCM5gkjNsn1GTJj95Rg069f/dJKxfGgIDihPv7NmzueuuuwBFXamsrOT6668/Yifeq666ioaGBlasWHFE5w8MwKtP38uZF3+Ln999KXUrSyj+xvuo9D40qt9w3PzLP8tr5JDDMEjJJN7uTlytLfS1NdPXqhAcT2c70mEmGkHUgaCQGUGVP4jYGNEZ1FgLDOQVGMkrMGA2SRgjLnSeNlRt+4k3NBBtaCDROXK2XADRakU3ZgzaMaPRjBpFf6mFJkuSpoSH7r4ufC436pCAI5GHI2HFnrCm2nnYkiNHGB32WfSnlRsVXkTBrbQHyI5RQOXIQ3SWIdmqcGnLaKWIvVEHO33GdObYbt/I6hYo9aBKrWrshghqrR9JHyZsEug3W+nSlRARD+9jkyf3Y0v6yYuHccSiOKMShSGRkoCesoCVyqQWnZwgKgcJxnz4wi68vi6CsX7CST+RRECpfH0YGPNsKYKTj8nmQGuwodJagRTJiSgkZ8AnJ/EpYfkAKrWIya7DkoqgGhxRZbJqMCR80Nc1ookq3tkJhyHUI0E0mVA5UsTGZs+0BwiP3aa0U9uiyUhCSqTJzFDCkyZBERfusNL+ND+dodCK2mzzld4xTNkZaNv1dtQISt6cEf1zurJJ0KclCBwKXd7IJixLJgIrpHXikc14ozL9oTjecJz+cBxvKI43HKM/NHg7Tn8ohjccP+IsyYeDQaMaQnI02FKqj3XI9uBzvgrmri8VgXnuuee44ooreOCBB5g9ezZ33HEHzz//PHv37qWoqIjLL7+csrIybrvtNgBuu+02Zs6cyZgxY4hGo7z55pv8/Oc/57777uOaa645omcODMCSx+/irMuv52d3XsCUNWMouPJVVNoIZtPtzJmz+DO/fA45HAmSiQTero6UKaoFV1szrtYWPF0dWXWesiDoEVT5CrER7QiiWVkEM4gm1BoN1nw9eQUGLDYNJgLogz3oXU2om/cQbzxIvLXtsCYIwWBAN2oU2rFjSFaW0F2g4aAtwjZtN3v7D9DkawJJThMaZW3FnsjDmcijWC6gQHJgi1swRXWI8pEXLxQII+JHFIIIBBCFAKIYQdSrEE06JJOJoM5Er8pMo2xlV8zEXn+CXa4gnvjhv+hNWoF8Uxy9PoSgjxAzQcBsocdYQmRoBM5hoJMj2KR+bPEg9ngERzRJfhiKQ1rK/CYqYkacCYmEHCIc9+GPuPH6ewhGPYSSAcIJPwl55PBuUBL6Kf44ToxWBxp9HqLKCoKZZCKj5PjdUYL90SOKctaZ1GkFZ7CpymTTYJCCaPw9JDsHHIt7SXo9JNwekm43CY+bpMcL/0ItG0GrTas3avsgwmO3Zys8KZVHsFoIJIJpU9UnmbLcEfen5swZCTadbZj5aiRlx6l3YkRUFJtPVHZS28nDk+oRoTUr5la9TVkbbEPa2cfiGis+THhlI56Ymv5IYhjJGYn4+MJxDlMC7oigEgVFyckyeWmHmLoU8pM3WPUxaFCr/jMFS/+dBEb9WS+44IIL6O3t5aabbqKrq4v6+nrefvttioqKAGhpackqxR0MBvn2t79NW1sbBoOBmpoannrqKS644ILP3NmBHAtiMo6g0iColF8nOt3nOyg55DAYKrUaZ3klzvJKJmRcv0jE43g62gaZohRy4+3uAjmCnGgjmWgb8Z5RwUjIbab7oEJqBNEMKZIjivMxTzuHvJPysOgTmBL96P2daHsa0TTtINl4EDkcVuoM7d4NgBmoB+o1GnTVVahGHUuwzE67U2CfM8gOo5td0VY6g+uH+UgIsoAlaUwrN46ElWIKKKeEQsmJI5GHJapHF9GgSojIGEhiIG1BkgEJCKSWFPJTyywGJlcTCElkdZKESiYoCrhR0ZUQ6I4l8Mdk/DENAQz4kQkgo0FGTT8aow+dKYHBFEXQRkloEkS1AmG9lqDOhFdjJyyaiQp6ulV6ulWAHhhBhBJkCauswZYQsSV0OKJOnBGZwpCa0qCBsqCJ0oSAJhkmkvQTiHjoD/QSiHoIR/z0t3TS03TosH8veosVizOfkionepNNITlqK7JsIpEwEQvrCPZLBDxRYuEE0WCCaDBAX+tw3ylQyjOYbA4sjlKMxToMYzXozRr0JmVtMKrRiXE08QDqiB8x4CGZIjZJt5ukx50mPEmPh4THgxwOI8diJLq7SXR3c0TTuyiistlQOewU2h2U2O2DFJ7RqBwzMkTI7iBhNeBJ+kf00xkgOQPHvFEvkizhjXrxRr0c6j/8+A7AoDYMV3asThyFM4b48zjIk0AVcn26sjOQWycWUBbfyLW7hkIDOFMLoiZFcIYQH7MN8rOJj6SzERJN+DDjkQy4kwa8ESlDcgYUn3A8pQjF6A/H8YTixBISSUnGFYwNS3B5JLDo1BniMwLJGez7kyZDBi16jfilUX2+UqUEXnz4L5x79Q386s+nM3nHLAqufAKA0pLnqK2d+QX3MoccFMSjEdztbYrTcFsL/T3dBNwuAm4XQY9rWLmEw0OdVm4QM0RHZ7Jhtlqx6rRYZMXfRttxEHXDdtT+vpFrRIkimvJytGNGI1WVEiix0WcT6DTGaNUFaU300BXsoiPQgS82PCJoAHpJhz1hxS5ZqdCWUaYqoVB0UpA0YY+psUQFTBHQRmWICsgJFZJkRMKMkif5f4cAcprcDF6HBIhrIK5OEtUkCGtihLRxfLok/XpwmTS4DRZ8OjtJleaInqWTI9iSfmyJIPZYFEckSWFYpCiopSxgoiyhxRaLEE/4CUS9+IK9BKJewgkf4WSAcCKAxMiKk95sweJwYrQ50RltqHV5iCoLkmwiETcSC+sJ9ksEPdGRsxh/AkSVkCY36XWqbUitdRoZTTKEOhZAE/WhCnhIegapOu7B2x4k3+H/Jj6xLxYLKrs9W+FxOFClTFxqh7IW8qz4TSIeIYwn6hmm5rjD2WauT6tGPqwfgohdZx9R2Rkcju7QWMlDxBiPIET7FTNV2KusI97h20OPfZYQ9MNBa/lUxQd9HjGNFT9m+jHglUy4kgY8URXeyFC1J0N8vKH4Z3JoHrF7anGIX0/Gpye9b5DvjyoRYdKoki+HAvNFYiDPhzqRQKXLSOsGQ94X1aUcchgGjU5P0eixFI0eO+yYLMuE/b40oQm4XfgH2p7UtquPaDAAJJAlL7LkzbpHIqz8UOwe2CGYEFRmhAnTUanN6PVmjGoNxmQCk9+LsbMJU18Lcksr8ZYWQKESRamlHsXXRl1YgKawBgqchG16+q1qXBboNERp0QVoVLnpCHfRHeqhU+5lN4cU9WVgjtamlpTqkW/Ip8RUTIneSQl6yhNqykIyRYEozv4gBp8fORRDwoQkm5ExI8lmJMxIsgkJM7JsQhIsyLKSZ8eMgHkkiiYDsdRyGESRCRAiKMiERZmQmCSsShJUJwloJHw68OrU9OoN9BmM+PVq/BonHZp8DpkhYSGdij899LKERTJhS5qxx5w4ognyIzKFIQ1lASNlUR35sTiaWJBQzIsv1Ecg4iGU9BPuDOBt7SB+GJOVzmTC7MjHaHWgMyr+OCq1BRk9clJHIqklEdMQj2qIhiQigTiJuISUlAn5YoQOk+l4JAiCBb3Zgd5Ug96mQV82hADpRTRE0SbCaOJ+1CEvYsCD7HFnmbTSao/XC8kkkt+P5Pen/+4+tR86HQ6Hg0K7PWPecthR2SehcqT2VdlJWI30G2TcmijumDftpzNU2XFH3Gl1xxVRlJ8DDC8HMhQqQYVJY8KitWDRWjBrzEpbb8FsKcKiHZt1zKw1Y9VYMKPCLElYpQTaaPDwxCe9Pag9YHKL+ZVlpOzcg6BlkOqT7rg2Q3YGiI/NBsUZ4pPU5RESLQQEE/2yCY9swJ0w0hfX4o3IaV+fjNkrQ34SkkwsISm5e/xHZpaTosOzhX9e+EoRGElWvinFRAJRqxAYWRbQ6UZ2/Mshhy8bBEHAaM3DaM2jsHr0Yc+Lx6IE3W6F0HgyZMfX20t/Tx8BTx8Rv1ch9XIQORlETnYjxSEehqzYknwgvwhBHINabUIratFLYIhEMPi9WPp7sQbd6A8qEVMDsKSWamAugEqFOj8fVeF4ks48wjYDvjw1fSZJITn6IAc1bpri3USkKH3hPvrCfewY6QUtYLQbKTUVU6wVKBUSlEheSmKdlAa9lPR3UeBuQZXyXZBldYroDBCbFNFR5SNpCkiIDuLkEZcsxCUjyaQeOalFTKrRSCIiArrU4hwgXUk1HNZHdvgv/CQyMUEmKkJEhIhKJKwWiKpVRFU2IqKdqAoiKoiIAt1GaDYLRFWQEC2Igg21XIpGiqNPJjHHRGxRLc6IFmdCxhQLEo378IX68EdciorT7ae3Yz/RZOgTi0xq9AYMFgsWqxmt3oRab0KtMSKqDQiiAdAhy3qSCS1SQkssqiEWVfH/27vbIDmu+t7j33P6aWZndndWEtaDLVkOuK4Jj46FHQO5eYEKSKgkJCSVuJzgAJUUYBI7rptgwsVOinLkPFQqD1CkQhXkVgXiQF0gCZWQIrJN4irjB2GTOARjylzbMdazdmdmZ6a7zzn/+6J7R7vS6tGSViv9P6qu7unpnTnTR9r56ZzTp/P5QJl7RGDYK0/66itogNlINrG5atnZlNC4clHoacWkkScNIxI3T5x3iYezxL2DyOzRXVr+4EEkz5E8x73wwnEHti+WRRGXdTpsXbMwbqcOPJ3Lx9vSmWLQipltBg5ljgNubkl31pGBpwwlXjzdonvc1sgTSW16VABqN9tMTq9nMnkZ7bS99Pl4gkmBdghMOkfb5UR59yRbgOZAfDXR5fy+ajmGiMP/tjcuecZANgXN6cMhaGYaNnWg0UHqVp9526ZHi65McDC0OOib7CubHChMFXyWdHsVHJg9tdayU7GqAszCaCfrHXFabXsf02g0jvdTSq06SZrR2bCRzoaNxzxGQmDQnRu33szt3cfB5/cyu2cfvYP7Gc4dIh/MEvwIKJFwiLI4RAnMQzUR8TQw3QJagMFGE8TRBKnNSCUic4F0MCDtzZHlA5rDeZpPfofMOeIQxv8D/B+LymUmmtiXbMKtnWLYaTI3FXGgLeOWnO8mh/h/ySwDBnx37mm+u9yHa0M0uYn1zXVsTKbYZFI2BGFTPmRj/yAb555jY38/bZFjh5CoWkQMQgPPNKFxKUWygdy8hKFZwzB0GPo2uWtRuAbOZYiLSSSmhWUSQ6Nu8YkwNMXQ9FQBqISq6edku3iSejmWFOwMG9qXE9qCM+Ds4dhiBSIJIJ4QHC6UFD7Hh4JSCsqQU/RzirkhRRhRhL0U4vChxInDi8NLWa3rfVEjIW01yJqTJI0JonSCKJnARk2MaSKSISHD+5SySCiLhHwQUY4CCPX4Hcfc3hPN8xKx0F6QNuMq6Fy5qEurFdPIIKGoQk/ZJx51SeYPEfX3I7N1t1bdpeUPHqzmW/Ief+AA/sAJ7updmwHWTk0tGqszQ7RmLfHMlePHfmqCUcMyTA39LDCfBnqRo+v79Ms+/aJPt+iOt3tFj17Zo1f06BfVMQBFKMatPqdrIp4Yh5yFANSeXs9UeuXhlp90inbSom1iphYCkPdMuoJmPji5rrByQFWhc9WyDANk9bJmuQPixtKurnXVuhuanK0+klUVYEJ9RUbkPDap+vG8j8myE0/jrtSFxlhLqzNDqzPDeo7urlpQ5iO6+/az79nd7H9uN4de2Et3/37mZw8y6h/C5V0kzAOB4Ocp/PzSnpiF31rjPqKZhRJgTYo1MXGwRD6QlI7EeTJXkL1wkMYzQ7KyYKP3bPGBN3pP4gORgOlMVyFn5nDI+X4dcp5OZtnXcnw/7Ob7Zg+7FpcnAdY1Yd1mZtIpNqYzbIxbbLQZ64Iw4wqmiyEzo3k6w1k6/YNMDw6SMIR8N1m+7NjeSh16AEIyQZmtZZC8hL5dx5ysZVY6HHBTHChb7B+12F80GIYWBU1SLA0MGZBiaADZonUKNIGGhYZZ/WOEwgAAIBZJREFU2GdIsWQCWTDjX8gWQyqQ+iO7yywQg83Gm2eCF0/IHX7ocFJUgSfUYUf6uIXgU+8nMZjYQmwhiSGKEBsjUUwgwpPgQ4zzMXkZU+QRRW4RsYSRYzhyzO8bjsd/L69dL5tJsojGmoTG5ir0ZK2ExoQltZ6UnNTV43lGc8SDQ0TdfXDowOHAc6ju1gqB0O1SdLvwzDMnPC+Lv7DNxARRu42tl6jdwrba2MnLsO1W9VyrDe0WZTMmzyzDzDCfCvOZ0Isdc1FBPwyq0FP06Jf9cfBZHIIWxvgM3ICBG7BnsOd4xTymyES00/bhLrDWJO3O4S6w8f6oSRtbtcyEwKTztF3BZDkkzfvH7wobzVVz97gR9EfVYOjF8rM3zHaVBZgqtFjvIa26k7xPNMAodRxJ1mDtZZex9rLLln1eRJifHbH/uX0cemEvs3v30jtwgPlDBxl2D1IM5ynzAa4cENwQJAccIATJCZJX1xkt+vKvvl2b9bLcm0IslshD0nOkh0akZckWV/BS53mzb5L4QGwMttXAt1OG7YjZduCFiZxnsx4vTBQcbM/x1OQc34qXGRdjqRqWWm0Mk0wmbWaSFp2oQcckdMQwE4RpVzJTjujkfTrDHjP9A0yXI6bLAVk5IOM5ZoDNy32OdOHjGMpshmG6hn7U4ZCZZr9MsdtN8nw5yROjCZ4vWuxnmgNhinkasMxYnojqC7MBtBPPRCJkiSGJ42oyxTgliyxpZGgYS2ahIYYsQOaFhocsLF03vNAIgYYXml7IQhWMEjn8/pGJiExEYrP6pJ2CwPFSSGUcgpcn4z/1RW1Sv6xULWgBkMIRcoccHFSPF54HhoDIBIEJhI3VHR4SsJdZ7BURNrFEqSWKwJpAZBxWCiKXY90Ak/eJhl2Y7xLme4TRABkMCIM+UhYQPCKOUHjCgRL27UdkDwRfdd2EqmVMxo/rNTBRLy+pP6udmBiHoHEQaq/DtreOg5C0JigaMUXdEjTIqEOQp5uUzMUFPTd/VBBa2O4X/brVzTOXzzF3jFaVk5FF2REBaAPt9GV1y0+byaRN2yZMYg93gXnHpHe0ixHh0AFgx2m///GsqgAjdRdSVHpMswozwcdE0YU7i6FSZ5sxhvZMk/bMFra+estxjw1BGPVLegf7zO2bpbd/lv7BOfqzXQZzXUb9PqN+j3I0oBgNED9EZASS1+sREMCAMwFnqS7hzWKqX0fHmWW4AA5CtD/hSt/iFd6ReE/iApE1mMTiU0OZGQbNwGzDs785Ym9jxFzLM9vq8t8TXZ6JjnEJaAIkBqbWjXdNxhPMRE06NqWDpROg4x0zZU4nH9AZdekMu3S8p1POMp0fZBq49KiTzJIvcG8zhukaetEMs3aafWEh7LR4Nm+xX6bY76bZX05xiEkcQjUu5+jxBFEUiDNPlApkEOIYn8UUcYZLUiS2EMdIUrWYSGwgsVgLaTA0gpB5aIQq7GRHhKApV9B2JW3naLtAywUmHLScoekMTWdpOksWLFkwJAFiAVtf4Lrw/28DGMyyd3U39Z8l52vx+qiTeQpKXy1HjSVd6NJrA5dUf/2mwZ7B/g6RUIWZ4EDCUQFHgkfE43OPH3lkb6ifdyA5JngmJDARHOvGAak6xlgLSRMTtzFpgklibBpjsgSTpkhq8VmESwxFAnkqjBJhkHgGiaMfl8zFOfMypO/79Nw8fd+j6xaWLt4EnPcMij7dwRzeeAJySlXghy9ukr/jWVUBBhFEBOsNpFWAkXByl0QqpV48aw0TUykTU2tYv3XZnvAxEaHMPYNuUQ8QLZify5k/1K9Cz6Eug+6i0JMPEMmrOXRCFXbGj+sQBEKwhsIaiiRd/o3Laml04TJaLG53inwgCYEYiAxYA0TgY6GMA6PY04tzuknOoCHMN0p6jYLdrcBTTaFIBW+pfoE3gEYDOkvH4E1GGR2b0SGqWnm8Z9oVzBRDpkd9ZoohnRDo+P3MlHt4mQ+8fPELLPMrbRRP04s6zNpp9ocpXvBTPF+02O0n2S9THBhOcWA4zQGZoksCBAzDqtvs2DVEHAckCeQx5DEciiN8EuPiBD8OPwZpZhA3x+FHYlN1H53g3j/NMKQZRkz4nAlf0iodrTIwVcB0bpjOY2bylDV5zLSDqTIwWTrifIR3Q0qfU4YCL+Xhbqxx15YniCMQsFiMsVgTHd62EUnSJI4z4ijF2hRrEywJhggkBolALHiLIcKaCIPBmqoBrwpcC9vVxzUc4/Eyc6MYYyGyUF+6fy5mTxGp/6nkYHuHO37P5KUuwQTECMEEvJUq6BiPM44Sh8NRUFLimMv7vIP3n8F3P2x1BZi6Gc/6CJMsBJhj/BJTSq0oYwxpIyZtxHQuOfHx3oVx0Bn2Cga9gmG3rNa9gsHciPm5PoPZKvR4P1oSbo4Veg6HH/CRxS83A6mrF6DNxPF/2YsQiVQtCkYQE3A24KynjKogVMTCKBVGaeD5LPBUFlEkLVw0gYsDZSK4KOAioYyFLI6YTGKmjaETAjNlyXQ5Yiafr1p3fMFM2M20+z4vC55tPpBYqm/OI8+jSZiPZ+hGHfqmTU+azEmTWd9g1mcccBlzoUlfmvRCk/6oWU8e2KQnE8zTwBCIT2J6O2sDNhFMDBJbQhLh4xifJBAbithSJCmzcVa1/iQGGhZpV8GIyBx1afqCcfhxRRV+nKdVBiYLmCws03lMJ0+YKSzTpTBVlDTzEclwHufmKfyIohgxHM1RhCG5H1GGEXkYUfghZRgte2sJG0UkjQnipEGUNLBRio1STJRhSDAmRaRqvQk+JoSY4GKcj5AyhlAHJZNiTYIxEdaYJaHH1u1NS8LRkY/H4chgLcSJJYktcWyI6vwYIVWXmARsvZjgscHX64DxHhOqliDjpWrBCYCxGBOBjWDR2owfW4xdPiJYsSAQEZGcoIGll5/6jMwna1UFGJGAiMf6GOL60gPR8S9KXQii2NKeyWjPnPjftIiQD1wVbBa18FTbh/cthJ9iWNZhZplwE44MOyNESpCyWlNtjye8MQa/5Es3ggBxgNhB8xRnrV8sIARbBZtRFHgmEZ6qW4dcLLioCj5lJNgYksiQ2UDDlDQpaEtO23imgmM67KHD86zBsUUcM8FXQ3aWjFVaXmkzRrbF0Ewwb5r0ZYKeNJgNDQ7VQagnE1XwKZv0y+qYhSDUlyoUFce96gowgo2lCjOJxccRoe7uKmNDEUfMJa1xyw+xRdqmDkN1CFomADXDkKbPmfB1+Ck97VJoF4ap3NIpEmbyiJlSmCo8k0VBYzAgGvUJYTQOOy4vcKFq/XGhWw1wDuXhVqFQ4KQ87iXuxhiitFEFojjFxhk2yjA2xdiUha4skQQJVSDyPia4CO8TjEnApNWaFOpQtLyFVHuc824gzSKSLCJJDUkMcSQk1hMbTywFkYyIfU5UDIncAJsPiEcDomEfM+pjB33soIft9zDeHRGA7JLHoxPdkf1FWFUBxgdBpCRaFGDM8UaGKaUuSMbUs822EmY2nHjQqSv8uEVnoXWnGLpqGXmK0aLtel0u7Ms9yMJEmouCTR1uRIpqW1y1zaLnpURYtL0QiEJe/7xD6gHRUP9vO0TE9VstM+TlhIb1cuR1K2UU8JEQIkFiQSLBRIK1Hms9kfHExpFYT2o9mfE0rKdp+jTNHBPGsxHHlcYxZTyT1pEaT2TkWI0oOJPUIWiCPlUImgsNZn2DXh1yeqFJv2jSzxdagibGz/Vlgh5NchKO1QFjIoHEEOIIiSMkMbjY0o0t3SRD4ubh1p/MIi0Liam7x2zVCrRIM4xo+hETvmTCVeGn5QKtUpgshFZpaZYxE6WlHSzTzjDhoOkDzdLTKBxZOQJX4MZBp8RJQRlKXFng84XH8+NQ5Ov1Qijycuw5eWwUE9WBKIozTJRhbVLdXLYOREESRGIkxHhXLZgEQ8qoTBjNp+NwBMmiLrDjdDo16qVzeFecWJLUkqSmCkJRILaBmJJYSvLhIXjg/x7zs7wYqyrAIAERh3ExJFXlWqNzwCilji9OI6bWNplae5xBwscgoRrLU4Wcer14e1EIKo8ViOrtZV9f6pn1xoFouZC0XCAqMGFUh6EcU/+M1KFI8Ig53DKQeHvC5n4YDyHiZBv+gxF8JEgUECsYEzA2YEzdvWGEyAYSE4hNIDXzJPRomMC08TQINEygiSetj4mMEJnqizAyAYyhtCmFSRmZjHlS5qXBwDSZlwa9ssl8WbUA9WkuCkALj6sgNCTjqCBkQWKDxBHEBpdYerGlG8eQpOPxPpJYmDBLB0MvBKBlxgJZ8aRSkIWSNJRkwZEGTxo8De/JnJB5oekMDUc1GLqMaLlqaTvDhAs0nafhPM28IC1LkrJAQjEOOi4UOF/iyrIOQyOc9MahyYUqZIf6+IUJYY8UxRlRkmLjBtammCjFmKrFZ0kLkU/wIYKQgEnwZUoxXDhucSiKMSZhWOhl1DUhiMeEpGqrBaw59V9ISil1sow1pM2YtBkfngLnNCwMai5Hnnzo6sBz/EBUDEqK+aJajxxFHigLQU5yOGgVjk4QiKTAhiHGDzFhVIUiKTBSjFuMgjjEBMLCsuhKFCsGW826t3wZWDLE6CyoymjNLDMmsM5WAWghMFWBKBDXgQprCcYQsHgT4U2EMzGFiSlNQkHKyCwsGQPTYEiDedOgT7NqTTJNnEnGP+9thIsjXJwQoqgKNZHBR5ZBZBhEBonSKuhEBqmfX1gkNdCsBkUvfm7JcYuauSJxZKEglZI0OLLgyLwjDYHMVZfMNzyHg9FCOCojWnVrUaP0NMqSRlGtE1cQFyPCoq4x5xeFJBkuDU1LWpaKo7rRjLFESUbwZ++u16sqwEgQJJTYkCB1F1IUn+KcBUoptQIWD2pudU6/61tEcEU4OuwMD4egcuTIByV5d0jRG5HPF+PusLIQyhJKb5cNQnLEGhYugV46ZlhkcauRO9wlJo6qRamastj4HCM5JpQYKSAUmLrLbWE9bjHCIQQEX13pQkAW1vUsMYsvy156XixOLO60h1wcvqHWwrQ1p31FdZ03xBjEWMQYgrEEE+GNHYcmZ2LKep2TUJqE3KTkJsGbGLcQkEyMsxZvY3wc46Jq7aMYF8eM4oT5uNr2SYaLY1yS1C1EFmkYaB0jPNmlISlZEoxKsuBJvSfzgcyHai4hZ2guajWacBETTmiUdYtRHY6yssT15oDPne6ZPK5VFWBAqn80PoaoujwwjidWuExKKXXuGGOqAZhZRGv6RQahMlAMHeWR3V71dj5fUvRz8vmcclDgckeZO3wZcIXgXYpzCd4J3oMLBi8L19Es855HrJf9fBwdlo5V/iooHQ5MUnfFmTDChgLrc0zIqVqhinq8UoHgCKbqbgs4gnF1aPL1c1VoWlgvTLUHgqmvQLMBjIANR8xhU3/AahocweBP6vOcDYKpWpvq8BSMPRyKWAhR1Tpg8XVICtbibdWyFGyEj6Jqf2TxUUQvipmNInwU46Nq4LWrB2D7bNF23KQ4RsvcmbCqAowgddNnDHE12XkS640clVLqVBljSNKIJI1eRFPD0USEEARfBFwZcIXHlaEOPYu2y0A5zHHzOeWwoBzkuFGBG5W43FVL4XFFfbwTvBNcAO8NQaqw5InxZGCXxojFN0o/5jngpC7KOvFnDiU25JgwHI9JgpxAdeWbmIJASTAFwZR4SoJxeOMWrasAhanCmDEBIwFDtViRaglVeLICJhhMsJhgIJhqTpsln6+65D+SwNnsxDueUVnyv8/Sa6+qAIMEQnCYEENUdSEl2THvaqKUUuocM8YQRYaoaUnP0RBFESE4wZW+Dk2h2h7kFL0Brj+iHORVSBrklAtBaRyWPK70S4KS99StShYvhiB19w8xwSYEe/hSZWMTxCbIMlfunGyL0lE/tHi9eHshbYnHBIepW6GMuGoQt5RAgYyXkkCBmJIgZR2U6jUOMQ5v6tYrU7VmGROAKkxVN3Ko1rYOVbZuiYpEMKEKVkYEI1WrlAkGpFr8CWPk6VtlAUYogsOaDBNVLTBpQwOMUkpdzIwxRIkhSuwRE2u0qe6CfeYF73HzI8rePEV3iOsPKOdHlP0R5WCEG+aUwxI3LHB5iRsttCgtaoVaEpSoW5QiAnE9XmYhLKWEaGlowkRIFJ2R7rhjOvLFj3y80IR11M/VAUgcNuoC951uCY5rVQUYESH3JTEpjAPM1AqXSiml1MXGRhHpVIt0qkXrqJtfnRkiAs4R8gIpC8KwDkbzoyoYjQr8qMTlJX5Uzy+TO3xRL6XDFx5feryrQlNwodr2Cy1N9Xao7kkZBIJYQt3qJFTjZ8REBBMTbHXn8Wo7rvbbGBbf48rUE+qZpOrvOktWXYAZlZ7INDB1F1KjcQY7b5VSSqnzhDEGkoQoSYAWzIxvgH7OifdIUSB5TiiKartewmhEyAvKYVEFqTpMlaOC2dnuWSvTqgowAPPOEZFi6wCTZdoCo5RSSp1NJoowzSY0m6c06Hmm24Vb331WyrQSV3adNpHAIB8R2wQbVSOqtQVGKaWUuvisqgBjEPJ8hLURUR1gmk0NMEoppdTFZlUFGBFhmI9YPMy82XwRc3srpZRSalVaVQEGhEE+wKbVtVwikCR6KwGllFLqYrPKAgyM8gEmqy7LCj5edAtwpZRSSl0sVlWAMSKMiiGk1fiXEJIT/IRSSimlLkSrKsCICKN8HtJqauLgNcAopZRSF6NVFWAAyvlZSKoWGNEWGKWUUuqitKomsjNG8L0esjAVYVipOQmVUkoptZJWVYARgTAcIGk1D6CRxgqXSCmllFIrYXV1IRlg3mOy+eqh6CR2Siml1MXotALMxz/+cbZu3Uqj0eC6667j4YcfPuaxn/zkJ/mRH/kRZmZmmJmZYfv27cc9/rhEkHkwjT4ASXR2bpOulFJKqfPbKQeYv/3bv+W2227jzjvv5Bvf+Aavec1reMtb3sLevXuXPf7+++/nhhtu4L777uPBBx9k8+bNvPnNb+b5558/jeIKrhdh6xaYLNtwGq+hlFJKqdXOiIicyg9cd911vO51r+NjH/sYACEENm/ezK/92q9x++23n/DnvffMzMzwsY99jHe+850n9Z7dbpfp6Wk++6k30oimaLzkGdJsyHT8v9j2P993KsVXSiml1Dmy8P09NzfH1NTUGX3tUxrEWxQFu3bt4kMf+tB4n7WW7du38+CDD57UawwGA8qyZM2aNcc8Js9z8jwfP+52uwC01v03rdbhRqN1m191KsVXSiml1AXilALM/v378d6zfv36JfvXr1/Pt7/97ZN6jQ9+8INs2rSJ7du3H/OYHTt28Lu/+7tH7Z/97mspGikSLNMbr2LrFW88leIrpZRS6gJxTi+jvvvuu7nnnnu4//77aTSOfQn0hz70IW677bbx4263y+bNm3n7e/7PGW+CUkoppdTqc0oBZt26dURRxJ49e5bs37NnDxs2HH9A7R/90R9x99138y//8i+8+tWvPu6xWZaRZdmpFE0ppZRSF5FTugopTVOuueYadu7cOd4XQmDnzp1cf/31x/y5P/iDP+CjH/0oX/nKV9i2bdvpl1YppZRSitPoQrrtttu46aab2LZtG9deey1/8id/wvz8PO9617sAeOc738mll17Kjh07APj93/997rjjDj772c+ydetWdu/eDUC73abdbp/Bj6KUUkqpi8UpB5if//mfZ9++fdxxxx3s3r2b1772tXzlK18ZD+x99tlnsfZww84nPvEJiqLgZ3/2Z5e8zp133snv/M7vvLjSK6WUUuqidMrzwKyEs3kduVJKKaXOjrP5/b267oWklFJKKYUGGKWUUkqtQhpglFJKKbXqaIBRSiml1KqjAUYppZRSq44GGKWUUkqtOhpglFJKKbXqaIBRSiml1KpzTu9GfboW5trrdrsrXBKllFJKnayF7+2zMWfuqggwBw4cAGDz5s0rXBKllFJKnaoDBw4wPT19Rl9zVQSYNWvWANV9ls70CVCnptvtsnnzZp577jm9rcMK07o4f2hdnF+0Ps4fc3NzbNmyZfw9fiatigCzcHPI6elp/ct4npiamtK6OE9oXZw/tC7OL1of54/FN3k+Y695xl9RKaWUUuos0wCjlFJKqVVnVQSYLMu48847ybJspYty0dO6OH9oXZw/tC7OL1of54+zWRdGzsa1TUoppZRSZ9GqaIFRSimllFpMA4xSSimlVh0NMEoppZRadTTAKKWUUmrVOe8DzMc//nG2bt1Ko9Hguuuu4+GHH17pIl3wduzYwete9zomJye55JJLePvb386TTz655JjRaMTNN9/M2rVrabfbvOMd72DPnj0rVOKLx913340xhltvvXW8T+vi3Hr++ef5xV/8RdauXUuz2eRVr3oVjz766Ph5EeGOO+5g48aNNJtNtm/fzlNPPbWCJb4wee/5yEc+whVXXEGz2eSlL30pH/3oR5fcc0fr4uz413/9V37iJ36CTZs2YYzhS1/60pLnT+a8Hzx4kBtvvJGpqSk6nQ7vec976Pf7p1YQOY/dc889kqapfOpTn5L//M//lF/5lV+RTqcje/bsWemiXdDe8pa3yKc//Wl54okn5PHHH5cf//Efly1btki/3x8f8973vlc2b94sO3fulEcffVR++Id/WF7/+tevYKkvfA8//LBs3bpVXv3qV8stt9wy3q91ce4cPHhQLr/8cvnlX/5leeihh+Tpp5+Wf/7nf5bvfve742PuvvtumZ6eli996UvyzW9+U37yJ39SrrjiChkOhytY8gvPXXfdJWvXrpUvf/nL8r3vfU8+//nPS7vdlj/90z8dH6N1cXb84z/+o3z4wx+WL3zhCwLIF7/4xSXPn8x5f+tb3yqvec1r5Otf/7r827/9m7zsZS+TG2644ZTKcV4HmGuvvVZuvvnm8WPvvWzatEl27NixgqW6+Ozdu1cA+drXviYiIrOzs5IkiXz+858fH/Nf//VfAsiDDz64UsW8oPV6Pbnyyivlq1/9qvzoj/7oOMBoXZxbH/zgB+WNb3zjMZ8PIciGDRvkD//wD8f7ZmdnJcsy+Zu/+ZtzUcSLxtve9jZ597vfvWTfz/zMz8iNN94oIloX58qRAeZkzvu3vvUtAeSRRx4ZH/NP//RPYoyR559//qTf+7ztQiqKgl27drF9+/bxPmst27dv58EHH1zBkl185ubmgMM31dy1axdlWS6pm6uuuootW7Zo3ZwlN998M29729uWnHPQujjX/v7v/55t27bxcz/3c1xyySVcffXVfPKTnxw//73vfY/du3cvqY/p6Wmuu+46rY8z7PWvfz07d+7kO9/5DgDf/OY3eeCBB/ixH/sxQOtipZzMeX/wwQfpdDps27ZtfMz27dux1vLQQw+d9Hudtzdz3L9/P9571q9fv2T/+vXr+fa3v71Cpbr4hBC49dZbecMb3sArX/lKAHbv3k2apnQ6nSXHrl+/nt27d69AKS9s99xzD9/4xjd45JFHjnpO6+Lcevrpp/nEJz7Bbbfdxm//9m/zyCOP8Ou//uukacpNN900PufL/d7S+jizbr/9drrdLldddRVRFOG956677uLGG28E0LpYISdz3nfv3s0ll1yy5Pk4jlmzZs0p1c15G2DU+eHmm2/miSee4IEHHljpolyUnnvuOW655Ra++tWv0mg0Vro4F70QAtu2beP3fu/3ALj66qt54okn+Iu/+AtuuummFS7dxeVzn/scn/nMZ/jsZz/LK17xCh5//HFuvfVWNm3apHVxkThvu5DWrVtHFEVHXU2xZ88eNmzYsEKlurh84AMf4Mtf/jL33Xcfl1122Xj/hg0bKIqC2dnZJcdr3Zx5u3btYu/evfzQD/0QcRwTxzFf+9rX+LM/+zPiOGb9+vVaF+fQxo0b+cEf/MEl+17+8pfz7LPPAozPuf7eOvt+8zd/k9tvv51f+IVf4FWvehW/9Eu/xG/8xm+wY8cOQOtipZzMed+wYQN79+5d8rxzjoMHD55S3Zy3ASZNU6655hp27tw53hdCYOfOnVx//fUrWLILn4jwgQ98gC9+8Yvce++9XHHFFUuev+aaa0iSZEndPPnkkzz77LNaN2fYm970Jv7jP/6Dxx9/fLxs27aNG2+8cbytdXHuvOENbzhqSoHvfOc7XH755QBcccUVbNiwYUl9dLtdHnroIa2PM2wwGGDt0q+wKIoIIQBaFyvlZM779ddfz+zsLLt27Rofc++99xJC4Lrrrjv5N3vRQ5DPonvuuUeyLJO/+qu/km9961vyq7/6q9LpdGT37t0rXbQL2vve9z6Znp6W+++/X1544YXxMhgMxse8973vlS1btsi9994rjz76qFx//fVy/fXXr2CpLx6Lr0IS0bo4lx5++GGJ41juuusueeqpp+Qzn/mMTExMyF//9V+Pj7n77rul0+nI3/3d38m///u/y0/91E/ppbtnwU033SSXXnrp+DLqL3zhC7Ju3Tr5rd/6rfExWhdnR6/Xk8cee0wee+wxAeSP//iP5bHHHpNnnnlGRE7uvL/1rW+Vq6++Wh566CF54IEH5Morr7ywLqMWEfnzP/9z2bJli6RpKtdee618/etfX+kiXfCAZZdPf/rT42OGw6G8//3vl5mZGZmYmJCf/umflhdeeGHlCn0ROTLAaF2cW//wD/8gr3zlKyXLMrnqqqvkL//yL5c8H0KQj3zkI7J+/XrJskze9KY3yZNPPrlCpb1wdbtdueWWW2TLli3SaDTkB37gB+TDH/6w5Hk+Pkbr4uy47777lv2OuOmmm0Tk5M77gQMH5IYbbpB2uy1TU1Pyrne9S3q93imVw4gsmrZQKaWUUmoVOG/HwCillFJKHYsGGKWUUkqtOhpglFJKKbXqaIBRSiml1KqjAUYppZRSq44GGKWUUkqtOhpglFJKKbXqaIBRSiml1KqjAUYppZRSq44GGKWUUkqtOhpglFJKKbXqaIBRSiml1Krz/wFbNUXgEeFL6QAAAABJRU5ErkJggg==",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -150,7 +150,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -177,7 +177,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -205,7 +205,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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c79SpU4qiKMbXVtWqVU3K/fTTTwpg8v6Q3vN8+PDhJs/zxwHKhAkTjH937NhRsbS0VC5fvmzcdvv2bcXBwUF5+eWXjdtSnxfNmzc3+b+NHj1a0el0Snh4uHFbev+joUOHKra2tkpcXJxxW79+/Z75Ppr6fGnZsqXJ62rWrFkKoMyfP9+4rVGjRgqg/Pbbb8Zt8fHxiru7u9KlS5ennsdgMBj3d3NzU3r27KnMnj3b5Dn1rLiffH9RlJT61mq1ypkzZ0y2b968Oc1rX1EUpW3btiafW+n9f+vVq5fmOXzo0KE0jz2zr5X04s4uuSz1hPLly+Ps7GzsS3PixAmio6ONnfX8/f3Zu3cvkNIXJzk52aQJ/vE+MxEREYSFhdGoUSOuXLlCRESEybm8vb1p1apVunH07dvX5FtAnTp1jB0LH1enTh1u3LhBUlJSho8pMjISIE2rTUREBC4uLia3p41A2rBhA+7u7vTs2dO4zcLCgpEjR/Lw4UN27doFpLTcAPzzzz9ASgtNrVq1aNGihbE5Ozw8nNOnTxvLPsuECRNwcXHB3d2dxo0bc/nyZb7++ms6d+7Mv//+y927d3nrrbdMrgm3a9cOX1/fdC8TPe3bcmZt2LCB2rVrm/z/7e3tGTJkCFevXuXs2bNZOhbAmDFjTLa/++67AM+81DVw4EA2bdpE48aN2bNnD5MnT6Zhw4b4+Piwb9++NOW7d+9u/FYFj/5nV65cMW57/LkcFxdHWFgYdevWBeDo0aNpjvnmm2+a/L1ixQoMBgOvvfYaYWFhxpu7uzs+Pj7pjjzMyGuvvUZsbCzr1q0jKiqKdevWZXhJavny5RQqVIgWLVqYnLdGjRrY29sbz7tt2zYSEhJ4++23TVqh0utQnp7H6yc6OpqwsDD8/f1RFIVjx46lKf9k/TRs2NCkvtOT+tp1cHDIVExZfU4OGDDA5Nv6k8+D1NfWm2++aVKuf//+FCpUKFMxZUZycjJbtmyhY8eOlC5d2ri9WLFi9OrViz179hjrItWQIUNM/m8NGzYkOTnZ5DLJ4/+jqKgowsLCaNiwITExMVkedZT6fBk1ahRa7aOPzsGDB+Po6JjmNWpvb2/ST9DS0pLatWs/83+u0WjYvHkzn3/+OYULF+aPP/5g+PDhlCpViu7duz9Xn5tGjRpRoUIFk21NmzalaNGiJi1eDx48YOvWrXTv3v2px+vevTtHjhwxGYiydOlSrKysePXVV43bsvpayQmS3DxBo9Hg7+9v7Fuzd+9eXF1dKVu2LGCa3KT+fPyNZO/evTRv3hw7OzucnJxwcXExdgpLL7nJSMmSJU3+Tn0jebIZuFChQhgMhjTHflzqG+PDhw9Nttvb27N161a2bt2aplkxPdeuXcPHx8fkhQ0YLyulvqm4ubnh4+NjTGR2795Nw4YNefnll7l9+zZXrlxh7969GAyGTCc3Q4YMYevWrWzfvp0jR45w9+5dPvjgA5PzPn5ZL5Wvr2+aa8J6vT5Tl26e5dq1a+me88n6yOyxtFqt8XmWyt3dHScnp0wdq1WrVmzevJnw8HD++ecfhg8fzrVr13jllVfSdCp+8vmVmug83ifl/v37vPPOO7i5uWFjY4OLi4vxOZve8+3J5/PFixdRFAUfH580SfS5c+ey1NHZxcWF5s2bs2TJElasWEFycjJdu3ZNt+zFixeJiIjA1dU1zXkfPnxoPG9qnfr4+KQ51+OJX0auX79O//79KVKkiLEfTaNGjYC09WNtbY2Li4vJtsKFC6fpA/QkR0dHIOWDOTOy+px81vMgozqysLAwSUKeV2hoKDExMRnGbjAY0vTTysxz+MyZM3Tq1IlChQrh6OiIi4uLMeF42ntmejJ6n7G0tKR06dJp6rZEiRJpLt1m5n8OYGVlxccff8y5c+e4ffs2f/zxB3Xr1jVeUsqu9D5z9Ho9Xbp0YfXq1cb+fStWrCAxMfGZyU23bt3QarXGxEhRFJYvX27sN5UqK6+VnCJ9btLRoEED1q5dy6lTp9IMsfT39+f999/n1q1b7NmzBw8PD+OL/PLlyzRr1gxfX1+mTZuGp6cnlpaWbNiwgenTp5t0RAaeOjIqo+vrGW1X/uvjkJ7UYYOnT5822a7X62nevDkAN2/ezHD/7GjQoAHbt28nNjaWI0eOMH78eCpVqoSTkxO7d+/m3Llz2NvbU61atUwdz8fHxxjr87KyskqToOUVOTGBla2tLQ0bNqRhw4YULVqUSZMmsXHjRvr162csk5nn0Wuvvca+fft4//33qVq1Kvb29hgMBlq3bp3muQxpn88GgwGNRsPGjRvTPV9W+3/16tWLwYMHExwcTJs2bTLsr2MwGHB1dWXx4sXp3v9kkpEdycnJtGjRgvv37zN27Fh8fX2xs7Pj1q1b9O/fP039ZKa/THpSX7unTp2iY8eOzxt2Gtl5P8krnhV7eHg4jRo1wtHRkc8++4wyZcpgbW3N0aNHGTt2bLrP4RcZX2YVK1aMHj160KVLFypWrMiyZctYsGABer0+w/eLjDpMZ/SZ06NHD3788Uc2btxIx44dWbZsGb6+vvj5+T01Ng8PDxo2bMiyZcv46KOPOHDgANevX+frr782iSUrr5WcIslNOh6f72bv3r0mzdQ1atTAysqKgIAADh48SNu2bY33rV27lvj4eNasWWPyrSIrze+5oVy5cvj4+LBq1SpmzJiR7SG6pUqV4uTJkxgMBpPkILV5N7UjHKQ0Ef/666/8+eefJCcn4+/vj1arpUGDBsbkxt/fP9tv+k/GBSkdtFNHUaUKDAw0ietpsppYlCpVisDAwDTb06uPzBzLYDBw8eJFkw7WISEhhIeHZ+lYj6tZsyYAd+7cydJ+Dx48YPv27UyaNMmks/rFixczfYwyZcqgKAre3t689NJLWTp/ejp16sTQoUM5cOCASRN6eufdtm0b9evXf+oXiNQ6vXjxokkrRGho6DO/XZ86dYoLFy6wcOFCk07mj4/oygkNGjQwXpr46KOPnvl6ycnn5OPlL168aPLaSkxMJCgo6Jkffpl9Tbm4uGBra5th7Fqt9qmdl9MTEBDAvXv3WLFiBS+//LJx++MjLLPi8feZx58vCQkJBAUF5diXr4xYWFhQpUoVLl68aLy8W7hw4XQvU2Wl1RhS5nEqVqwYS5cupUGDBuzYsYOPP/44U/t2796dt956i8DAQJYuXYqtrS3t27c33v+iXitPyptfX1VWs2ZNrK2tWbx4Mbdu3TJpubGysqJ69erMnj2b6Ohok0tSqW88j2fmERER/Prrry8u+AxMnDiRsLAw44RQT8rMt4m2bdsSHBxs8sGSlJTEzJkzsbe3NzYzwqNr919//TVVqlQxXlZr2LAh27dv599//830JalnqVmzJq6ursydO9dk2PTGjRs5d+4c7dq1y9Rx7OzsstRE2rZtWw4dOsT+/fuN26Kjo/npp5/w8vJKc237WccC0syQPG3aNIBnPobt27enuz21L096zf1Pk95zOb34nqZz587odDomTZqU5jiKoqQZUv4s9vb2zJkzh4kTJ5q8eT7ptddeIzk5mcmTJ6e5Lykpyfhh0Lx5cywsLJg5c6ZJfJl5jOnVj6IofPfdd5l8NJlja2vL2LFjOXfuHGPHjk33dfr7779z6NAhIGefk5Dy2nJxcWHu3LkmI8gWLFiQqb4fqV+knlVWp9PRsmVLVq9ezdWrV43bQ0JCjBM4Pn6ZIzPS+x8lJCTwww8/ZOk4qZo3b46lpSXff/+9yTF/+eUXIiIiMv0+8ywXL17k+vXrabaHh4ezf/9+ChcubGx9LFOmDBERESZTbKRO7pgVWq2Wrl27snbtWhYtWkRSUtIzL0ml6tKlCzqdjj/++IPly5fzyiuvmHyBflGvlSdJy006LC0tqVWrFrt378bKyooaNWqY3O/v78+3334LmPa3admyJZaWlrRv356hQ4fy8OFD5s2bh6ura5a/Oee0Xr16cfr0aaZMmcKhQ4fo0aMH3t7eREdHc/r0af744w8cHBye2tdgyJAh/Pjjj/Tv358jR47g5eXFX3/9xd69e5kxY4ZJp8eyZcvi7u5OYGAgb7/9tnH7yy+/zNixYwFyLLmxsLDg66+/ZsCAATRq1IiePXsah4J7eXkxevToTB2nRo0aLF26lDFjxlCrVi3s7e2f+iH64YcfGocnjxw5kiJFirBw4UKCgoL4+++/s3Tpy8/Pj379+vHTTz8Zm9MPHTrEwoUL6dixo8ms2Ol59dVX8fb2pn379pQpU4bo6Gi2bdvG2rVrqVWr1lMfR3ocHR15+eWX+eabb0hMTKR48eJs2bIlS996y5Qpw+eff864ceO4evUqHTt2xMHBgaCgIFauXMmQIUOyPOPz45fWMtKoUSOGDh3KlClTOH78OC1btsTCwoKLFy+yfPlyvvvuO7p27Wqca2bKlCm88sortG3blmPHjrFx40aKFi361HP4+vpSpkwZ3nvvPW7duoWjoyN///13pvpTZNX777/PmTNn+Pbbb9m5cyddu3bF3d2d4OBgVq1axaFDh4ydxnPyOQkpr63PP/+coUOH0rRpU7p3705QUBC//vprpvrcpL53jhw5klatWqHT6TIcWvz555+zdetWGjRowFtvvYVer+fHH38kPj4+U/PDPMnf35/ChQvTr18/Ro4ciUajYdGiRdm+5Obi4sK4ceOYNGkSrVu3pkOHDgQGBvLDDz9Qq1Ytk87Dz+PEiRP06tWLNm3a0LBhQ4oUKcKtW7dYuHAht2/fZsaMGcaEoUePHowdO5ZOnToxcuRIYmJimDNnDi+99FK6nf6fpnv37sycOZMJEyZQuXLlTE/R4erqSpMmTZg2bRpRUVFpkqIX+VoxkSNjrszQuHHjFEDx9/dPc9+KFSsUQHFwcFCSkpJM7luzZo1SpUoVxdraWvHy8lK+/vprZf78+QqgBAUFGcs9OXw1VeoQzeXLl5tsf3xY7ONSh86FhoZm6nEFBAQoXbt2VYoVK6ZYWFgojo6OSs2aNZUJEyYod+7cMSn75FBwRVGUkJAQZcCAAUrRokUVS0tLpXLlyiZDAx/XrVs3BVCWLl1q3JaQkKDY2toqlpaWJsO2M5I6/PB///vfM8suXbpUqVatmmJlZaUUKVJE6d27t3Lz5k2TMv369VPs7OzS3f/hw4dKr169FCcnJwUwGWJJOkPBFUVRLl++rHTt2lVxcnJSrK2tldq1ayvr1q1L9zE8bSi4oqRMOzBp0iTF29tbsbCwUDw9PZVx48aZDFnNyB9//KH06NFDKVOmjGJjY6NYW1srFSpUUD7++GPjEP3HY0mvPnliaO7NmzeVTp06KU5OTkqhQoWUbt26Kbdv305T7lnPwb///ltp0KCBYmdnp9jZ2Sm+vr7K8OHDlcDAwKc+poye80/K6LX0008/KTVq1FBsbGwUBwcHpXLlysoHH3yg3L5921gmOTlZmTRpklKsWDHFxsZGady4sXL69GmlVKlSzxwKfvbsWaV58+aKvb29UrRoUWXw4MHKiRMn0vyvM3rOZXXY619//aW0bNlSKVKkiKLX65VixYop3bt3VwICAkzKZeY5mdH7THrPVUVRlB9++EHx9vZWrKyslJo1ayr//PNPmveH9PZNSkpS3n77bcXFxUXRaDQmj/fJ55GiKMrRo0eVVq1aKfb29oqtra3SpEkTZd++fSZlMnpepPc/2rt3r1K3bl3FxsZG8fDwUD744APj0OfHy2VmKHiqWbNmKb6+voqFhYXi5uamDBs2THnw4IFJmUaNGikVK1ZMs29mzhMSEqJ89dVXSqNGjZRixYoper1eKVy4sNK0aVPlr7/+SlN+y5YtSqVKlRRLS0ulXLlyyu+//57hUPD03sNSGQwGxdPTUwGUzz//PM39GT03FEVR5s2bZ/xMTO99PbOvlZwcCq5RlHzQc0wIIYQQIpOkz40QQgghzIokN0IIIYQwK5LcCCGEEMKsqJrc/PPPP7Rv3x4PDw80Gg2rVq165j4BAQFUr14dKysrypYta1yhVAghhBACVE5uoqOj8fPzY/bs2ZkqHxQURLt27WjSpAnHjx9n1KhRDBo0iM2bN+dypEIIIYTIL/LMaCmNRsPKlSufOsX42LFjWb9+vckyAj169CA8PJxNmza9gCiFEEIIkdflq0n89u/fn2aK61atWj11Fd/4+HiTWWsNBgP379/H2dk5R9bxEUIIIUTuUxSFqKgoPDw8njkhZb5KboKDg3FzczPZ5ubmRmRkJLGxsemuIzNlyhQmTZr0okIUQgghRC66ceMGJUqUeGqZfJXcZMe4ceMYM2aM8e+IiAhKlixJUFCQyXIBuWHy+nOsOHaHHjWL07VG8Vw9V36QlJTMoYMHqV2nDnr98y+YmV3hsYn8ezWcf6894MTNCBKTU67MdqxajPHtfM22RS8xMZGdO3fSpEkTLCws1A6nwJH6V5fUv7pyov6joqLw9vbO1Gd3vkpu3N3dCQkJMdkWEhKCo6Njhqv/WllZYWVllWZ7kSJFsrwQW1bdS7RAa2VLzXKe1PbN2oq25igxMZHQy6eoVc5T9TeXltVSfsYlJrPx9B3eXXaCNeciqFUumj51s7cCd16XmJiIra0tzs7Oqtd/QST1ry6pf3XlRP2n7peZL6D5ap6bevXqpVn9eOvWrdSrV0+liJ7uVngsACWc0k+8hPqsLXR0qlaCsa19AZi05gwHr2RttWohhBB5i6rJzcOHDzl+/DjHjx8HUoZ6Hz9+3Ljc+7hx4+jbt6+x/JtvvsmVK1f44IMPOH/+PD/88APLli3L9KrPL5KiKNz+L7kpXliSm7xuyMulae/nQZJB4a3FR43/OyGEEPmPqsnNv//+S7Vq1ahWLeUawZgxY6hWrRrjx48H4M6dO8ZEB8Db25v169ezdetW/Pz8+Pbbb/n5559p1aqVKvE/zb3oBOISDWg0UKyQJDd5nUaj4ZsuVahQzJF70QkMXXSEuMRktcMSQgiRDar2uWncuDFPm2YnvdmHGzduzLFjx3Ixqpxx60HKN39XByss9fnq6l+BZWOp48c+NXh19l5O3Yrgo5WnmPZaVbXDEkIIkUXyqZtLUvvbFJf+NvmKZxFbZvWqhk6rYcXRW6w/eUftkIQQQmSRJDe5JLXlpnhhW5UjEVnlX6YobzUuA8D41ae59zD+GXsIIYTISyS5ySXScpO/vd3UB193B+5FJzB+9Rm1wxFCCJEFktzkkpsPYgAoISOl8iVLvZap3fzQaTWsP3VHLk8JIUQ+IslNLrn5QIaB53eVihdi+H+Xpz5dfZowuTwlhBD5giQ3uUQm8DMPI/67PHU/OoHxq08/ewchhBCqk+QmF0TGJRIVlwRIy01+9/jlqQ2ngll38rbaIQkhhHgGSW5yQepIqcK2Ftha5qvlu0Q6Hr889fHK0zJ7sRBC5HGS3OSCW9LfxuyMaOqDX4lCRMQm8s6fx0hKNqgdkhBCiAxIcpMLZBi4+bHUa5nZszoOVnoOX33A99svqh2SEEKIDEhykwseJTcygZ85Kelsy5edKwMwc+cl9l0OUzkiIYQQ6ZHkJhfIZSnz1d7Pgx61PFEUGPXncZm9WAgh8iBJbnLBzdRh4JLcmKUJ7StS1tWeu1HxvLv8BAZDxou/CiGEePEkuckFt/6bnVj63JgnG0sds3pVw0qvJSAwlAX7rqodkhBCiMdIcpPD4hKTCXuYAEjLjTnzdXfkk3blAZi6JVCGhwshRB4iyU0OS+1MbGepo5CNhcrRiNzUu04papQqTExCMp+tPat2OEIIIf4jyU0Oe7wzsUajUTkakZu0Wg1fdKqETqth05lgtp8LUTskIYQQSHKT42SOm4LF192RQQ28ARi/+gyxCckqRySEEEKSmxwmw8ALnnea+1DcyYZb4bF8v0Mm9xNCCLVJcpPDZAK/gsfWUs/EDhUBmPfPFS6ERKkckRBCFGyS3OSw1JYbGSlVsLSo4EaLCm4kGRQ+XnlK5r4RQggVSXKTw4wtN5LcFDgTO1TExkLH4asPWHHsltrhCCFEgSXJTQ5KTDZwJ+K/lhvpUFzgFHeyYWQzHwC+2XSeh/FJKkckhBAFkyQ3OSg4Ig6DApY6LUXtrdQOR6hgYAMvSjnbcjcqntk7L6kdjhBCFEiS3OSg1EtSHk7WaLUyx01BZKXX8Um7CgD8sjuIa/eiVY5ICCEKHkluclC6w8DDr8PRRXD7GCTGqRSZeJGal3eloU9REpINfLH+nNrhCCFEgaNXOwBzku4Efld2wZoRKb9rdFD0JXCvBO6Vwa0SlKgF1o4qRCtyi0ajYfwrFWj93W62nA1hz8UwGvgUVTssIYQoMCS5yUHGlpvH57ixKQzeL0PwaYi9D6HnUm6nlqfc//rfULZ5yu/n1sH59WDl8MTNMeVnyTopxwNIigeNFnSyflVe5OPmQJ+6pViw7yqfrTvDhpEN0eukoVQIIV4ESW5yULrDwMu/knJTFIi6k5LkBJ+EkNMQfArcKj8qe/sYnFiS8QkG74DiNVJ+P/gjbP0U9NbpJ0JNPwW3lL4f3DkJNw6mlLWweexmm/KzSOmUfQAMyYAGtPJB/LxGN3+J1cdvcSHkIYsPXqefv5faIQkhRIEgyU0OSk1u0p3AT6MBR4+U20st0z+AT4uUJCM+CuIjIf7hfz//+9umyKOy8f/NgpsUl3KLDjU9VsP3Hv1+JSAlEcpI39VQunHK70cWwPoxoLMyTYT0NmBhDS2/gFL1UspePwgn/ki536kkVO0tl9geU8jWgjEty/HpqtNM23qBDn4eFLazVDssIYQwe5Lc5BCDQXn+RTNL1k25ZUajsVDvLYiLhISH/yVA/yVBcZFQ2OtR2SLeUL5DShKUGPvolvTfT0uHR2WT/uv0nByfcosLNz1vYsyj30PPwZFfH/39z//g5feh5kDQy1B4gF61S7L4wDXOB0cxY9sFJr1aSe2QhBDC7Elyk0PCouNJSDKg1YB7IevcP6FOn9L/JrUPztOUb59yy4xag6BK98eSoBjTpKiY36OyxapC448gMTqlr9C9S7DpQ9j/AzT5CKq8Blpdth6eudBpUzoX9/r5IL8fvE7vuqV4yc3h2TsKIYTINklucsjN/zoTuztaY5GfO47qrTLf6uJRNeUGKX18ji+GgK8g4jpsmwAVOoClXW5Fmm/4ly1KywpubDkbwuR1Z/ltYG00GpkHSQghcoskNznEwUpP95qe2FkV0CrVWUCN/imtPgd/BHvXR4mNwZDSWdqtiqohqunjduUJCAxl98Uwdgbepamvm9ohCSGE2Sqgn8Q5z8fNga+7FtwPbyMLG2gwynTbmRXw9xvoyrbAUd9IlbDUVsrZjgENvPhx1xU+X3eOBmVdsNTn4xY+IYTIw+TdVeS++0Gg0aG9tJXG5z9Ft/pNeHBV7aheuBFNylLU3pIrYdH8tv+q2uEIIYTZkuRG5L5G78OIwxgqdESDgvb0XzCzJmz4AB7eVTu6F8bB2oL3WpYD4LvtF7kfnaByREIIYZ4kuREvhnMZkjv9TEC5SRhKNwFDIhz6Ef4aqHZkL1S3mp5UKOZIVFwS07YGqh2OEEKYJUluxAsVYetNcs/l0HcNeFRPmRcnVUKM2S8uqtNqmNA+ZeboJQevcz44UuWIhBDC/EhyI9RRulHKchKlH+tgvO97mFUTji3+bxkI81SntDNtK7tjUGDSmrMoiqJ2SEIIYVYkuRHqeXyuF0MynF4BETdg9Vswxz9lYkAz/eAf16Y8lnot+6/cY/OZELXDEUIIsyLJjcgbtDoYugtafAbWThB6Hv7sBb+0hKt71Y4ux3kWsWXoy6UB+GLDWeISzbelSgghXjRJbkTeYWED9d+Bd05AgzEpi3XePAQL2sLad9SOLscNa1wGd0drbtyP5Zc9QWqHI4QQZkOSG5H32DhB8wnwzvGURTiLlAb/kWpHleNsLfV82MYXgNk7LxESad6dqYUQ4kWR5EbkXQ7u8Mp0GH4YnMuoHU2ueLWqB9VLOhGTkMzXm86rHY4QQpgFSW5E3qd7bJWQC5vh70GQnKRePDlIo9EwoX1FAFYcvcWx6w9UjkgIIfI/SW5E/hFzP2XSv1PLYfXwlAU5zYCfpxNda5QAYMKaMyQbzHOEmBBCvCiS3Ij8w7YIdJ4HGh2c/BM2vGc2Q8U/aF0OB2s9J29G8Ote6VwshBDPQ5Ibkb/4toXOPwEa+PcX2DbBLBIcVwdrPm5bHoBvt1zgxv0YlSMSQoj8S5Ibkf9U7grtv0v5fe938M9UdePJId1reVK3dBFiE5P5aOUpmblYCCGySZIbkT/V6Aetvkz5fefnKR2N8zmNRsOUzlWw1GvZfTGMlcduqR2SEELkS5LciPyr3nBoPA6qvQ5lm6sdTY7wLmrHqOY+AHy27ixhD+NVjkgIIfIfSW5E/tZoLHSYlbJ8g5kY3LA05Ys5Eh6TyOR1Z9UORwgh8h1JbkT+ptE8WoAzOQlWj4DAjerG9JwsdFq+7lIZrQZWH7/NptN31A5JCCHSNXVzIAv3XVU7jDQkuRHm49/5cGwRLOsHV3apHc1zqVLCiUENUxbWHPnncXZdCFU5IiGEMHU7PJZZOy8xed3ZPDc/lyQ3wnzUHADl2kFyPPzRE24cVjui5/J+q3K0quhGQpKBwb/9y56LYWqHJIQQRg9iEgBIMijci85b/QMluRHmQ2cB3X6F0k0gMRoWd4E7J9WOKtssdFpm9qxO8/IpCc4bCw+z75IkOEKIvCEy9tEyOHcjJbkRIvforaDHYvCsC3ERsKgThF5QO6pss9Rrmd27Gk19XYlPMjBw4WH2X76ndlhCCEFkXKLx99AoSW6EyF2WdtB7GRTzg5gwWNINkhLUjirbrPQ65rxencblXIhLNDBwwWG2nwtROywhRAEXGSvJjRAvlnUheH0leFSDdtNAb6l2RM/FSq9j7us1ePklF2ITkxn027/M3nlJZjEWQqgmMu6xy1JRcSpGkpYkN8J82TnDoB1QtpnakeQIawsdP/etyet1S6Io8L/NgQxfcpTo+KRn7yyEEDlMWm6EUIv2sad42MWUUVRxEerF85ws9Vo+71iZLztVxkKnYcOpYLrM2ScLbQohXrjH+9zcleRGCBUYDLCsLwRugMWvQUK02hE9l151SvLH4LoUtbfifHAUr87ey4WQKLXDEkIUICajpSS5MTV79my8vLywtramTp06HDp06KnlZ8yYQbly5bCxscHT05PRo0cTF5e3rvWJPEirhc4/pfTFuXEA/uwNifn7eVPTqwhr365PpeKO3I9OoM8vB6UFRwjxwshoqQwsXbqUMWPGMGHCBI4ePYqfnx+tWrXi7t276ZZfsmQJH374IRMmTODcuXP88ssvLF26lI8++ugFRy7yJffK0PtvsLCDKzvhr4GQnPjs/fKwYoVs+P2NOrzkZk9IZDx9fjmY595khBDm6fE+N3ej4vLUAAdVk5tp06YxePBgBgwYQIUKFZg7dy62trbMnz8/3fL79u2jfv369OrVCy8vL1q2bEnPnj2f2dojhJFnLej5B+isIHA9rBqWcskqH3OyteS3gXUoUdiGq/di6Df/kMk3KiGEyA2Pj5aKSzTwMA8NbtCrdeKEhASOHDnCuHHjjNu0Wi3Nmzdn//796e7j7+/P77//zqFDh6hduzZXrlxhw4YN9OnTJ8PzxMfHEx//6JtsZGQkAImJiSQmygfAi5Ra36rXu6c/mi7z0f3VD82p5SQ7v4Sh/mh1Y3pOzrY6fu1XnR7zDnP2TiQDfz3E/L41sLF8tFp6nqn/AkrqX11S/zkvMtZ0/rDb96Mp7WKXbtmcqP+s7KtachMWFkZycjJubm4m293c3Dh//ny6+/Tq1YuwsDAaNGiAoigkJSXx5ptvPvWy1JQpU5g0aVKa7Vu2bMHW1vb5HoTIlq1bt6odAgAeJYdQOnQrB+8VJ3HDBrXDyREDy8DMMzr+vRZOxxnbaF8ymdKOpmXySv0XVFL/6pL6zzn3o3SABq1GwaBoWLdtF2ULPX2f56n/mJjM9ylULbnJjoCAAL788kt++OEH6tSpw6VLl3jnnXeYPHkyn376abr7jBs3jjFjxhj/joyMxNPTk5YtW+Lo6JjuPiJ3JCYmsnXrVlq0aIGFhYXa4QBtQZlIC43q/epzVLVaDxj42xGuRBn47oye2l6FGdaoNLVLOrBt27Y8VP8FS957/hcsUv85y2BQGHUgJVHxcrbnSlg0pStWo22VYumWz4n6T73ykhmqJTdFixZFp9MREmI6jXxISAju7u7p7vPpp5/Sp08fBg0aBEDlypWJjo5myJAhfPzxx2i1aT+krKyssLKySrPdwsJCnuAqyZN1ryhwbi14NQDbImpH81z8fVzZPOpl5gRc5u+jNzl09QGHrh6hSnFHatlraK3T5736L0Dy5PO/AJH6zxkRsYmk9h/2cUtJbu7FJD2zbp+n/rOyn2pfWS0tLalRowbbt283bjMYDGzfvp169eqlu09MTEyaBEanS+lTkJd6aYt8aOt4WNYHNpvHyLtSznZ81aUK/3zQhAH1vbC20HLyViS/BOpoN2sffx+5SWJy/u5ILYRQT+pIKWsLLcWdUrp4hD7MOyM1VW2PHzNmDPPmzWPhwoWcO3eOYcOGER0dzYABAwDo27evSYfj9u3bM2fOHP7880+CgoLYunUrn376Ke3btzcmOUJkS/kOgAZO/AEXt6kdTY4pVsiGCe0rsmdsU9582RtrncKl0GjeXX6Cxv8LYMHeIK6EPiTZIF8OhBCZlzoi09HaAheHlKsjoZF5J7lRtc9N9+7dCQ0NZfz48QQHB1O1alU2bdpk7GR8/fp1k5aaTz75BI1GwyeffMKtW7dwcXGhffv2fPHFF2o9BGEuPGtB3WFw4AdYNwre2g9WDmpHlWOK2lvxbgsfvGIvEuZUngX7r3ErPJaJa88CKd++fFwdKOfuQPlijlQv6URFj0JY6tP//hMek4CtpT7D+4UQ5i11dmJHGwtcU5ObPNRyo3qH4hEjRjBixIh07wsICDD5W6/XM2HCBCZMmPACIhMFTtNP4Pw6CL8O2yZBu6lqR5TjbPQw9GVvBr1chuX/3uCvIzc5HxxFXKKBU7ciOHXr0bpbVnotfiWcqF6qMM52llwOfcjl0IdcuvuQBzGJ+Lo7sGp4fawtpNVUiILmUcuN3thyc1daboTIgyztoP33sKgjHJ4HlTpDKX+1o8oV1hY6+tTzok89L5INCtfuRRMYHMX54ChO34rg6PUHPIhJ5NDV+xy6ej/dY5wPjuKHnZcY07LcC45eCKG21D43jjYWuDr+l9xE5Z0lbSS5EeJxZZpAtdfh2O+wZiS8dQB05v0y0Wk1lHaxp7SLPW0qpwzjVBSFoLBo/r32gKPXHhAVn0SZonaUcbWnrKs9F0MeMmrpcebsukx7Pw983MznEp4Q4tlSZyd2tLbA1cEagAcxiSQkGfLE5WrzftcWIjtafg5hF6HRWLNPbDKi0TxKeF6r6Znm/grFHFl38g7bzoUwbsUplg2th1arUSFSIYQaIowtN3qcbCzQazUkGRTCHsbj4WSjcnR5YFVwIfIcm8IwcDOUbaZ2JHmWRqPhs1crYmep499rD/jj8HW1QxJCvEDGy1LWFmi1mkcjpvLIwr2S3AiRHs1jrRARt/L96uG5wcPJhvdbpfS3+WrDeUIi8871diFE7jJ2KLZJmVgvdcTUXUluhMgHji+B2bVh30y1I8mT+tTzws/Tiaj4JCatPaN2OEKIF8Q4FNw6JbmRlhsh8hONFhIeQsBXKf1whAmdVsOUTpXRaTVsOBXM1rMhz95JCJHvPWq5SemX6PJfp+K8MmJKkhshnqZKdyjbHJLjYfUIMMiSBU+q4OHI4IalAXh32XEuhkSpHJEQIrc93ucGHrXcyGUpIfIDjQZemQGW9nDjAPz7i9oR5UmjmvtQo1RhIuOS6P/rYe5K/xshzFpU3KMZiuFRnxu5LCVEfuHkCc0npvy+bWLKDMbChLWFjnl9a+Jd1I5b4bEMWHCYh/FJaoclhMglj1puUi5LSYdiIfKjmm9AyXop/W/WjgJZhT6NInaWLBxQm6L2lpy5Hclbi4/KyuNCmKFkg0JUvGnLTeplqTBJboTIR7Ra6DATLOzAvRIYpFUiPSWdbfmlXy1sLHT8cyGUj1eeQpFEUAiz8jDu0fufQ2rLjWNKh+LQqPg88ZqX5EaIzCrqA6NOQYvPQGehdjR5lp+nE7N6VUOrgWX/3mT4kqPSB0cIM5I6UsraQouVPmXh3KL2lgAkJBsIj1F/XjBJboTICjvnR7/LyKkMNSvvxpePDRFvNm0Xvx+4hsGg/jc6IcTziXhipBSAlV6Hk23K36EP1b80JcmNENlx9xz80gLOrlE7kjyrR+2SrB5eH78ShYiKS+KTVafpOncf54Mj1Q5NCPEcnpydOJWxU3GkJDdC5E+n/oJb/8KG9yD2gdrR5FmVihdixVv1mdi+AvZWeo5eD+eV7/cwbUsgCUnS8iVEfvRodmLThYWNsxQ/VP8ytCQ3QmTHy++Dsw88DIHNH6sdTZ6m02roX9+brWNeplVFN5IMCt/vuET7mXs4eTNc7fCEEFmU2nJTKE3LzX+zFEvLjRD5lIU1vDoL0MDxxXB5h9oR5XnFCtnwY5+a/NC7Os52lgSGRNFx9l6+2nieuMRktcMTQmSScY6bjC5L5YHh4JLcCJFdJetC7cEpv699B+IfqhtPPtG2cjG2jmlEez8PDArM3XWZltP/YU7A5TyzLo0QImORcaaLZqbKS4tnSnIjxPNoNgEKeabMWrzjc7WjyTeK2Fkys2c1fuxTAxcHK67fj+HrTeepN2UHgxb+y5YzwTIBoBB51KOWm/T73OSFLymS3AjxPKzsof2MlN9vHoKkBFXDyW9aVXRn53uN+bpLZaqXdCLZoLDtXAhDFh2h1fR/OHJNOmsLkdcYR0vl4ZYb/bOLCCGeqmxz6LUcyjQFnbykssreSk/3WiXpXqskl+5Gsezfm/x15CZXwqLpNncfQxuVYVRzH+NkYUIIdRlHS2XUoTgPJDfSciNETnippSQ2OaCsqwMftS3Pzncb06lacQwKzAm4TIeZezl9K0Lt8IQQZNxy4+qY0nITFZek+iABSW6EyEmJcbD9Mwg+pXYk+VohWwumd6/K3NdrmIys+vDvkxy4ck9mOhZCRRn1uXGw0mOlT0kr1L40JcmNEDlp20TY/S2sHgHJsrjm82pdyZ0to1+mdUV3kgwKfx6+QY+fDtDg6x1M2XhOZjsWQgVRGYyW0mg0xtYbtTsVS3IjRE5qMAqsC8Gd43BgttrRmAVneyvmvF6dPwbX5bWaJXCw0nM7Io4fd12h9YzdvP3HMe5HS0duIV6UiAzmuYFH/W6k5UYIc+LgDq2+TPl955cQdkndeMyERqOhXhlnvunqx+FPmjOnd3VaV3RHp9Ww9sRtWk7fxabTwWqHKYTZS0o28DA+/eUXAFzs88ZEfpLcCJHTqvaG0k0gKQ7WjpTVw3OYtYWONpWLMbdPDVa+5Y+Pqz1hDxN48/cjjPzjGA+kFUeIXJOa2AA4WKfTcuOYN4aDS3IjRE7TaKD9d2BhB9f2wpFf1Y7IbFUp4cS6kQ14q3EZtBpYc+I2Tb8NYMyy4/x95CbBEepPJiaEOUkdBm5jocNSnzaFMLbcqLy+lCQ3QuSGwqWg2fiU33d9A0nqz/tgrqz0Oj5o7cvKt+rj42rPg5hEVhy9xbvLT1B3ynaaT9vFlI3nuBspiY4Qz8s4DNwm/akv8kqHYpmYQ4jcUnswRNyA2kNAb6V2NGbPz9OJDe805FDQffZeCmPvpTBO3org0t2HXLr7kAV7r9KrTkmGNSqDq6O12uEKkS8Zh4Gnc0kKHutQ/FDdL3SS3AiRW7Q6aPWF2lEUKBY6LfXLFqV+2aIAhMcksPfSPX7Zc4Wj18P5de9Vlhy8Ts/aJXmjgTeeRWxVjliI/OVRy036yU2VEoWY17cmHk7qfoGQ5EaIF+XWUXCvDLr03xREznOytaRdlWK0rezOnkthzNh2kSPXHrBg31UW7LtKGRc7Gr3kSqNyLtTxLoK1hSzxIMTTGJdeSGekFKRM3dCigtuLDCldktwI8SJs+AAO/QhtvoE6Q9WOpsDRaDQ09HGhQdmi7L10jx8CLnEw6D6XQ6O5HBrE/L1BWOm1tKnkTv/63lT1dFI7ZCHypGe13OQVktwI8SK4lEv5GfAVVOkONk6qhlNQaTQaGvgUpYFPUSJiE9l3KYyAwFB2XQglODKOVcdvs+r4bap6OjGgvhdtKhVLd0SIEAXVs/rc5BWS3AjxIlTvB4d+gtDzKcsztJysdkQFXiEbC9pULkabysVQFIWTNyNYuP8q607c4fiNcN758zif2Z2lRBFbHKz02FvpsbfW4+FkQ796pXC2l07iouCJTF16IYPRUnlF3o5OCHOh00OLybCkGxycC7XegMJeakcl/qPRaPDzdGKaZ1XGtSnPkoPX+f3gNUKj4rmXzqSAC/YG8V6rcvSuUwqdVqNCxEKoQ1puhBCmfFpA6cZwJQC2TYJuMrlfXuTiYMU7zX0Y1rgMJ26GExGTyMP4JKLik4iKS2TdiTucvRPJ+NVn+PPQDT57tSI1vYqoHbYQL4T0uRFCmNJooOXnMLchnFkBdd8Cz1pqRyUyYKnXUiudpGXoy2VYcvAa/9scyNk7kXSdu582ldx5pYoHjcq5YG8lb6vCfD0aLWVmyU1sbCyKomBrmzI/xLVr11i5ciUVKlSgZcuWOR6gEGbFvXLK2lOXtkLMPbWjEdmg02roU8+LtpWLMXVLIH8evsHG08FsPB2MpU5LvTLONK/gRquKbsYJzYQwF8+aoTivyHJ0r776Kp07d+bNN98kPDycOnXqYGFhQVhYGNOmTWPYsGG5EacQ5qPlZGjzNVjZqx2JeA7O9lZM6VyFPnW9WHX8FlvPhhAUFs2uCymjryatOUOriu70rlOSemWc0z2GoijEJxmIjE0kMi6JyLhEEpMMONpY4GRrgZONJdYWWjQa6dcj8gaz7XNz9OhRpk+fDsBff/2Fm5sbx44d4++//2b8+PGS3AjxLLbSP8OcVPBwpIKHIx+1Lc+luw/ZejaETWeCOXEjnPWn7rD+1B1KF7Wjaw0PLt/RcGT9eW48iOXa/RhuPoglIenpq8Zb6rW42FtRvLANJQrbUKKwbcpPJxvcC1nj4WQjkw+KF+bRaCkzS25iYmJwcHAAYMuWLXTu3BmtVkvdunW5du1ajgcohNkyGOD0X5CcANVeVzsakQPKutpT1tWeYY3LcO5OJIsPXmPl0VtcCYvmm80XAR1cvZ5mP40GHKz0ONpYYKHTEhmbSHhsIskGhYQkA7fCY7kVHsuhoPTPW9jWAvdCNhSxs8DR+r+bjZ4idlY0K+/KS24OufvARYGQlGzgYfzTZyjOK7IcXdmyZVm1ahWdOnVi8+bNjB49GoC7d+/i6OiY4wEKYbbOr4UVg8G6EJRrKy06ZqZ8MUc+71iZD9uUZ83x26w/eZuoB6HUrlCa0i4OeDnb4lnEFidbC+ws9WifGFKuKArRCck8iE7gblQ8Nx+ktPSk3GK4HR7LnYg4YhKSeRCTyIOYxHTj+HrTeSp6ONKpWnE6VPWQfkAi21ITGwAHc7ssNX78eHr16sXo0aNp1qwZ9erVA1JacapVq5bjAQphtnxfAbdKEHIadn0Dbb5SOyKRC+yt9PSqU5Ju1YuxYcMG2rZ6CQuLZ38waDSalIkDrfR4FrGlRqnCacooikJkbBK3I2IJjogjIjaRyLhEouKSiIxN5HLoQ3ZdCOXM7UjO3I5kysbzvOxTlEENS+Nfxln68ogsSR0pZWOhy/Mzd2c5uenatSsNGjTgzp07+Pn5Gbc3a9aMTp065WhwQpg1rS6lc/GiTnB4HtQeDM5l1I5K5CMajYZCthYUsrWgfLH0W87vRyew/uRtVhy7xbHr4ewMDGVnYCiVixdiaKPStKlUTCYiFJmSX0ZKAWQr9XJ3d6datWpotY92r127Nr6+vjkWmBAFQpmmULYFGJJg2wS1oxFmqIidJX3qebHyrfrseLcR/f29sLbQcupWBCOWHKPptwH8fuAasQnJaocq8rj8MlIKstFy06lTp3SbMjUaDdbW1pQtW5ZevXpRrly5HAlQCLPXcjJc3g7n1sK1fVDKX+2IhJkq7WLPxA4VGdnMh4X7rrJw/1Wu3Yvhk1Wn+XZLIH3qlqJPPS9cHGTdLJFWfpmdGLLRclOoUCF27NjB0aNH0Wg0aDQajh07xo4dO0hKSmLp0qX4+fmxd+/e3IhXCPPjWh6q9035ffPHoCjqxiPMXhE7S0a3eIl9HzZlQvsKeBax4UFMIt/vuET9r3cw9q+TXLobpXaYIo+JMLbc5P3LUlmO0N3dnV69ejFr1izjZSmDwcA777yDg4MDf/75J2+++SZjx45lz549OR6wEGapycdw7zI0/jBlXLAQL4CtpZ4B9b3pU7cUW86G8NM/Vzh+I5yl/95g2ZEbtKtcjLeb+lDOXYaSi0cdigvlg5abLCc3v/zyC3v37jXpb6PVann77bfx9/fnyy+/ZMSIETRs2DBHAxXCrNm7Qv91akchCii9TkvbysVoU8mdI9ce8OM/V9h6NoR1J++w7uQdWld05+1mZanoUUjtUIWKzPqyVFJSEufPn0+z/fz58yQnp3RIs7a2liGGQjyPpHi1IxAFkEajoaZXEeb1rcnGdxrSrnIxNBrYdCaYdt/v4f3lJ4iKS38+HWH+8lOH4iwnN3369OGNN95g+vTp7Nmzhz179jB9+nTeeOMN+vZN6Tewa9cuKlasmOPBCmH2khJg5xSYUQWiw9SORhRg5Ys5Mrt3dTaPepkOfh5oNLD8yE3afLebQ0H31Q5PqODR0gtm2Odm+vTpuLm58c033xASEgKAm5sbo0ePZuzYsQC0bNmS1q1b52ykQhQEWj1c2AgPgyFgCrT7Vu2IRAH3kpsD3/esxut1SzFm2XFuPoil+0/7GfpyGUa38MFKL+taFRRm3XKj0+n4+OOPuXPnDuHh4YSHh3Pnzh0++ugjdLqUJ3nJkiUpUaJEjgcrhNnTaqHlFym///srhF5QNx4h/lPbuwgb32lItxolUBSYu+syHWfvIygsWu3QxAti1n1uHufo6CjrSQmR07wbpqw1pSTD1vFqRyOEkYO1Bf/r5sfc12tQxM6Sc3ci6fzDXg5flctUBUHqaCmzbLkJCQmhT58+eHh4oNfr0el0JjchRA5o8RlodCmXqK7sUjsaIUy0ruTOpnca4leiEA9iEuk97yBrT9xWOyyRy1JbbhzMcZ6b/v37c/36dT799FOKFSsmo6KEyA1FfaDmwJQ1p7Z8AkN2pVyyEiKPcHW05s8h9Xjnz2NsORvC238c48aDGIY1KiOfC2YqdRI/s5znZs+ePezevZuqVavmQjhCCKPGH8LJpRB6HoJPgEc1tSMSwoSNpY45r9fgi/XnmL83iG82BXLjfgyfd6wsi3GamcRkAzH/rT9mlsmNp6cnikwPL0TusysKneeBqy8U9lI7GiHSpdNqGN++AqWcbZm09gx/HLoBaPiyUyVpwTEjqa02kD8uS2W5nXvGjBl8+OGHXL16NRfCEUKYKNdaEhuRL/Tz92Jmz+poNPDHoetM33ZR7ZBEDkodBm5vpUevy/uXyLOcfnXv3p2YmBjKlCmDra0tFhamzVP370uveSFyxc0j4OSZslSDEHlQuyrFCI+txMcrT/P99ou42FvSp56X2mGJHJCf+ttANpKbGTNm5EIYQoin2vU/2Pk51OgP7b9TOxohMtS7TinCohKYvu0C49ecwdneiraVi6kdlnhOxhXBzTW56devX44GMHv2bP73v/8RHByMn58fM2fOpHbt2hmWDw8P5+OPP2bFihXcv3+fUqVKMWPGDNq2bZujcQmRp3g3hJ3A0d+g9lBwq6B2REJkaGSzsoQ+jOP3A9cZ9edxnGws8C9bVO2wxHN41HKT9/vbQCb73ERGRpr8/rRbVixdupQxY8YwYcIEjh49ip+fH61ateLu3bvplk9ISKBFixZcvXqVv/76i8DAQObNm0fx4sWzdF4h8p2SdaF8B1AMsPVTtaMR4qk0Gg2TOlSiTSV3EpINDFl0hMDgKLXDEs8hMp9dlspUclO4cGFjwuHk5EThwoXT3FK3Z8W0adMYPHgwAwYMoEKFCsydOxdbW1vmz5+fbvn58+dz//59Vq1aRf369fHy8qJRo0b4+fll6bxC5EstJoHWAi5tg0vb1Y5GiKfSaTVM716V2t5FeBifxBsLDxP2UFa7z6/Mss/Njh07KFKkiPH3nBjel5CQwJEjRxg3bpxxm1arpXnz5uzfvz/dfdasWUO9evUYPnw4q1evxsXFhV69ejF27NgMZ0eOj48nPv7RCyq1dSkxMZHExMR09xG5I7W+pd6zycETbc2B6A79iLLlE5I864M287OCS/2rqyDWvw6Y1aMK3X48xLX7MQxeeJhFA2piZfHiZ7MviPWfkx5Ep3yOOljpslWHOVH/WdlXo6g0ac3t27cpXrw4+/bto169esbtH3zwAbt27eLgwYNp9vH19eXq1av07t2bt956i0uXLvHWW28xcuRIJkyYkO55Jk6cyKRJk9JsX7JkCba2tjn3gIR4ASySHtL87PtYJkdzzHMg14s2VjskIZ4pJBamn9IRm6yhRlEDfcoakClw8pcll7QcDNXS1jOZViXUmesuJiaGXr16ERER8cx1LbPcM8jHx4fevXvTu3dvfHx8sh1kdhgMBlxdXfnpp5/Q6XTUqFGDW7du8b///S/D5GbcuHGMGTPG+HdkZCSenp60bNlSFv18wRITE9m6dSstWrRIM4WAyDyt2z2U/TOpXKMOlSpmviO91L+6Cnr9l6t6jzd+O8qRMC31q/jwdpMyL/T8Bb3+n9f6P45D6F1q+VWkbZ2SWd4/J+o/K/16s5zcvPXWWyxZsoTJkydTvXp1Xn/9dbp37467u3uWjlO0aFF0Oh0hISEm20NCQjI8VrFixbCwsDC5BFW+fHmCg4NJSEjA0tIyzT5WVlZYWVml2W5hYSFPcJVI3T+num9CrYHoreyztbvUv7oKav038nVncsdKjFtxiu93XKasmyMd/DxeeBwFtf6fV1RcytILReytn6v+nqf+s7JflqcZHD16NIcPH+bcuXO0bduW2bNnG1tCfvvtt0wfx9LSkho1arB9+6OOkQaDge3bt5tcpnpc/fr1uXTpEgaDwbjtwoULFCtWLN3ERgizpLeEbCY2QqipZ+2SDGrgDcB7y09w5JpM+ppf5Ld5brI9h/JLL73EpEmTuHDhArt37yY0NJQBAwZk6Rhjxoxh3rx5LFy4kHPnzjFs2DCio6ONx+nbt69Jh+Nhw4Zx//593nnnHS5cuMD69ev58ssvGT58eHYfhhD5l8EAJ5fD9s/UjkSITBvXtjzNy7uRkGRg8G9HuHYvWu2QRCaY5WipjBw6dIglS5awdOlSIiMj6datW5b27969O6GhoYwfP57g4GCqVq3Kpk2bcHNzA+D69etotY/yL09PTzZv3szo0aOpUqUKxYsX55133mHs2LHP8zCEyJ/unoEVgwANVOgIxaqoHZEQz6TTavi+Z1Ve+3E/p29FMuDXw6x4yx8nW2l9z8vy2zw3WU5uLly4wOLFi/njjz8ICgqiadOmfP3113Tu3Bl7+6w3lY8YMYIRI0ake19AQECabfXq1ePAgQNZPo8QZse9MlTqAqf/hi0fQ981yBAUkR/YWuqZ368WHWfv5UpYNEMWHWHRG7Wx0r/4IeLi2ZINClHxSUD+SW6yfFnK19eXTZs2MXz4cG7evMnmzZvp27dvthIbIcRzajYBdJYQ9A8EblA7GiEyzdXRmvkDamFvpedQ0H0+/PsUKs1MIp4htdUGwNHaTJObwMBADh48yDvvvGO8fCSEUEnhUlDvvz5nmz+GJJkBVuQfvu6O/NC7OjqthpXHbjF75yW1QxLpiIxLSW5sLHRY6rPdVfeFynKUL3puGyHEMzR8F+zd4UEQ7J+tdjRCZMnLL7kw+dVKAEzbeoF9l8NUjkg8Kb91JoZsJDfJyclMnTqV2rVr4+7uTpEiRUxuQogXzMohZd0pgN3fQuwDdeMRIot61SlJ1xolMCjwzp/HCY2SFsi8pEAkN5MmTWLatGl0796diIgIxowZQ+fOndFqtUycODEXQhRCPFPl16BaH+i1FGyytoCtEHnB5Fcr8ZKbPaFR8Yxaeoxkg/S/ySsKRHKzePFi5s2bx7vvvoter6dnz578/PPPjB8/XkYxCaEWrRZenQVeDdSORIhssbHU8UPv6thY6Nh76R6zdkj/m7wiv03gB9lIboKDg6lcuTIA9vb2REREAPDKK6+wfv36nI1OCJE9kXdSJvkTIh8p6+rAF51S+t/M2H6BfZek/01eUCBabkqUKMGdO3cAKFOmDFu2bAHg8OHD6a7hJIR4wfbPhpnV4cQStSMRIss6Vy9B95qeKAqM/PM4d6Pi1A6pwHvUcvNc8/6+UFlObjp16mRcD+rtt9/m008/xcfHh759+zJw4MAcD1AIkUWKARJjYNtEiMv8KrpC5BUTO1SknJsDYQ/jGfXncel/o7LI2Pw1gR9kY4bir776yvh79+7dKVmyJPv378fHx4f27dvnaHBCiGyoPRSOLIR7F+Gfb6Dl52pHJESW2FjqmN27Ou1n7mHf5Xv8sPMSbzeTaUjUkt+WXoDnWDgzVb169RgzZowkNkLkFXpLaD0l5fcDcyDsorrxCJENZV3t+bxjSv+b6dsucPDKPZUjKrjMus/NhQsXOHTokMm27du306RJE2rXrs2XX36Z48EJIbLJpwX4tAJDEmwap3Y0QmRLlxol6FI9Zf6bkX8e495Dmf9GDWad3IwdO5Z169YZ/w4KCqJ9+/ZYWlpSr149pkyZwowZM3IjRiFEdrSeAloLuLQVLmxWOxohsuWzVytSxsWOkMh43l1+AoP0v3nhzDq5+ffff2nTpo3x78WLF/PSSy+xefNmvvvuO2bMmMGCBQtyI0YhRHY4l4G6w1IW1rwfpHY0QmSLnZWeWb2qY6XXEhAYyrzdV9QOqcAx6+QmLCyMEiVKGP/euXOnST+bxo0bc/Xq1RwNTgjxnF5+H946AHXfVDsSIbKtfDFHJrSvCMA3mwM5ck2WGHlRDAbFuHCmWU7iV6RIEeP8NgaDgX///Ze6desa709ISJDl6oXIa6wdU1pwhMjnetb25JUqxUg2KIz84xjhMQlqh1QgPExIIvWj3Sxbbho3bszkyZO5ceMGM2bMwGAw0LhxY+P9Z8+excvLKxdCFELkBM2to5QNWffsgkLkQRqNhimdK1PK2ZZb4bG8t/ykfKF+ASJiUlptLPVarC10KkeTeZlObr744gvOnz9PqVKlGDt2LN988w12dnbG+xctWkTTpk1zJUghxHN6cBXdwtZUvL0Mza2jakcjRLY4WFswu1d1LHVatp0LYf7eq2qHZPbyY38byEJy4+Xlxblz5zh27BjXrl1j2LBhJvdPmjSJTz75JMcDFELkgMJeKJW6AaDdMk7WnRL5VqXihfi4XXkAvtp4jhM3wtUNyMzlxwn8IIuT+On1evz8/PDw8Ehzn5+fH87OzjkWmBAiZyU3+ZQkrTXa20fg5FK1wxEi2/rWK0Xriu4kJisMX3LU2Logcp7Zt9wIIfI5B3cC3Tuk/L5tAsRHqRuPENmk0Wj4umsVPIvYcPNBLGP/kv43uUWSGyFEnnfFpRVKYW94GAL/TFU7HCGyrZCNBbN6VsdCp2HTmWCWHLqudkhmybgiuHX+WREcJLkRokAxaC1Ibj455Y/9s2VyP5Gv+Xk68UErXwAmrzvLxRBpjcxpqXPcmGXLTefOnYmMjATgt99+Iz5e1vcQIr9SfFpBlR7Q7ltwKql2OEI8lzcaeNPQpyhxiQbe/uMYcYnJaodkVsz6stS6deuIjo4GYMCAAURERORqUEKIXKTRQOcfoUY/0OafeSuESI9Wq+Hb1/woam/J+eAovtp4Xu2QzEpEbBKQv2YnBsjURTRfX1/GjRtHkyZNUBSFZcuW4ejomG7Zvn375miAQohclhADOouUmxD5kKuDNf/r5seAXw+zYN9VXn6pKE193dQOyyzk15abTCU3c+fOZcyYMaxfvx6NRsMnn3yCRqNJU06j0UhyI0R+cn4DbHgf6r0F9YarHY0Q2daknCsD63szf28Q7y0/yaZ3GuLqaK12WPlefk1uMnVZyt/fnwMHDhAaGoqiKFy4cIEHDx6kud2/fz+34xVC5KToUIi8CQFfwcNQtaMR4rmMbVOO8sUcuR+dwJhlJzAYZHj48yoQk/gBBAUF4eLikhuxCCFetGqvQzE/iI+EHZ+pHY0Qz8VKr2Nmz2rYWOjYcymMH/+5onZI+Z5xKLi5JzelSpUiIiKCb7/9lkGDBjFo0CCmTZsmnYyFyI+0OmjzTcrvRxfB7WPqxiPEcyrras/EDhUA+HZLIMeuP1A5ovxLUZSC03Lz77//UqZMGaZPn879+/e5f/8+06dPp0yZMhw9KgvyCZHvlKwLlV8DFNg4FmSmV5HPvVbTk1eqFCPJoPD2H8eMc7WIrIlJSCbpv0t7Zp/cjB49mg4dOnD16lVWrFjBihUrCAoK4pVXXmHUqFG5EKIQIte1mAQWdnDjIJxarnY0QjwXjUbDl50rG5dn+GjFKVmeIRtSL0nptRpsLfPXtBHZarkZO3Ysev2jgVZ6vZ4PPviAf//9N0eDE0K8II4e0HBMyu83DqkbixA5wNHagu97VEOv1bDu5B3+OnpL7ZDyncdHSqU3Qjovy3Jy4+joyPXradfwuHHjBg4ODjkSlBBCBfVGQP/10E7WnBLmoVrJwrzbshwAn60/T3CMygHlM/l1GDhkI7np3r07b7zxBkuXLuXGjRvcuHGDP//8k0GDBtGzZ8/ciFEI8SJYWINXA7WjECJHDX25tHF5hl8v6IhNkOUZMiu/jpSCTE7i97ipU6caJ+tLSkqZltnCwoJhw4bx1Vdf5XiAQggVRN6Bs6ug7jC1IxHiuaQuz9Duu90EP0xgwrpzTHutar67zKKG/JzcZLnlxtLSku+++44HDx5w/Phxjh8/bhwxZWVllRsxCiFepLgI+KEObPoQLu9QOxohnpurgzXTX6uCBoWVx26z7N8baoeUL+TXYeCQjeQmla2tLZUrV6Zy5crY2trmZExCCDVZFwK//y4xb/wQkmUYrcj/6ngXoV1JAwDjV5/h7O1IlSPK+x4lN1m+yKO6bCc3Qggz1vhDsCkCYYFw+Be1oxEiRzTzUGj0UlHikwwMX3KUKJn/5qkKVIdiIUQBYFMYmn2a8nvAlxAdpm48QuQArQb+16USxZ1sCAqLZuzfJ2X+m6eQ5EYIYX6q9wP3yil9cLZPUjsaIXJEYVtLZvWqhoVOw4ZTwczfe1XtkPIsSW6EEObHZN2p3+DOCXXjESKHVCtZmI/algfgyw3nOHDlnsoR5U35ObnJVi+hixcvsnPnTu7evYvBYDC5b/z48TkSmBAiDyjlD40+hJDTKauHp1IUkKG0Ih/r7+/FiRvhrDp+mxFLjrLu7Ya4F7JWO6w8xTgU3LoAJDfz5s1j2LBhFC1aFHd3d5O5AjQajSQ3QpibJuPAkPwooXl4F/7oAU0+grLN1Y5OiGzRaDRM6VyF88FRnA+OYtjiI/w5pC5W+vy1hlJuioxLmcuuQMxz8/nnn/PFF18QHBzM8ePHOXbsmPEmq4ILYaa0/73hKwrs/hZuHYHfu8CfveHBVVVDEyK7bCx1/NinBo7Weo5dD+eztWfVDilPyc+XpbKc3Dx48IBu3brlRixCiLxMo0m5Nfk4ZR0qrR7Or4PZdSDgK0iMVTtCIbKslLMd3/WshkYDiw9eZ9lhmeAPIC4xmYSklG4nhWwLQHLTrVs3tmzZkhuxCCHyA2tHaPUFvLkXvF+GpDgImAKza8P5DWpHJ0SWNSnnyujmLwHwyerT/Hv1vsoRqS+11UarAXvL/DeJX5YjLlu2LJ9++ikHDhygcuXKWFiYZnQjR47MseCEEHmYqy/0XQNnV8PmjyH8OgTtAt+2akcmRJaNaFKWU7ci2Ho2hDcW/stfb9bDx81B7bBU8/i6Ulpt/hs8kOXk5qeffsLe3p5du3axa9cuk/s0Go0kN0IUJBoNVOwIPi1g3yyoM/TRfRG3UpZysLJXLTwhMkur1fB9j2r0+vkAx66H02/+IVa8Vb/AjqDKz/1tIBvJTVBQUG7EIYTIzyztoPHYR38rCqwcCvcuQ8vJUKmLDB0XeZ6NpY5f+tWi65x9XAmLpt/8Qyx7s16+/YB/HhEx+XcYODznJH6KosjU1UKItB7ehYgbEHUb/n4DFraHEBmJIvK+InaWLBxYGxcHKwJDohjy27/EJSarHdYLFxmXv1tuspXc/Pbbb1SuXBkbGxtsbGyoUqUKixYtyunYhBD5lYMbvHUwZWSV3hqu7oa5DVJWGY8NVzs6IZ7Ks4gtCwfUxsFKz8Gg+4xeepykZMOzdzQj+f2yVJaTm2nTpjFs2DDatm3LsmXLWLZsGa1bt+bNN99k+vTpuRGjECI/srCGRh/A8ENQvj0oyXBwDsyqCXfPqR2dEE9VwcORH/vWwFKnZePpYD746yTJhoJzpeLxDsX5UZb73MycOZM5c+bQt29f47YOHTpQsWJFJk6cyOjRo3M0QCFEPle4FHT/HS5th41jU+bHcS6rdlRCPJN/maJ837Maw5ccZcWxW1jqtXzZqXK+HD2UVQWu5ebOnTv4+/un2e7v78+dO3dyJCghhBkq2wyG7YNef4LuvzfMpHjYPhliZF4RkTe1ruTOjO5V0Wrgz8M3mLDmTIHoa1rgkpuyZcuybNmyNNuXLl2Kj49PjgQlhDBTekso7PXo7wM/wO6pKRMA3pLlW0Te1N7Pg6nd/NBoYNGBa3y+/pzZJzjhMfk7ucnyZalJkybRvXt3/vnnH+rXrw/A3r172b59e7pJjxBCZMizDrj4Quj5lBFV3X+HMk3UjkqINDpXL0FCkoEPV5zilz1BWOm1fNDaV+2wcs2FkCgAvIraqhxJ9mS55aZLly4cPHiQokWLsmrVKlatWkXRokU5dOgQnTp1yo0YhRDmqpQ/DNoG3o0g4SEs7ganV6gdlRDp6lG7JJ+9WhGAHwIuM2vHRZUjyh0RsYncfJCyVlyFYo4qR5M92VowokaNGvz+++85HYsQoiCycoDey2HFEDi7Cv4aCLH3odYgtSMTIo2+9byIS0zmyw3nmbrlAjaWet5o4K12WDnq/J1IAIo72eBka6lyNNmTqZabyMhIk9+fdhNCiCzTW0HX+VDzDUCBLZ9C5G21oxIiXUNeLmNcaHPyurMsPnhN5Yhy1tn/kpvyxfLv2lqZarkpXLgwd+7cwdXVFScnJzTpTKOuKAoajYbk5II3k6MQIgdoddDuW7B3g+I1wNFD7YiEyNDIZmWJSUzix11X+GTVaWwsdHSuXkLtsHLE2dspyU1+vSQFmUxuduzYQZEiRQDYuXNnrgYkhCjANBrTNaoAwi6Bk2dK644QeYRGo+HD1r7EJSSzcP813lt+Ap1Ww6tVi6sd2nM7F/xfcuNh5slNo0aNjL97e3vj6emZpvVGURRu3LiRs9EJIQq2e5dhfitwr5Qyksoq/zaTC/Oj0WiY0L4icYkGlv57g1FLj3M/OoEB9fNvH5zEZAMXgh8CUD4ft9xkebSUt7c3oaGhabbfv38fb+/s/UNnz56Nl5cX1tbW1KlTh0OHDmVqvz///BONRkPHjh2zdV4hRB4XeRsSY+FKQMpQ8egwtSMSwoRWq2FK58r0q1cKRYFJa8/y9abz+XYenMuhD0lINmBvpcezcP4cBg7ZSG5S+9Y86eHDh1hbW2c5gKVLlzJmzBgmTJjA0aNH8fPzo1WrVty9e/ep+129epX33nuPhg0bZvmcQoh8wrsh9F8LNkXg9rGUVpzw62pHJYQJrVbDxA4Veb9VOQDmBFzmg79O5svFNs891pk4Py8zkemh4GPGjAFSmuE+/fRTbG0fZXTJyckcPHiQqlWrZjmAadOmMXjwYAYMGADA3LlzWb9+PfPnz+fDDz9Md5/k5GR69+7NpEmT2L17N+Hh4Vk+rxAinyheA97YAos6wb1L8EtL6LMSXMurHZkQRhqNhuFNylLU3pJxK06x/MhN7kUnMKtXNWwtszXriirMoTMxZCG5OXbsGJDScnPq1CksLR+Nfbe0tMTPz4/33nsvSydPSEjgyJEjjBs3zrhNq9XSvHlz9u/fn+F+n332Ga6urrzxxhvs3r37qeeIj48nPj7e+HfqcPXExEQSExOzFK94Pqn1LfWujnxd/4W8oO969H++hib0PMr81iS9vhrcKqodWabl6/o3Ay+q/jtXLUYhKx3vLDvJjvN36f7jfn7sXQ0Xh/zRIf7MrQgAXnK1y9G6yon6z8q+mU5uUkdJDRgwgO+++w5Hx+fP6sLCwkhOTsbNzc1ku5ubG+fPn093nz179vDLL79w/PjxTJ1jypQpTJo0Kc32LVu2mLQ+iRdn69ataodQoOXn+rdwH0ndmGmgKOw7dIEwjlNYV1jtsLIkP9e/OXhR9T/MF+ad13HqViTtZgQwpHwyHnn8I0dR4MR1HaDh/pWTbLh7MsfP8Tz1HxMTk+myWW4rmzFjBklJSWm2379/H71enyNJT0aioqLo06cP8+bNo2jRopnaZ9y4ccZLapDScuPp6UnLli1zNVaRVmJiIlu3bqVFixZYWOTPxdjyM7Op/8RXICmehDt7+O7Qd4yvM55XvF9RO6pnMpv6z6fUqP9X7sUweNFRgu7FMOu8FTO7+9HQJ3OfXWoIjowj+sA/6LQa+ndqhbWFLseOnRP1n5WJgrOc3PTo0YP27dvz1ltvmWxftmwZa9asYcOGDZk+VtGiRdHpdISEhJhsDwkJwd3dPU35y5cvc/XqVdq3b2/cZjCkdNjS6/UEBgZSpkwZk32srKywskrbHGhhYSFvMCqRuldXvq9/i0IAHD15lCRDEuP3jyfq4kb6tvtJ5cAyJ9/Xfz73Iuu/rHshVg6vz5BFRzgUdJ/Bvx/js1cr0rtOqRdy/qy6GHofgNJF7XCwzfoAocx4nvrPyn5ZHi118OBBmjRJu2pv48aNOXjwYJaOZWlpSY0aNdi+fbtxm8FgYPv27dSrVy9NeV9fX06dOsXx48eNtw4dOtCkSROOHz+Op6dnVh+OECKf+qz+Z7xeojkA/wvbz4y/u6AY8t/oFGHenGwtWfRGbTpXK06yQeHjlaeZsPo0iXlwJJWxM3E+nrwvVZaTm/j4+HQvSyUmJhIbG5vlAMaMGcO8efNYuHAh586dY9iwYURHRxtHT/Xt29fY4dja2ppKlSqZ3JycnHBwcKBSpUomnZyFEOZNq9HyQZNveadIDQB+eXiBCUtbkpQYp3JkQpiy0uv49jU/xrRIWY9q4f5r9P75IGEP45+x54t17k4UkP9HSkE2kpvatWvz009pm3/nzp1LjRo1shxA9+7dmTp1KuPHj6dq1aocP36cTZs2GTsZX79+nTt37mT5uEII86fRahnUfgETi7dCqyisTAhhzB/NiIt9oHZoQpjQaDSMbObDvL41sbfScyjoPh1m7uH0f6OT8oJHC2bm/+Qmy31uPv/8c5o3b86JEydo1qwZANu3b+fw4cNs2bIlW0GMGDGCESNGpHtfQEDAU/ddsGBBts4phDAfXZpPxWmfCx9cWMROIhm6tDkzO63CsZBcqhZ5S4sKbqwa7s/g344QFBZNlzn7+LpLFTpWU3dNquj4JK7eiwbMI7nJcstN/fr12b9/P56enixbtoy1a9dStmxZTp48KbMFCyFU08x/LHOrjsHeoHBUk8CAzQMIjUm7VIwQaivr6sCq4fVpUs6F+CQDo5Ye58sN50g2qLdkw/ngKBQFXB2s8s2cPE+TrWkTq1atyuLFi3M6FiGEeC61qg7kV5uivHn4Cy7EhtB3Y19+avETno7SgiPylkI2FvzcrxbTtgYye+dlfvrnCufuRDKzZzWcbF98/9HUS1Lm0JkYstFy87i4uDgiIyNNbkIIoSbfch1Y1OEvStiX4ObDm/TZ0JtzF9aqHZYQaei0Gt5v5cvsXtWxsdCx+2IYr87ey4WQqBceS+pIKXO4JAXZSG5iYmIYMWIErq6u2NnZUbhwYZObEEKozdPRk0VtF+Hr5MO9+Af02TuO6X93ISJCFt0UeU+7KsX4e5g/JQrbcO1eDJ1m72XzmeAXGkPqgpnmMFIKspHcvP/+++zYsYM5c+ZgZWXFzz//zKRJk/Dw8OC3337LjRiFECLLitoUZX6TmfhjQ7xWw/yHF2izoi0/r+1PbMx9tcMTwkQFD0fWjGhAvdLORCck8+bvR5i76zKKkvv9cJINCueDC/hlqbVr1/LDDz/QpUsX9Ho9DRs25JNPPuHLL7+UfjhCiDzFwbE4c/scYFa5AfgYtERpNXx3/wht/3yZpZvfJjEhWu0QhTAqYpcy4V+fuqVQFPhq43k++OskCUm5O+FfUFg0cYkGrC20eDnb5eq5XpQsJzf379+ndOnSADg6OnL/fso3oAYNGvDPP//kbHRCCPGcNFotjeqOYXmfw0wp1ZHiyRCm0/B5cACvrmjLhisbMCh5b7ZYUTDpdVomd6zExPYV0Gpg+ZGb9PnlIA+iE3LtnKmXpHzdHdFpNbl2nhcpy8lN6dKlCQoKAlKWQ1i2bBmQ0qLj5OSUo8EJIURO0ekteaXxZNb2PsBHbo1wRseN+PuM3T2W19a+xu4Lq2T5BpFn9K/vzS/9a2Fvpedg0H06/bCXS3cf5sq5Vh67BUCl4uZxSQqykdwMGDCAEydOAPDhhx8ye/ZsrK2tGT16NO+//36OByiEEDnJwsqOnq1nsaHXPt6u9jb2FvYEPgjkrf2f0v+3Whw7+bvaIQoBQJNyrvw9zJ/iTjZcvRfDq7P2sP5kzs7Yv/1cCDvO38VCp2FAfe8cPbaaspzcjB49mpEjRwLQvHlzzp8/z5IlSzh27BjvvPNOjgcohBC5wdbCliFVhrCx80YGlGiO1X+T//U99jVv/+bPhUsb1Q5RCMq5O7B6RH3qli5CdEIyw5ccZfK6szmy8GZcYjKT1p4FYGADb8q42D/3MfOKLCU3iYmJNGvWjIsXLxq3lSpVis6dO1OlSpUcD04IIXKbk7UTY5pNZ12b3+liWQydohCgRNF1z/uMW9yEGzf2qx2iKOCK2lvx+xt1eLNRGQB+2RNEz58OEBL5fIvEzvvnCtfvx+DmaMXbTX1yItQ8I0vJjYWFBSdPnsytWIQQQjXu7lWZ2HMLKxvPoqXOCUWjYV1SGB22D+bL3R8TFhumdoiiANPrtHzYxpcf+9TAwUrPv9ce0O773aw5cTtbw8VvPohhdsAlAD5qWx57q2wtWJBnZfmy1Ouvv84vv/ySG7EIIYTqvL0a8+3ru/mz9kTqYUOSRsMfV9bQdkVbZh6bSVSszJEj1NOqojtr3m6Ar7sDYQ8TGPnHMV77cX+WVxf/Yv054hIN1PYuQgc/j1yKVj1ZTtWSkpKYP38+27Zto0aNGtjZmY6JnzZtWo4FJ4QQaqlYvgs/le/CgRv/8N2JOZy+d5qfTv7E0uM/Mti1Ht2bfoO1jczKLl4876J2rBpen5/+ucIPAZc4fPUB7WftoXtNT95rVY6i9k9f+HL3xVA2ng5Gp9UwqUNFNBrzGP79uCwnN6dPn6Z69eoAXLhwweQ+c6wgIUTBVtfzZeqUaMiO6zv4fs+nXEmKYmrYARb90ZC3SramQ6PP0VtYqx2mKGCsLXSMbOZD1xol+HrTeVYfv82fh2+w6vgtWlV0p2O14jQsWxS97tEFmrjEZI7fCGfCmjMA9KlbymzWknpSppObK1eu4O3tzc6dO3MzHiGEyHM0Gg3NSjWjkUd91v4znh+ubSBYp2HCrc38umgLA0u1pla5LhQvXlu+5IkXysPJhu96VKNP3VJMWnuWU7ciWH38NquP36aovSXt/TxwsLbg4JV7HLsRbpzt2NnOktEtXlI5+tyT6eTGx8eHO3fu4OrqCkD37t35/vvvcXNzy7XghBAiL9FbWNOp2Te0jfuYpTve5+eQfVzVwfibG+HmRpytnfFz8cPP1Q8/nQMVvZrLpSvxQtT0KsKaEfU5cTOCVcdusfbEbcIeJvDr3qsm5VwcrKjjXYQ3G5WhkI2FOsG+AJlObp7sjb1hwwamTJmS4wEJIUReZ2VdiL5tf6Jz1B0W7/iAf+6f5qw2mXtx99hxYwc7buwAQH9wEr6KBX52JajkUo24uMIyC7LINRqNhqqeTlT1dOLjduXZczGMtSdvYzAo1PZ2pm7pIngXtSsQrYvmNfZLCCFeIHuHYgx9dRFDgfikOM7eP8eJuyc4fmsvJ27tJ0yn4bQmidOxV+H6VQB+WvwLfo7e+JXvip+LH+Wdy2Ole3oHUCGyykKnpYmvK018XdUORRWZTm40Gk2abK8gZH9CCJEZVnprqrlWo5prNajUH8Vg4PadfzlxaT0nQo5wIvomgZok7uo0bI2+ytZ/pwJgodVTPkmDn70nfu618HupPe5ufio/GiHytyxdlurfvz9WVinfMOLi4njzzTfTDAVfsWJFzkYohBD5kEarpXjx2hQvXpu2pMzwvnbtMop7xXE6OZwTUdc4EXqC+3H3OamFkzFXWHTlClxZinuygp9lEfyKlMfPtzOlitdN+TKZlACJsRmf1NIOdP+9rT+rrIUt6P/rc5GUCIkxmSubnAQJ0U8pawN6y8yV1VuDxaNWK71Gj62FbcblhcikTCc3/fr1M/n79ddfz/FghBDCnOl09lSv/Bp1LFISBUVRuBl8nOPn/05p3Ym9zQVNMsE6DcHJD9gcug9C96kc9YvTxrsN37z8jdphCDOQ6eTm119/zc04hBCiwNFoNHgWq4ZnsWq0/29bzMO7nL64hhM3/uFE+EVOWGgJT4hUNU4h8hvpUCyEEHmIrb0rtasNona1QUBK606SkpRypyEZlKeMttLoQPvfpG0GAyjJKpTVglaX9bKABunHKXKGJDdCCJGHaTQaLDT/9XfRZmFekqysHJgXygqRg+SpJ4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcxKnkhuZs+ejZeXF9bW1tSpU4dDhw5lWHbevHk0bNiQwoULU7hwYZo3b/7U8kIIIYQoWFRPbpYuXcqYMWOYMGECR48exc/Pj1atWnH37t10ywcEBNCzZ0927tzJ/v378fT0pGXLlty6desFRy6EEEKIvEj15GbatGkMHjyYAQMGUKFCBebOnYutrS3z589Pt/zixYt56623qFq1Kr6+vvz8888YDAa2b9/+giMXQgghRF6kV/PkCQkJHDlyhHHjxhm3abVamjdvzv79+zN1jJiYGBITEylSpEi698fHxxMfH2/8OzIyEoDExEQSExMzPG5ycjJJSUkoipKpOMSzJSUlodfrefjwIXq9qk+9Aimr9a/RaNDr9eh0uhcQnflLfb952vuOyD1S/+rKifrPyr6qfsKEhYWRnJyMm5ubyXY3NzfOnz+fqWOMHTsWDw8Pmjdvnu79U6ZMYdKkSWm2b9myBVtb23T3cXBwwMHBAa1W9YYts+Pu7s6VK1fUDqPAymr9GwwGoqKiiIqKysWoCpatW7eqHUKBJvWvruep/5iYmEyXzddfn7/66iv+/PNPAgICsLa2TrfMuHHjGDNmjPHvyMhIYz8dR0fHNOVDQkKIjIzExcUFW1tbNBpNrsVf0CiKQnR0NHZ2dlKvKshq/SuKQkxMDKGhobz00ktpvoSIrElMTGTr1q20aNECCwsLtcMpcKT+1ZUT9Z965SUzVE1uihYtik6nIyQkxGR7SEgI7u7uT9136tSpfPXVV2zbto0qVapkWM7KygorK6s02y0sLNJUcHJyMlFRUbi5ueHs7JyFRyIyw2AwkJiYiI2NjbSKqSA79W9nZ4dWq+Xu3bsUK1ZMLlHlgPTee8SLI/Wvruep/6zsp+onjKWlJTVq1DDpDJzaObhevXoZ7vfNN98wefJkNm3aRM2aNXMsntTreRldrhKiIEp9PUhfBSFEfqH6ZakxY8bQr18/atasSe3atZkxYwbR0dEMGDAAgL59+1K8eHGmTJkCwNdff8348eNZsmQJXl5eBAcHA2Bvb4+9vX2OxCSXTIR4RF4PQoj8RvXkpnv37oSGhjJ+/HiCg4OpWrUqmzZtMl7fv379ukkT+pw5c0hISKBr164mx5kwYQITJ058kaELIYQQIg9SPbkBGDFiBCNGjEj3voCAAJO/r169mvsBiWc6f/48/fv35/jx4/j6+nL8+PFn7jNgwADCwsJYu3YtAI0bN6Zq1arMmDEj1+IMCAigSZMmPHjwACcnp1w7jxBCiLxDenWaif79+6PRaNBoNFhaWlK2bFk+++wzkpKSnvu4HTt2TLN9woQJ2NnZERgYmO0JFFesWMHkyZOfK74TJ07QoUMHXF1dsba2xsvLi+7du2c4w7UQQgjzlydabkTOaN26Nb/++ivx8fFs2LCB4cOHY2FhYTJJYmYlJyc/ta/F5cuXadeuHaVKlcp2vBlNvJhZoaGhNGvWjFdeeYXNmzfj5OTE1atXWbNmDdHR0c917GdJSEjA0tIyV88hhBAie6Tl5hkURSEmIUmVW1ZnR7ayssLd3Z1SpUoxbNgwmjdvzpo1awB48OABffv2pXDhwtja2tKmTRsuXrxo3HfBggU4OTmxZs0aKlSogJWVFQMHDmThwoWsXr3a2CoUEBCARqPhyJEjfPbZZ2g0GmNfp1OnTtG0aVNsbGxwdnZmyJAhPHz4MMN4GzduzKhRo4x/PyvGJ+3du5eIiAh+/vlnqlWrhre3N02aNGH69Ol4e3ublD1y5Ag1a9bE1tYWf39/AgMDjfddvnyZV199FTc3N+zt7alVqxbbtm0z2d/Ly4vJkyfTt29fHB0dGTJkCAB79uyhYcOG2NjY4OnpyciRI3M9sRJCCPF00nLzDLGJyVQYv1mVc5/9rBW2ltn/F9nY2HDv3j0g5fLSxYsXWbNmDY6OjowdO5a2bdty9uxZ49wBMTExfP311/z88884OztTrFgxYmNjiYyM5NdffwVSWlvu3LlD8+bNad26Ne+99x729vZER0fTqlUr6tWrx+HDh7l79y6DBg1ixIgRLFiwIFPxZibGx7m7u5OUlMTKlSvp2rXrU1uaPv74Y7799ltcXFx48803GThwIHv37gXg4cOHtG3bli+++AIrKyt+++032rdvT2BgICVLljQeY+rUqYwfP54JEyYAKUlR69at+fzzz5k/fz6hoaHG/mOp9SWEEOLFk+TGDCmKwvbt29m8eTNvv/22MWHYu3cv/v7+QMoCpJ6enqxatYpu3boBKfOY/PDDD/j5+RmPZWNjQ3x8vMmkiu7u7uj1euzt7Y3b582bR1xcHL/99ht2dnYAzJo1i/bt2/P1118/c3bbzMb4uLp16/LRRx/Rq1cv3nzzTWrXrk3Tpk3p27dvmvN98cUXNGrUCIAPP/yQdu3aERcXh7W1NX5+fiaPefLkyaxcuZI1a9aYdHRv2rQp7777rvHvQYMG0bt3b2Prk4+PD99//z2NGjVizpw5Gc6aLYQQIndJcvMMNhY6zn7WSrVzZ8W6deuwt7cnMTERg8FAr169mDhxItu3b0ev11OnTh1jWWdnZ8qVK8e5c+eM2ywtLZ862/PTnDt3Dj8/P2NiA1C/fn0MBgOBgYHPTG7OnTuXqRif9MUXXzBmzBh27NjBwYMHmTt3Ll9++SX//PMPlStXNpZ7/HEVK1YMgLt371KyZEkePnzIxIkTWb9+PXfu3CEpKYnY2FiuX79ucq4nJ4w8ceIEJ0+eZPHixcZtiqJgMBgICgqifPnyT33MQgghcockN8+g0Wie69LQi9SkSRPmzJmDpaUlHh4eWV5528bGJl9O2Obs7Ey3bt3o1q0bX375JdWqVWPq1KksXLjQWObxy1qpj9FgMADw3nvvsXXrVqZOnUrZsmWxsbGha9euJCQkmJzn8cQNUi5nDR06lJEjR6aJ6fHLWUIIIV6s/PGpLTLFzs6OsmXLptlevnx5kpKSOHjwoPGSz7179wgMDKRChQpPPaalpSXJycnPPHf58uVZsGCBcWFGSOnwq9VqKVeuXKb2z26MT8ZbpkyZLHXq3bt3L/3796dTp05AStKSmfmUqlevztmzZ9OtcyGEEOqR0VIFgI+PD6+++iqDBw9mz549nDhxgtdff53ixYvz6quvPnVfLy8vTp48SWBgIGFhYRmuL9S7d2+sra3p168fp0+fZufOnbz99tv06dMnU6tJZyfGdevW8frrr7Nu3TouXLhAYGAgU6dOZcOGDc98XE+ee8WKFRw/fpwTJ07Qq1cvY6vO04wdO5Z9+/YxYsQIjh8/zsWLF1m9enWGE1IKIYR4MSS5KSB+/fVXatSowSuvvEK9evVQFIUNGzY8c5XVwYMHU65cOWrWrImLi4txhNGTbG1t2bx5M/fv36dWrVp07dqVZs2aMWvWrFyLsUKFCtja2vLuu+9StWpV6taty7Jly/j555/p06dPps87bdo0ChcujL+/P+3bt6dVq1ZUr179mftVqVKFXbt2ceHCBRo2bEi1atUYP348Hh4emT63EEKInKdRsjqZSj4XGRlJoUKFiIiIwNHR0eS+uLg4goKC8Pb2lpEuucBgMBAZGYmjo6PJemHixchu/cvrImckJiayYcMG2rZt+8wvFSLnSf2rKyfq/2mf30+STxghhBBCmBVJboQQQghhViS5EUIIIYRZkeRGCCGEEGZFkhshhBBCmBVJboQQQghhViS5EUIIIYRZkeRGCCGEEGZFkhshhBBCmBVJbsxEaGgow4YNo2TJklhZWeHu7k6rVq3SLJfw+++/4+vri7W1NV5eXkyePDnNsa5evYpGozHenJ2dadmyJceOHcvw/AEBASb7pN6Cg4NNys2bN4/SpUtjbW1NnTp1OHTokMn9cXFxDB8+HGdnZ+zt7enSpQshISHPUTNCCCEKGkluzESXLl04duwYCxcu5MKFC6xZs4bGjRtz7949Y5mrV6/St29fOnbsyLlz51i2bBne3t4ZHnPbtm3cuXOHzZs38/DhQ9q0aUN4ePhT4wgMDOTOnTvGm6urq/G+pUuX8sknn/Dpp59y9OhR/Pz8aNWqFXfv3jWWGT16NGvXrmX58uXs2rWL27dv07lz5+xXjBBCiAJHr3YA4vmFh4eze/duAgICaNSoEQClSpWidu3aJuVSW1MGDhyIt7c33t7eaco8ztnZGXd3d9zd3Zk6dSr169fn4MGDtGrVKsN9XF1dcXJySve+GTNm0LdvXwYMGIBWq2Xu3LmsX7+e+fPn8+GHHxIREcEvv/zCkiVLaNq0KZCymGb58uU5cOAAdevWzWLNCCGEKIik5SazEqIzviXGZaFsbObKZoG9vT329vasWrWK+Pj4DMsVL16cmjVrMmLECOLi4jIslx4bG5uUcBMSnlquatWqFCtWjBYtWphcEktISODIkSM0btzYuE2r1dK8eXP2798PwJEjR0hMTKR58+bGMr6+vpQsWdJYRgghhHgWabnJrC89Mr7PpyX0Xv7o7/+VhcSY9MuWagAD1j/6e0ZliLmXttzEiEyHptfrWbBgAYMHD2bu3LlUr16dRo0a0aNHD6pUqWIsN3jwYBRFoXTp0rRp04bVq1cbV1Zt3749pUqVYtasWWmOHx4ezuTJk7G3t8+wpadYsWLMnTuXmjVrEh8fz88//0zjxo05ePAg1atXJywsjOTkZFxcXEz2c3Nz4/z58wAEBwdjaWmZpuXHzc0tTd8dIYQQIiPScmMmunTpwu3bt1mzZg2tW7cmICCA6tWrs2DBAgDOnj3LggULWLBgAXPmzKFkyZI0btzY2N/l9OnTNGzY0OSY/v7+2NvbU7hwYU6cOMHSpUtxc3NL9/zlypVj6NCh1KhRA39/f+bPn4+/vz/Tp0/P1ccthBBCPElabjLro9sZ36fRmf79/qWnlH0inxx1KvsxPcHa2poWLVrQokULPv30UwYNGsSECRPo378/J0+exMrKigoVKgAwf/58unfvTv369fnggw+IioqiQ4cOJsdbunQpFSpUwNnZOcN+NE9Tu3Zt9uzZA0DRokXR6XSEhoaalAkJCcHd3R0Ad3d3EhISCA8PNznf42WEEEKIZ5GWm8yytMv4ZmGdhbI2mSubAypUqEB0dEr/neLFixMfH8/BgwcB0Ol0LFmyhDJlyjBkyBA+/vhjY7+aVJ6enpQpUyZbiQ3A8ePHKVasGACWlpbUqFGDXbt2Ge83GAxs376devXqAVCjRg0sLCzYvn27sUxgYCDXr183lhFCCCGeRVpuzMC9e/fo1q0bAwcOpEqVKjg4OPDvv//yzTff8OqrrwLQoEED/P396d69OzNmzKBy5cqcOXOG27dvY2dnx5IlSxg6dCi2trbZimHGjBl4e3tTsWJF4uLi+Pnnn9mxYwdbtmwxlhk1ahQDBgygXr161K1blxkzZhAdHc2AAQMAKFSoEG+88QZjxoyhSJEiODo68vbbbxvLCyGEEJkhyY0ZsLe3p06dOkyfPp3Lly+TmJiIp6cngwcP5qOPPgJShoFv2rSJSZMmMWbMGG7dukXZsmUZMmQI3bp1o06dOvTu3Zu///47WzEkJCTw7rvvcuvWLWxtbalSpQrbtm2jSZMmxjLdu3fnxo0bTJw4keDgYKpWrcqmTZtM+vFMnz4drVZLly5diI+Pp1WrVvzwww/PV0FCCCEKFI2iKIraQbxIkZGRFCpUiIiICONIoVRxcXEEBQXh7e2NtbV1BkcQ2WUwGIiMjMTR0RGtVq6IvmjZrX95XeSMxMRENmzYQNu2bbGwsFA7nAJH6l9dOVH/T/v8fpJ8wgghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNEEIIIcyKJDdCCCGEMCuS3AghhBDCrEhyI4QQQgizIsmNMAuBgYG4u7sTFRWVK8f38vJixowZuXJsNfXo0YNvv/1W7TCEECJHSXJjJkJDQxk2bBglS5bEysoKd3d3WrVqxd69e03K/f777/j6+mJtbY2XlxeTJ09Oc6yrV6+i0WiMN2dnZ1q2bMmxY8cyPH9AQIDJPqm34OBgk3Lz5s2jdOnSWFtbU6dOHQ4dOmRyf1xcHMOHD8fZ2Rl7e3u6dOlCSEjIMx//uHHjePvtt3FwcHhm2adZsGABTk5OabYfPnyYIUOGPNex1RQQEIBOpyMiIsJk+yeffMIXX3yRZrsQQuRnktyYiS5dunDs2DEWLlzIhQsXWLNmDY0bN+bevXvGMlevXqVv37507NiRc+fOsWzZMry9vTM85rZt27hz5w6bN2/m4cOHtGnThvDw8KfGERgYyJ07d4w3V1dX431Lly7lk08+4dNPP+Xo0aP4+fnRqlUr7t69aywzevRo1q5dy/Lly9m1axe3b9+mc+fOTz3n9evXWbduHf3798+wTHJyMgaD4anHeRoXFxdsbW0zvD8xMTHbx1ZTpUqVKFOmDL///rvaoQghRM5RCpiIiAgFUCIiItLcFxsbq5w9e1aJjY01bjMYDEp0QrQqN4PBkKnH9ODBAwVQAgICnlru6tWrilarVQIDA59aLigoSAGUY8eOGbft3btXAZRNmzalu8/OnTsVQHnw4EGGx61du7YyaNAgJTk5WVEURUlOTlY8PDyUKVOmKIqiKOHh4YqFhYWyfPly4z7nzp1TAGX//v0ZHvd///ufUrNmTZNtv/76q1KoUCFl9erVSvny5RWdTqcEBQUpcXFxyrvvvqt4eHgotra2Su3atZWdO3eaPIbHbxMmTFAURVFKlSqlTJ8+3Xh8QPnhhx+U9u3bK7a2tsZyq1atUqpVq6ZYWVkp3t7eysSJE5XExEST/ebOnau0a9dOsbGxUXx9fZV9+/YpFy9eVBo1aqTY2toq9erVUy5dumTyeDJz3Hnz5ikdO3ZUbGxslLJlyyqrV69WFOXR//PxW79+/Yz7Tpo0SWnQoEGG9Zve60JkXUJCgrJq1SolISFB7VAKJKl/deVE/T/t8/tJ+hedTOU3sUmx1FlSR5VzH+x1EFuLjFsLUtnb22Nvb8+qVauoW7cuVlZW6ZYrXrw4NWvWZMSIEaxZswZra+tMx2JjYwNAQkLCU8tVrVqV+Ph4KlWqxMSJE6lfv75xvyNHjjBy5EhjWa1WS/Pmzdm/fz8AR44cITExkebNmxvL+Pr6UrJkSfbv30/dunXTPefu3bupWbNmmu0xMTF8/fXX/Pzzzzg7O+Pq6sqIESM4e/Ysf/75Jx4eHqxcuZLWrVtz6tQp/P39mTFjBuPHjycwMBBIqduMTJw4ka+++ooZM2ag1+vZvXs3ffv25fvvv6dhw4ZcvnzZeClrwoQJxv0mT57MtGnTmDZtGmPHjqVXr16ULl2acePGUbJkSQYOHMiIESPYuHGj8fFl5riTJk3im2++4X//+x8zZ86kd+/eXLt2DU9PT/7++2+6dOnC4cOH8fDwwM7Ozrhf7dq1+eKLL4iPj8/wuSOEEPmJXJYyA3q9ngULFrBw4UKcnJyoX78+H330ESdPnjQpN3jwYBRFoXTp0rRp04bIyEjjfe3bt2fEiBHpHj88PJzJkydjb29P7dq10y1TrFgx5s6dy99//83ff/+Np6cnjRs35ujRowCEhYWRnJyMi4uLyX5ubm7GfjnBwcFYWlqm6fPyeJn0XLt2jf+3d/dBUV3nH8C/y8IuL3YXhZGFKAkqoiRgQYIBtdoJBhtagxKlBCuiNKaJFcUmkCg4jiEGHS2NGtTGl77EBOmoNWk0pUuiISUIKCCSopOSaFXEiMjysrDsnt8fGfbnCiKahQub72dmZ9xzz7373IdheTz3nHu9vLy6tRsMBrz99tsIDw+Hn58fvv32W+zbtw95eXmYPn06xo4di9/97neYNm0a9u3bB4VCAbVaDZlMBo1GA41G02tx89xzzyExMRFjxoyBt7c31q9fj7S0NCQkJGDMmDGYNWsWNmzYgF27dlnsl5iYiAULFmD8+PFITU3F119/jfj4eERGRmLixIlITk7Gp59+au7f1+MuXrwYcXFxGDduHN544w00Nzfj1KlTkMvlGDFiBIDvLq9pNBqo1Wrzfl5eXujo6Og1x0REQwlHbu7Byd4Jxc8VS/bZfRUTE4OoqCh89tln+OKLL3Ds2DFs2rQJ77zzDhYvXozq6mrs378f586dw8SJE7F48WLMnDkTx48fx8iRI1FVVYWFCxdaHDM8PBx2dnZoaWnBmDFjkJubCw8Pjx4/38/PD35+fhb7fvXVV/j973+Pv/zlLw+WgD5qa2vrcRRKoVAgMDDQ/P7s2bMwGo0YP368Rb/29na4ubnd9+feOVpUUVGBzz//HJmZmeY2o9EIvV6P1tZW85yd22PqymdAQIBFm16vR1NTE1Qq1QMd18XFBSqVymI+0910jcq1trb2+dyJiAYzFjf3IJPJ+nRpaDBwdHTErFmzMGvWLKSnpyMpKQnr1q3D4sWLUVlZCaVSCX9/fwDA3r17ERsbi6lTp+KVV16BTqfDnDlzLI6Xm5sLf39/uLm59biC6F5CQ0NRWFgIAHB3d4dcLsf169ct+ly7dg0ajQYAoNFo0NHRgcbGRovPu71PT9zd3XHz5s1u7U5OTpDJZOb3zc3NkMvlKCsrg1wut+jb2wjN3dx+aafr+OvXr+9xAvTtxZeDg4P5313x9dTWNQH6QY7bdZy+TKJuaGgAgG6jakREQxWLGxvm7++PI0eOAPhuvk17ezuKi4sxZcoUyOVyHDhwAHPmzMHzzz+PrVu3mv8H32X06NEYO3bsA39+eXk5PD09AXw3ijJ58mScOHECcXFxAL77463Vas2XwyZPngwHBwdotVrExMQA+G711cWLFxEWFnbXzwkKCkJ1dfU94wkKCoLRaER9fT2mT5/eYx+FQgGj0Xhf59klODgYNTU1GDdu3APt35/HVSgUANDjuVVVVWHUqFFwd3d/4OMTEQ0mLG5swI0bNzB//nwsWbIEgYGB+NGPfoTS0lJs2rQJzzzzDABg2rRpCA8PR2xsLLKzsxEQEIBz587hypUrcHFxwYEDB7Bs2bJelzv3Jjs7Gz4+Pnj00Ueh1+vxzjvvoKCgAP/85z/NfVauXInExESEhYXhiSeeQHZ2NlpaWpCYmAgAUKvVWLp0KVJSUjBixAioVCr89re/Nfe/m8jISCQlJcFoNHYbkbnd+PHjER8fj0WLFmHLli0ICgrC9evXodVqERgYiKioKDzyyCNobm6GVqvFpEmT4Ozs3OecZGRk4Oc//zm8vb3x7LPPws7ODhUVFaiqqsLrr7/ex0z2z3EffvhhyGQyfPzxx5g3bx5cXFzMo1WfffYZnnrqqQeOj4ho0HngNVlD1P0uBR8K9Hq9SEtLE8HBwUKtVgtnZ2fh5+cn1q5dK1pbW839mpqaxOrVq4WPj49QKBTC399fZGdni8uXL4tRo0aJ6OhoYTQae1wKfi9ZWVli7NixwtHRUYwYMULMnDlTFBQUWPQxGo0iKytLeHt7C4VCIUJDQ8UXX3xh0aetrU28+OKLYvjw4cLZ2VnMnTtXXL16tdfPNhgMwsvLy2KZetdS8Dt1dHSIjIwM8cgjjwgHBwfh6ekp5s6dKyorK819XnjhBeHm5nbPpeCHDx/udvzjx4+L8PBw4eTkJFQqlQgNDRW7d+++63495bqnZfX3e1whhFCr1WLfvn3m9+vXrxceHh5CJpOZl4K3tbUJtVrd61L7ofp7MdhwKbK0mH9pDfRScJkQQkhWWUmgqakJarUat27dgkqlstim1+tRW1sLHx+f+1omTX1jMpnMk2Tt7Ky7UG/Hjh04evQoPv74Y6se15b0lP+cnBwcPnzYYoTtTvy9sA6DwYCPPvoITz/9dLf5UdT/mH9pWSP/vf39vhMvS5FNWLZsGRobG6HT6b73Ixh+SBwcHLBt2zapwyAisioWN2QT7O3tsWbNGqnDGHKSkpKkDoGIyOp4Ez8iIiKyKSxuiIiIyKawuOnBD2yONVGv+PtAREMNi5vbdM3g5m3oif5f1+8DV5gQ0VDBCcW3kcvlcHV1NT+Px9nZ2eL2/fT9mEwmdHR0QK/XW30pON3b/eZfCIHW1lbU19fD1dW11xskEhENJixu7tD1DKO+PHCQ7o8QAm1tbd2e+UQD40Hz7+rq2uuzvYiIBhsWN3eQyWTw9PTEyJEjYTAYpA7HphgMBpw8eRI/+clPeIlDAg+SfwcHB47YENGQMyiKmx07dmDz5s2oq6vDpEmTsG3bNoSGht61f15eHtLT0/H111/D19cXWVlZePrpp60ak1wu55e6lcnlcnR2dsLR0ZHFjQSYfyL6oZB84kNubi5SUlKwbt06nD59GpMmTUJkZORdLwv9+9//RlxcHJYuXYozZ84gOjoa0dHRqKqqGuDIiYiIaDCSvLjZunUrfv3rXyMxMRH+/v7YuXMnnJ2dsXfv3h77/+EPf8Ds2bPx8ssvY+LEidiwYQOCg4Oxffv2AY6ciIiIBiNJi5uOjg6UlZUhIiLC3GZnZ4eIiAgUFRX1uE9RUZFFfwCIjIy8a38iIiL6YZF0zs23334Lo9EIDw8Pi3YPDw/85z//6XGfurq6HvvX1dX12L+9vR3t7e3m97du3QIANDQ0cMLwADMYDGhtbcWNGzc450MCzL+0mH9pMf/Sskb+dTodgL7dWHRQTCjuTxs3bsT69eu7tfv4+EgQDREREX0fOp0OarW61z6SFjfu7u6Qy+W4du2aRfu1a9fuel8NjUZzX/1fffVVpKSkmN+bTCY0NDTAzc2N91oZYE1NTRg9ejQuXboElUoldTg/OMy/tJh/aTH/0rJG/oUQ0Ol08PLyumdfSYsbhUKByZMnQ6vVIjo6GsB3xYdWq8Xy5ct73CcsLAxarRYrV640t+Xn5yMsLKzH/kqlEkql0qLN1dXVGuHTA1KpVPxykRDzLy3mX1rMv7S+b/7vNWLTRfLLUikpKUhISEBISAhCQ0ORnZ2NlpYWJCYmAgAWLVqEhx56CBs3bgQAJCcnY8aMGdiyZQuioqLw/vvvo7S0FLt375byNIiIiGiQkLy4iY2NxfXr15GRkYG6ujr8+Mc/xvHjx82Thi9evGjxHJzw8HAcOHAAa9euxWuvvQZfX18cOXIEjz32mFSnQERERIOI5MUNACxfvvyul6E+/fTTbm3z58/H/Pnz+zkqsjalUol169Z1u0xIA4P5lxbzLy3mX1oDnX+Z6MuaKiIiIqIhQvI7FBMRERFZE4sbIiIisiksboiIiMimsLghIiIim8LihvrVm2++CZlMZnHTRb1ej5deeglubm4YNmwYYmJiut11mh7c5cuXsXDhQri5ucHJyQkBAQEoLS01bxdCICMjA56ennByckJERAQuXLggYcS2w2g0Ij09HT4+PnBycsLYsWOxYcMGi2fhMP/Wc/LkSfziF7+Al5cXZDIZjhw5YrG9L7luaGhAfHw8VCoVXF1dsXTpUjQ3Nw/gWQxdveXfYDAgNTUVAQEBcHFxgZeXFxYtWoQrV65YHKO/8s/ihvpNSUkJdu3ahcDAQIv2VatW4YMPPkBeXh5OnDiBK1euYN68eRJFaVtu3ryJqVOnwsHBAceOHUN1dTW2bNmC4cOHm/ts2rQJb731Fnbu3Ini4mK4uLggMjISer1ewshtQ1ZWFnJycrB9+3Z8+eWXyMrKwqZNm7Bt2zZzH+bfelpaWjBp0iTs2LGjx+19yXV8fDzOnTuH/Px8fPjhhzh58iSef/75gTqFIa23/Le2tuL06dNIT0/H6dOncejQIdTU1GDOnDkW/fot/4KoH+h0OuHr6yvy8/PFjBkzRHJyshBCiMbGRuHg4CDy8vLMfb/88ksBQBQVFUkUre1ITU0V06ZNu+t2k8kkNBqN2Lx5s7mtsbFRKJVK8d577w1EiDYtKipKLFmyxKJt3rx5Ij4+XgjB/PcnAOLw4cPm933JdXV1tQAgSkpKzH2OHTsmZDKZuHz58oDFbgvuzH9PTp06JQCIb775RgjRv/nnyA31i5deeglRUVGIiIiwaC8rK4PBYLBonzBhAry9vVFUVDTQYdqco0ePIiQkBPPnz8fIkSMRFBSEP/7xj+bttbW1qKurs8i/Wq3GlClTmH8rCA8Ph1arxfnz5wEAFRUVKCwsxM9+9jMAzP9A6kuui4qK4OrqipCQEHOfiIgI2NnZobi4eMBjtnW3bt2CTCYzP9+xP/M/KO5QTLbl/fffx+nTp1FSUtJtW11dHRQKRbeHl3p4eKCurm6AIrRd//3vf5GTk4OUlBS89tprKCkpwYoVK6BQKJCQkGDOcdfjTbow/9aRlpaGpqYmTJgwAXK5HEajEZmZmYiPjwcA5n8A9SXXdXV1GDlypMV2e3t7jBgxgj8PK9Pr9UhNTUVcXJz5wZn9mX8WN2RVly5dQnJyMvLz8+Ho6Ch1OD84JpMJISEheOONNwAAQUFBqKqqws6dO5GQkCBxdLbv4MGDePfdd3HgwAE8+uijKC8vx8qVK+Hl5cX80w+WwWDAggULIIRATk7OgHwmL0uRVZWVlaG+vh7BwcGwt7eHvb09Tpw4gbfeegv29vbw8PBAR0cHGhsbLfa7du0aNBqNNEHbEE9PT/j7+1u0TZw4ERcvXgQAc47vXJ3G/FvHyy+/jLS0NPzyl79EQEAAfvWrX2HVqlXYuHEjAOZ/IPUl1xqNBvX19RbbOzs70dDQwJ+HlXQVNt988w3y8/PNozZA/+afxQ1Z1ZNPPomzZ8+ivLzc/AoJCUF8fLz53w4ODtBqteZ9ampqcPHiRYSFhUkYuW2YOnUqampqLNrOnz+Phx9+GADg4+MDjUZjkf+mpiYUFxcz/1bQ2toKOzvLr1W5XA6TyQSA+R9Ifcl1WFgYGhsbUVZWZu5TUFAAk8mEKVOmDHjMtqarsLlw4QL+9a9/wc3NzWJ7v+b/e01HJuqD21dLCSHECy+8ILy9vUVBQYEoLS0VYWFhIiwsTLoAbcipU6eEvb29yMzMFBcuXBDvvvuucHZ2Fn/961/Nfd58803h6uoq/v73v4vKykrxzDPPCB8fH9HW1iZh5LYhISFBPPTQQ+LDDz8UtbW14tChQ8Ld3V288sor5j7Mv/XodDpx5swZcebMGQFAbN26VZw5c8a8GqcvuZ49e7YICgoSxcXForCwUPj6+oq4uDipTmlI6S3/HR0dYs6cOWLUqFGivLxcXL161fxqb283H6O/8s/ihvrdncVNW1ubePHFF8Xw4cOFs7OzmDt3rrh69ap0AdqYDz74QDz22GNCqVSKCRMmiN27d1tsN5lMIj09XXh4eAilUimefPJJUVNTI1G0tqWpqUkkJycLb29v4ejoKMaMGSPWrFlj8WXO/FvPJ598IgB0eyUkJAgh+pbrGzduiLi4ODFs2DChUqlEYmKi0Ol0EpzN0NNb/mtra3vcBkB88skn5mP0V/5lQtx260wiIiKiIY5zboiIiMimsLghIiIim8LihoiIiGwKixsiIiKyKSxuiIiIyKawuCEiIiKbwuKGiIiIbAqLGyIiIrIpLG6IaEgoKiqCXC5HVFSU1KEQ0SDHOxQT0ZCQlJSEYcOGYc+ePaipqYGXl5fUIRHRIMWRGyIa9Jqbm5Gbm4vf/OY3iIqKwv79+y22Hz16FL6+vnB0dMRPf/pT/OlPf4JMJkNjY6O5T2FhIaZPnw4nJyeMHj0aK1asQEtLy8CeCBENCBY3RDToHTx4EBMmTICfnx8WLlyIvXv3omvQuba2Fs8++yyio6NRUVGBZcuWYc2aNRb7f/XVV5g9ezZiYmJQWVmJ3NxcFBYWYvny5VKcDhH1M16WIqJBb+rUqViwYAGSk5PR2dkJT09P5OXlYebMmUhLS8M//vEPnD171tx/7dq1yMzMxM2bN+Hq6oqkpCTI5XLs2rXL3KewsBAzZsxAS0sLHB0dpTgtIuonHLkhokGtpqYGp06dQlxcHADA3t4esbGx2LNnj3n7448/brFPaGioxfuKigrs378fw4YNM78iIyNhMplQW1s7MCdCRAPGXuoAiIh6s2fPHnR2dlpMIBZCQKlUYvv27X06RnNzM5YtW4YVK1Z02+bt7W21WIlocGBxQ0SDVmdnJ/785z9jy5YteOqppyy2RUdH47333oOfnx8++ugji20lJSUW74ODg1FdXY1x48b1e8xEJD3OuSGiQevIkSOIjY1FfX091Gq1xbbU1FQUFBTg4MGD8PPzw6pVq7B06VKUl5dj9erV+N///ofGxkao1WpUVlbiiSeewJIlS5CUlAQXFxdUV1cjPz+/z6M/RDR0cM4NEQ1ae/bsQURERLfCBgBiYmJQWloKnU6Hv/3tbzh06BACAwORk5NjXi2lVCoBAIGBgThx4gTOnz+P6dOnIygoCBkZGbxXDpGN4sgNEdmczMxM7Ny5E5cuXZI6FCKSAOfcENGQ9/bbb+Pxxx+Hm5sbPv/8c2zevJn3sCH6AWNxQ0RD3oULF/D666+joaEB3t7eWL16NV599VWpwyIiifCyFBEREdkUTigmIiIim8LihoiIiGwKixsiIiKyKSxuiIiIyKawuCEiIiKbwuKGiIiIbAqLGyIiIrIpLG6IiIjIprC4ISIiIpvyf+w09xUZWWbbAAAAAElFTkSuQmCC",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
diff --git a/code/notebooks/WealthPortfolio.ipynb b/code/notebooks/WealthPortfolio.ipynb
index 4191184..3015fb9 100644
--- a/code/notebooks/WealthPortfolio.ipynb
+++ b/code/notebooks/WealthPortfolio.ipynb
@@ -11,7 +11,8 @@
     "from HARK.utilities import plot_funcs\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
-    "from estimark.snp import snp_data, snp_data_full"
+    "from estimark.snp import snp_data, snp_data_full\n",
+    "import numpy as np"
    ]
   },
   {
@@ -20,7 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "csv_file_path = \"../../content/tables/TRP2/WealthPortfolio_estimate_results.csv\"\n",
+    "csv_file_path = \"../../content/tables/TRP/WealthPortfolio_estimate_results.csv\"\n",
     "res = pd.read_csv(csv_file_path, header=None)\n",
     "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()"
    ]
@@ -33,7 +34,7 @@
     {
      "data": {
       "text/plain": [
-       "(4.062017216065237, 0.5, 0.0)"
+       "(5.927650549987919, 0.4337387151345948, 9.395681818099618)"
       ]
      },
      "execution_count": 3,
@@ -45,7 +46,7 @@
     "portfolio_agent = WealthPortfolioLifeCycleConsumerType(**init_calibration)\n",
     "portfolio_agent.CRRA = float(res[\"CRRA\"])\n",
     "portfolio_agent.WealthShare = float(res[\"WealthShare\"])\n",
-    "# portfolio_agent.WealthShift = float(res[\"WealthShift\"])\n",
+    "portfolio_agent.WealthShift = float(res[\"WealthShift\"])\n",
     "portfolio_agent.CRRA, portfolio_agent.WealthShare, portfolio_agent.WealthShift"
    ]
   },
@@ -65,7 +66,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -85,7 +86,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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x0z7W9eLH2XgvGLc0PNMlCyGEEGKKJS7AREQ4dqwDoxyDsgarxq4ZnQMTZFMYd6wDU/FzuOXijNcshBBCiKmVuABjiZhnm/GtSyrU4I5O4h1LMNqJ31aQSdc7MJgRql4DbkXOQhJCCCGSzp3tAo6UUYZfqp6JwbLJPA3a1ibxjl0z2oHxMwHGG9+BacCphLNRthBCCCGmUPI6MCpCoXDQGGtQDmB5WYBxKO8couf/PskZ3vL4cbU5ME7ZYMbt5iuEEEKI5ElcgDFqrINisFBbhTQ6hKS0RinFwPe2YMsRFzkrgLHzkHTF0FuqzkrtQgghhJgaiQswVo3vniiUC8qMLqMGpeMVSLYUveyBJULt4JQDdg+NzFC1QgghhJgOiQswRo0PJorR1dOjZ1Tr0QBjxoJO2h091DGPU21gf5+cSC2EEEIk2VEFmNtvv52lS5eSSqW46KKLeOyxx17x+i9+8YucfvrppNNpOjs7+fjHP06pVDqqgnHGgolFoxzQxkJtDozWGhsaooGx58+l5tYeMIJbbWBwYP/RvbYQQgghjglHHGDuvvtubrjhBm655RaefPJJVqxYwRVXXMG+fQc/5fnrX/86n/jEJ7jllltYv349X/7yl7n77rv5kz/5k6OseCzAKBTaUbVl1LW7HZewtzjWkgEyfisQz4MJogYGB7uO7rWFEEIIcUw44gDzhS98gQ9+8INce+21nHnmmdxxxx1kMhnuuuuug17/85//nEsvvZRf//VfZ+nSpVx++eW8973v/YVdm0NRzviSFTi6thPv6P0O1X2FCY/JeS1AHGD8qIGhkT1H9dpCCCGEODYcUYCpVCqsXbuW1atXjz2B1qxevZqHH374oI+55JJLWLt2bT2wbN26lR/+8Ie85S1vOeTrlMtlhoaGJnyNUs649dIolKNRhnEdGIew+2UBxm0Gagc62hyFwsG7RUIIIYRIhiPayK6np4coiujo6Jhwe0dHBxs2bDjoY37913+dnp4eXvOa12CtJQxDPvzhD7/iENJtt93Gpz/96YPep7yJAUY7GoWtd2C0HuvA+EsaqewYIueMTuIdQekGKkWZxCuEEEIk2bSvQnrwwQf5zGc+wz/8wz/w5JNP8u1vf5sf/OAH/Pmf//khH3PjjTcyODhY/9q1a1f9PuW+rGRHgxkbQtKuQ7gvPu8otTye+5LTOSDezK7qNaBGeqfuDQohhBBixh1RB6atrQ3Hceju7p5we3d3N/PmzTvoY2666Sbe//7384EPfACAs88+m3w+z4c+9CE++clPovWBGSoIAoIgOOjzOY4DtZXU/eU+tOPEc2Bq+8M42qXaE3dg0me0MnTvdlIqwNMBoantxlsYPJK3LYQQQohjzBF1YHzf5/zzz2fNmjX124wxrFmzhosvvvigjykUCgeEFMep7dVyFFv6K9/hv3d9iQf33k1vuQ/ruvEcmNo66ozbBKFFeRq3PUPoxMuRcm5zfB6Sl8Mt5o/4dYUQQghx7DjiwxxvuOEGrrnmGlatWsWFF17IF7/4RfL5PNdeey0AV199NQsXLuS2224D4KqrruILX/gCK1eu5KKLLmLz5s3cdNNNXHXVVfUgcyQcz6UQDlEIhwic5VjHq3Vg4vsbdFP8xuamUVpR9S1uEbJuM/2Vbiqej3e0e9AIIYQQ4phwxAHmPe95D/v37+fmm2+mq6uLc889l3vuuac+sXfnzp0TOi6f+tSnUErxqU99it27dzN37lyuuuoqbr311qMqWHljoUdZMMolbuTUOi1OM1jw2jMA2IyGIjQELVCAigN+qXJUry2EEEKIY8MRBxiA66+/nuuvv/6g9z344IMTX8B1ueWWW7jllluO5qUO4Hhe/WdlIdIeymhGA0xWNYIFtxZgVKMHvSE5vxkAQxGn4k9JLUIIIYSYHYk7C8kJvHG/Kaz2UIwLMDZeMj3agXFbU/Ht4zaz88rZGatXCCGEEFMvgQFmrHuilCLUHso4YOMAk7bxkmm3Iw4wqfb496wTz43BxOchlcKXnVYthBBCiMRIXIBx/fEdGI1RLgqNxZBxG3FwwFG4rWkAMp3N8XfVEF9nRnCiBrryMpFXCCGESKrEBRgnSKGcdgB8vQiDAzYeQmr02gBw29L1IweyC1sIidBKk3EbsbYWYHrlOAEhhBAiqRIXYLxUCr/hvQRNH8YzPgYdT+K1hqwbz39x56Tr17uBy4iKuy3xXjB5XNvAvr7ds1K/EEIIISYvcQHG9dMo5aB0Bm3KgAIcwOCqeH6MDibuL5NXZQByXhPWjKDJsW9gF0IIIYRIpsQFGC/I1H92olKtA1MLMDqeH6P8iW+rpOJ9X7JuC9g8RmcZGNg7YzULIYQQYmolL8CkxgUYU8YaFS+jtgZX1QLMyzowZScEIOc1A1BxfSoDe2amYCGEEEJMucQFGD8Y28NFR7UhpNok3noHxpsYYEKvtktvLcCUXY0dlEm8QgghRFIlLsBM6MBEJaxVKKuxRGNzYPyJAcYG8YqknNsMQMUFPdw3MwULIYQQYsod1VECs8n3UsAgAKniPqxtJ57Ea3FGOzBBnMvuvvtuurq6WJbpgD7wdICvU4Q6wh0pzM4bEEIIIcSkJa4DE/hpznn2SyzZcS9tPY+DAW002LEOjPIcoihi/fr19Pf3k1dl8kxcSu2VEvfWhRBCCFGTuA5M4KVp632Ott7nyAdgrCLOYbY+B0YHDoODg/XHVJVhWJXI2hRZr4XB6gheSQ50FEIIIZIqcW2IeAhpHGtrhzlGY6uQfIf+/v76JSOVPEMqHjLKuU1Ym8erZhBCCCFEMiUuwLieN+F3Y0HZ2jLqcfvAjA8wg4VhhnQRgJzXgjV5nFACjBBCCJFUCQwwQf3nSIM1Bmy8kZ2jxpZRjw8w5UqZfjUCQNZtwpo8OsrNaN1CCCGEmDrJCzCuQ6W2Svq+lQpjLLp2GrVW8R3KVRMCDMDA6BCS1wy2gLY5rLUzWboQQgghpkjyAoxWfOY9mq/9kuabr9VE1taHkPTo29FjAcarDTkViM9DyjiNtcCTYqBUnZX3IIQQQojJSVyAUUrxwhLN9y/SGK2wxsQBBoNWYwGmry/eqG758uUAGGWpEqKUIuM2YvDYsn/HLL0LIYQQQkxG4gLMy0XW1FYhGVRtCKkUlimX447L6aefDoDFMqLivWAybiOR1uzav21WahZCCCHE5CQ+wFgToRidxBsHmIGheA+YXC7HokWLgLgDM6TilUhZt5Gqht69W2elZiGEEEJMTvIDjI2HkNS4CbkDwwMAtLS00NTURBDEK5d61BAAabeB0InId8sQkhBCCJFEiQ8wphZgtBq7rb/WgWlpaUEpRUdHBwC9enQpdSPGlgh7e2a8XiGEEEJMXiIDTGMU1X+2tWXU4/ILA4MDQBxggHqAGa4NIWXcRqzNowbKM1KvEEIIIaZWIgNMizH1n601MLaAGoD+gXgJdWtrKzAWYApqbCm1NXmcvOwDI4QQQiRRIgNMUzQWYIwxKBxUrQVjFfU9YF7egakQArUOjMnjlhJ3lqUQQgghSGiA+WB/vKvuVcN5sAbsWAfGaFM/iXo0wLS3twNxuClRwdUevorQZTmRWgghhEiiRAaYc4uWB3a+xF/09MarkMbNghlRJay1uK5LLhefdxQEAbl0FoBupxeAjJtDhxJghBBCiCRKZICpKI+5kUFD7TyjsUm8wzrerG60+zI09AxRVGZOSzwfZp8TDy9l3EaUDRBCCCFE8iQzwODVf7bWxh2Y2iSYYR2vNGppaWFv13/y+BPvYOOmW5jXfuBSamtkDowQQgiRRIkMMFXGDf1YA2psEu+wGuvAbNnyVwDs3ftNFi3uBGCA2nECTiNWjV98LYQQQoikSGaAUWMdGENtI7vaINIwYx0YYyr16+Z1LgAgTxWDjc9DwhKacAYrF0IIIcRUSGiAGevAqJftAzMaYJqbmzGmVL8ulzNgwQJDqhAHGGXoK/TNXOFCCCGEmBKJDDCVWoCxxKdMM24OzIitHdiYLU3owJQrL+Go+O32qZFagKmwZeemGa1dCCGEEJOXyADzrHc2EO/rgqnNgUFhMJSoxvexa8JjSsXdBMSTdvv0CGknB6bIS5skwAghhBBJk8hlON/J/Co7hhUP8SrOtfdhVdyBGQ0vAGF124THlEp7SOmAgqnQr0ZQSpHWlt7tL810+UIIIYSYpER2YKzj8+XoSnbQgYpMHGBQlFQcYDKZDCP5jQA4TgaAUnk3WS8FwGBtKXVau1T2yRwYIYQQImkSGWA8J57v4iiDsgajFEppSiqe85LJZBgZ2QBAW9sbACgVX6IhHe/MO6zKWCxZN4carhzkFYQQQghxLEtkgHF1XLZSptaBUThKU6wNIeVyHsXiDgDa5vwSAMXSHloamgGIsBQok3Eb0YWZr18IIYQQk5PIAJP2HaDWgcFilEbhUFGjAWYQsPj+XBob4wm/pdJuck0N9ecY1EUybiOEspmdEEIIkTSJDDA3//KZzG9K8fE3noyOLFaBVpoIC4Dv7wMgl1tOKhVvYGdMkWzTWFiJ94JpgCiRfwIhhBDihJbIf72XtmV5+MY38J5VC1A2wiiNVhqDAcD1uoA4wGgd4PvtAHjZkfpzDKlaB8ZIB0YIIYRImkQGmFHWhihrsUqhcYhGA4wbB5iG3BkApFILAXDSQ/XHDqpCfB6ShaGCTIQRQgghkiThASZCG4NRoJTGqHhvXq33AnEHBqgPI2l/oP7YIVXAd1JopXh667MzXboQQgghJiHxAQZb2wdGORgMQZBHqRJK+WQyJwGQTi0CINI91KbJMKSKWCyB67F506Oz9RaEEEIIcRQSHmBCtLFYZdFoIgzZXD8A2ewpaB2fWj06hFSq7sHHBSyRMhQok3Y8Rratn623IIQQQoijkPAAE3dgjNLxEBKWbDYOMLnc6fXrRoeQSqXdpPBQKp4rM6gLZBwPumU3XiGEECJJEh5gQrQxWAWOiifx5moBZnQCL0AqHQ8hlUq7CZRXvz1eiZTFHZjRsoUQQggxSYkOMMZGKGuwKBQaowyZ7AAwNoEXIF0bQgrDIVK+wdp46fSgKpBxG3EKqRmvXQghhBBHL9EBZmwZNWjlEGHxvDIAftBev85xMnheCwDZTInRtz2kimScRlSUnfHahRBCCHH0Eh5gIpSxGKhvZDc6v0Urf8K1o/Ng0tmxPV+Gah0Ya6UDI4QQQiRJwgPMgRvZaR0BoPXLA0w8DyaYEGCKpNwcRnn0DA/OXOFCCCGEmJREBxisAWux1Dayw6B1rQOjvQmXji6l9rP52i2GSBlKKsRxUzz1guwFI4QQQiRFogPM2D4w8RwYo8P6fVoHE64dHUJy08MAeG58cvWQLuB7Lruee3iGqhZCCCHEZCU6wBgboagFGDRWV+v3qZfNgRldiYQbL7N2VDzUNKSKpJWltL17ZooWQgghxKQlOsBYG6KMwWLjIaQJHZiXDyHFc2CM6olvqB1CHR/q6EBvZUZqFkIIIcTkJTzARPEcGKXQyhnXgXFRauJbG50DE9kBtA4JjQPUViI5GZzCxI6NEEIIIY5dCQ8wIdpaLPFZSFbFHRilvAOudd1GHCcHQBDkCUMXgEFVJOM2oqrBAY8RQgghxLEp4QEmAkN9Ei9OHGBevgcMgFKqPpE3ExSAuAMzrIqk3RzGHhh6hBBCCHFsSnyAUaMdGDU2iVfpg4eRdG0eTDZVjK+rLaXGSxGNTooRQgghxDEv4QEm3sgOBRqnPoR0sA4MjM2DyaTizex8N564W3LB6IiXunbOQNVCCCGEmKyEB5jRDky8kR21XXiVPlSAiYeQglS8mZ2v43OThlSBtKN46uEHp71mIYQQQkxewgNMiDLUh5CoLaN++TECo0Y7MEFqBAC3dm7SkCrS4LjsX795BqoWQgghxGQlO8CYWgdGWRRjHZhDBph0PAfGTcW78TomnvcyqAo0uDmqe4dnoGohhBBCTFayA4wNAQvEe8GoXxRgah0Y7Y2gVIQO48m+Q6pIo9eKHTEzUbYQQgghJinZAQaDGs0cSqNrvziHCDC+NwetA5SyBEEBE2aBeCl1zm/BVhP95xBCCCFOGIn+F9uYEKzGYrCAHu3AOAfflC7eC6Y2DybIU7YeSsVLqR0/hzGJ/nMIIYQQJ4xE/4sd2QhlFFgDSqF0rQNziAADY8NIqdQIJSci8OIl1QUnwnNkMzshhBAiCY4qwNx+++0sXbqUVCrFRRddxGOPPfaK1w8MDHDdddcxf/58giDgtNNO44c//OFRFTxeZCPiHXUNVit07YTpQ+0DAxOXUleVwXfjTe2GVIGMGxCWq4d8rBBCCCGODUccYO6++25uuOEGbrnlFp588klWrFjBFVdcwb59+w56faVS4Y1vfCPbt2/nW9/6Fhs3buTOO+9k4cKFky4+NCbuwBB3YHStA3OoSbww1oHJBnFw8Wqb3w2qIg2ux/OPPjzpuoQQQggxvY44wHzhC1/ggx/8INdeey1nnnkmd9xxB5lMhrvuuuug199111309fXx3e9+l0svvZSlS5fyute9jhUrVky6+NAYlNUoLBGmvgrpUBvZwdhxAqO78Xo2fsyQKtDgZnnxyXWTrksIIYQQ0+uIAkylUmHt2rWsXr167Am0ZvXq1Tz88ME7F9/73ve4+OKLue666+jo6OCss87iM5/5DFEUHfJ1yuUyQ0NDE74OWo8xYDXxNF4zNon3MDowfm03XsfEhzoOqQIN3hz6t+99hb+AEEIIIY4FRxRgenp6iKKIjo6OCbd3dHTQ1dV10Mds3bqVb33rW0RRxA9/+ENuuukmPv/5z/MXf/EXh3yd2267jaampvpXZ2fnQa8LI4O2GgVE2Poy6lcOMPEcGCcYAgyYNBDvBZPzWwiHSod8rBBCCCGODdO+CskYQ3t7O//0T//E+eefz3ve8x4++clPcscddxzyMTfeeCODg4P1r127dh30unIEWI1SYMYNIb3SJN4g6EApF6UMflCkUs2hVIRRFuv5KNkLRgghhDjmuUdycVtbG47j0N3dPeH27u5u5s2bd9DHzJ8/H8/zcBynftsZZ5xBV1cXlUoF3z8wbARBQBAcein0qEptDowGImUOaxKvUg6p1AKKxZ2kUiPki41kGvaTz7fSr/KkdfYXvq4QQgghZtcRtRt83+f8889nzZo19duMMaxZs4aLL774oI+59NJL2bx5M8aMbdO/adMm5s+ff9DwciSqxoJ10Creyk7VhpBeaRIvQDq1uPZ9mIKKSPvx4Y79Kk/OS0+qJiGEEEJMvyMeL7nhhhu48847+ed//mfWr1/PRz7yEfL5PNdeey0AV199NTfeeGP9+o985CP09fXx0Y9+lE2bNvGDH/yAz3zmM1x33XWTLr5qiIeQgOgwJ/ECpNPxnJpMKg8KfCdeUt2vR8i5KSpF2QtGCCGEOJYd0RASwHve8x7279/PzTffTFdXF+eeey733HNPfWLvzp070XosF3V2dnLvvffy8Y9/nHPOOYeFCxfy0Y9+lD/+4z+edPGV0KCsQ7wTjBmbxPsKc2AA0um4A5NLx0upAx0Hlj6V52S3ke4XNtN5/hmTrk8IIYQQ0+OIAwzA9ddfz/XXX3/Q+x588MEDbrv44ot55JFHjualXlFoABRaKSLsLzyNetRogEmn46XUrlEADKo8WW8JL/z8ZxJghBBCiGNYopfchJGJ58AARh35EJKXjveXUWEKR4fxSiQ/xd6tO6a1biGEEEJMTqIDTBTWduKtLaMeXYX0Cyfx1jow2s3jOFXK1UYy2X4AhpwyDNnpLVwIIYQQk5LoABNGBoVT38hOqcPrwLhuA57XAkCQGqEQ5shmB4B4Im8aWYkkhBBCHMsSHWCMsWA0GjWhA/OLJvECpFOdte/DDFZ8MrUA06fy5NzMtNUshBBCiMlLdoCJLApdm8R7+HNgAFK1eTCp1AgV65Dy4gm9/WqErJvBWhlGEkIIIY5ViQ4wkRmbxBupsY3sDifAjM6DaaidSu0TL6UeUkXSbob8YHl6ihZCCCHEpCU8wMQdmLFJvIffgcnUAkymtheMDr04ACmwQZq96zZMX+FCCCGEmJREBxhrLNgD58D8olVIMDaEFGSGAQhLOZxMHGYKrmHXk89MU9VCCCGEmKxEBxhjbO0wRzVxFZLyfuFjR89DcoJBwFCpNNLYuA+IVyIN79o/bXULIYQQYnKSHWCi0SGkl61COpwOTGoeSnkoFREERUqVBlpztQCj8qiSmtbahRBCCHH0kh1gDCjrxKuQ1JEFGKUcUqmFAKRSw4xU02QzA0Dcgcko2QtGCCGEOFYlO8BEBqjNgVFjJ0hrHRzW49PjllIPloP6XjB5VSbwJMAIIYQQx6pEBxhrakcJoDB6LMCow9jIDsaWUmdSeaphCo3BNfE8GuO7RPFpkUIIIYQ4xiQ6wEQ2qm1kp7E6rN+u9S+exAtjHZhcugAowmIWx403tCt5mn3buqe8ZiGEEEJMXqIDjLVjO/HaegfGRanDe1ujHZh0ZgSAqJSlsW0vAIO6wFM/un/KaxZCCCHE5CU7wESj+8CMdWDUYSyhHjW6lNpPDQFQLTXQ0tIDQJ8eobB5zxRXLIQQQoipkOgAY6wFNEodZYBJL4of4xZwnAqVSsPYSiSVJ1s5vLk0QgghhJhZiQ0w1tpagFE4yqkPIR1JgHHdBjyvFYhXIhUrOdKZQbBQVlV8LzcdpQshhBBikhIcYEKwFmUVasIQ0pF1TcYvpR6qBDhOhF87iToKHMJyZWoLF0IIIcSkJTrAGGOxaLRysCoOMIdzjMB49Ym8qWEK5QwAmVw/ABVPc/83/2MKqxZCCCHEVEh0gLGo+jJqnFoH5jCXUI9Kp0aXUhcplzNYo2icE0/eHXIq7H742aktXAghhBCTltgAY0w1Po1a1YaQRjswh3GMwHj1zezSeax1qA43k8n1AvGRAuliamoLF0IIIcSkJTbAWBtia5N4tXJAj55EfXRzYILaXjDlwXaytSMFBlSejJOdspqFEEIIMTUSHWDAYusBptaBcQ7vHKRRox0Yxx8EDMXhDlKpYZSFqorwA598/9AUVy+EEEKIyUh0gLHWEu8Eo+Eoh5CCoAOlfJSKCIICwyPtaG1Je/HqIyfdwJp/+fpUly+EEEKISUhsgDGmtoxa6XgejBMfvOgc5knUo5RySKcXAvFS6v5iA6bq0tC0H4CK79H17OapLV4IIYQQk5LYAGNtldEhJJRGq9ocmCMcQoKxlUip9DBFHVLqbSPdsA+AAV2gKZIN7YQQQohjSYIDTIiycdcFpdC61oE5mgCTXgJANhWfRF3pbyNTm8jbr0ZoCVpqE4aFEEIIcSxIbIAxNkTZqN6BUbVVSEcTYDKZZQA05mqHOg621c9EGlB5Wv057H1h29QULoQQQohJS2yAsSYeQkIprFLo0WXURziJF6Ch8SwAMrn4JOrKcAdBagTHqWKUxaRzPPQf35yy2oUQQggxOckNMDYEE2EAq0CreAjpqAJM7kxA4/h5fL9AxTQSFXO0tMQ78nY7Q0R7ilNYvRBCCCEmI7EBxtgQbSOsmjiEdKQb2QE4Tpps9hQAcrleSq6m0LuApuYuAPboPlp085TVLoQQQojJSWyAsTZERxGoWgemNolXHUUHBqCx4WwAcg19DLsV8vszNDfvBaBbD9IcNFMtV6emeCGEEEJMSnIDjAnR1lCLLWPLqI8ywIzOg2nI9VJWIcX9Pun0MEqFGGWJMjle+J+fTU3xQgghhJiU5AYYG+IYg1UKoxRKH/0cGBjrwDQ29AEWW2qPf58Td2H2ucNsefDRyRcuhBBCiElLbIAxtoo2EUYR7wMzOon3KObAAORyZ6CUg+sX8f0iUTCXylAbHa07Adij+wny3lSVL4QQQohJSGyAsaa2kV1tDoyaxDJqAMdJkc2eCkCuoZdCoCnsb6G5NpG3Rw3R6DdPSe1CCCGEmJzkBhgbokyEZXQS7+QCDEBDw9g8mGGnQqEnIEgVcEyIVRBl0nRv3zMV5QshhBBiEhIbYIytoq3FKjBMfhUSjM2DaWkYACAcbou/N8Q79O53C7z4vf89+qKFEEIIMSUSG2Ds6D4w1IaQJrkKCcY6MNnaRN5K1ImJHBYuiI8R2KP7GX6xa7KlCyGEEGKSEhxgotocGIVRtt6BOdpJvAC53HKUctFeHj8oUPADSr1tdM7ZAkC/HiGlMlNSvxBCCCGOXnIDjKmCjVAKDGZK5sCMn8jbkOtlyClT6Mni+2X8MASgmg6olMqTfwNCCCGEOGrJDTA2BGtBaSIMahJnIY03Og9mTsMQKKgOtgJQdvsB6PNKbH3wqUm9hhBCCCEmJ7EBxtgwHkZSYLD1DozSk9urZXQeTEtzPHG3XOoEIOqID3Pco/vp/vmGSb2GEEIIISYnsQHGmirKGnStAzMVc2AAGhvjDoyf6wYsw6aVsJTitEUbUBaGdJHKkAwhCSGEELMpuQHGhlhra5N4zZSsQgLIZk9HKRf0CEFQYNCtUuhpYmG2m1Q1DkmVwI1fWwghhBCzIrEBxtgQi0EpRcS4VUiTDDCOE5DLng5AW24QqyzVgZb4NaN9APT7VfZv3DGp1xFCCCHE0UtsgLG2CsaiUBiiKQswAA0NrwKgvTWe91IuLABgoHkEiOfB7Lz/mUm/jhBCCCGOTnIDjAmxWJTSRKpav13rYNLP3VCbB9PYOgDAUCkOMO4ii7ZQUGUGduyb9OsIIYQQ4ugkNsCExoC1KAVWh/Xb1SQn8QI01lYiqdRLgKXPOpSHcpw5dzPZ2vzdkVrHRwghhBAzL7EBpmKjWoBRGGd8B2Zyy6gBcrnTUcojskM0+mUiZSj3NTPP70GXegAY8KsU+oYm/VpCCCGEOHLJDTDGYK1FozB6NMA4KDX5t6R1QC53GgCL5sTPXRmZB0AptR2AvbqfnT9+dtKvJYQQQogjl9gAUw4t8VFICqtGh5Am330Z1VDbkbdtXgGAkeJ8AHIn5XGMoqxCtq59YcpeTwghhBCHL7EBphgBVqMV2FoHRqmpCzCj82DSLfGQUXepCWsUZ5yyk1wl/rPlo/CQjxdCCCHE9El0gLFWowBTm8Q7lQFmdCVShS341qFiFeX+JhRAtQ+ozYPpH5my1xRCCCHE4UlsgCmFCmsdNAqcWoDBnbLnz2VPQymfMBxkUWP8vOWhuQDk528CoFsPcv/nvzplrymEEEKIw5PYAFOMADQasLV9YNQUbGI3SmufXC7ekXdhZ3xMQSEf7wdz1vyNeEYTqojqUGXKXlMIIYQQhyexAaYUaqx140m80zCEBGPzYJrmDwOwr9gKQK4NUlFt5VO2gb0btk/p6wohhBDilSU2wFRDA6Y2hFQPMFPXgYGxeTAqsxvXaoaKWaojGZRn2TfnRQB63DyPfOmbU/q6QgghhHhliQ0wYWhRkROvQqoto56Kc5DGG+3AjOSfp8NrBhQju08C4FWtY/Ng2k0bxsjOvEIIIcRMSW6AqcYBRqHAieeoTHWAyWZPw3FyhOEQi5daAAZ6zgBg/sJBlA0xyqJzTTz73z+d0tcWQgghxKElNsBEocExDnrcRnZTHWC09pjbthqA5kVbAeitNpDf14F2IMzuAWCn00vXfbIrrxBCCDFTkhtgIouOJs6BmeoAA9DRcSUAFecxtIWirjK8bREAZ7THw0ibnS7mB4soF4pT/vpCCCGEOFBiA4ypWlStA4MeHUIKpvx1WlsvxXUbqFT3sbg1D0CpZxkmcmnv3IulRFVFdKVKPP6P35vy1xdCCCHEgZIbYKIIrzYHRtU6MI4z9QFG64C5bZcDsKBzLwBVr5nhXSejFGSbNgCw3nkJta0w5a8vhBBCiAMlNsBExuCaWvm1DowzDUNIMDaM5DW/ABiGfUvvxgwA5yzbgLKGfp3HzTaz/8WXpqUGIYQQQoxJdIBxongISY8GGCc1La/V0nIJnteCYZCWpv3kdYWor42wmMNrrFL2egHY4O7lha/cPy01CCGEEGJMYgOMiUI842CVQqt4DxbHmZ4OjNYec+fGw0gnL4pPp2bOEvq3xJN5l7c+AcB2vZ9sOSd7wgghhBDT7KgCzO23387SpUtJpVJcdNFFPPbYY4f1uG984xsopXj7299+NC87gbEWxzgoBWqaOzAAHe3xMFK2bTNKGfYFFQZfjF9v4Sk9uNURrLLsSeXZ8qMnpq0OIYQQQhxFgLn77ru54YYbuOWWW3jyySdZsWIFV1xxBfv27XvFx23fvp0/+IM/4LWvfe1RFztBFKKNAg1axx0PPQ2TeEc1N1+E57Vi7BBLWkcwyuJ4Kyj2tYELpYZuADa4e9j/wMZpq0MIIYQQRxFgvvCFL/DBD36Qa6+9ljPPPJM77riDTCbDXXfddcjHRFHE+973Pj796U9z0kknTapgAGsjjI03srNotJqenXjH09qlvf3NACw7tQ+AvpxL38YmAC6Z9whOZCioMhXfpzQwMm21CCGEECe6IwowlUqFtWvXsnr16rEn0JrVq1fz8MMPH/Jxf/Znf0Z7ezu//du/ffSVjmNMCNagrQalUaMdmGkMMDA2jET6adJKU9AVqj2nY40mWGzA7AZgo9vFxn/532mtRQghhDiRHVGA6enpIYoiOjo6Jtze0dFBV1fXQR/z0EMP8eUvf5k777zzsF+nXC4zNDQ04Ws8a6tYE6GNBq3rk3j1FJ9G/XLNzavw/XbCaIizT1YAFDOLGH6pHYCgaR9Y2Ov0M7JzYFprEUIIIU5k07oKaXh4mPe///3ceeedtLW1HfbjbrvtNpqamupfnZ2dE+63NkQRoXABVZ/EO90dGKUc2tvfBMC807rAQo9XZHhb/N5WnbKedGEYgN1Bnp6nt01rPUIIIcSJ6ogCTFtbG47j0N3dPeH27u5u5s2bd8D1W7ZsYfv27Vx11VW4rovrunzta1/je9/7Hq7rsmXLloO+zo033sjg4GD9a9euXRPutzbEWgM4WE19H5jpDjAwNow0VHyIpbm481IurSIsB9hmSzkd1/qis5et31477fUIIYQQJ6IjCjC+73P++eezZs2a+m3GGNasWcPFF198wPXLly/n2WefZd26dfWvt771rbz+9a9n3bp1B3RWRgVBQGNj44Sv8YwNgQhlHVC6vgpJzUCAaWo6jyCYRxSNcM4FLgD7UjC4tRWAzpa9BBVLVUX0VUtE5XDaaxJCCCFONEc8hHTDDTdw55138s///M+sX7+ej3zkI+Tzea699loArr76am688UYAUqkUZ5111oSv5uZmGhoaOOuss/D9owsc1oRgI6xysYCagVVIo5TS9S6M1/Y8jSpDVUWU958DwOIz9hIU4qGjje5ednxPujBCCCHEVDviAPOe97yHz33uc9x8882ce+65rFu3jnvuuac+sXfnzp3s3bt3ygsdz9oqEAETOzDTPYl3VHv7WwDo7X2AlactB6An6qQ0mAXfkm8ZQhvo13l2Prl5RmoSQgghTiTu0Tzo+uuv5/rrrz/ofQ8++OArPvarX/3q0bzkBMaGGCJAY9XMzoEBaGxcQSq1iFLpJU59dcRDGzSDTpnCjk5S52zgjI5udmwoMNycYac7SGFnH5nFrTNSmxBCCHEiSORZSNaEaBNitINVoEaXUWtvRl5fKUVHrQszmH+A5W3x5nyDA+dhLbSctg/XPAPANr2fDd889B45QgghhDhyyQwwtoqOQiKt48Mcax2YmZjEO6q9I54H09PzABe+/jwAdluXwt4WACrtikxJYZVlR283tioHPAohhBBTJaEBJkRHBqM1RjHjc2AAGnKvIp1egjElgrlbmOe3YpWlsO9sAM4/bTsU48m8L3rdPPn/fjRjtQkhhBDHu0QGGGMjnCjEKFUbQprZOTAwcRhpX/cPuGDlKgB2DS8hqjrYtgom2IgXKQqqTNfWPVTKlRmrTwghhDieJTLAWFNFR4ZIq9oQ0sychfRy7R2/DEBv308483VnkMYnb6G4J97f5tUtw1jTC8DuVIH7/+SfZrQ+IYQQ4niVzABjQ3QUxR0YmLUAk8ueTiZzMsZU6B9cwzmLzwCgt/csAMKVFUrVn4OFPU4/85xF7Hz0hRmtUQghhDgeJTbAOMZglMU6Uf12rYMZrUMpxfx57wRg+7bbueBNF6Ms7BxpprivBeVHvHHxEHndB8Bmv4e9/7YWE0Wv9LRCCCGE+AUSGWCMraIig9YOoa7Wb1czOIl31KJF78f351Is7aQQ/YiTGhcBit5NqzGRJjwz4tzBdQBsdPaQzc7loc/++4zXKYQQQhxPEhlgrAlxIoPSGjMuwMzUPjDjuW6Wk5Z9DIBt2/6eC14Tr0LaFWXpeXopAPNet5co3E+kDA96zzNvYA69m3fPeK1CCCHE8SKZAcaGqMiilcY4owHGQanZeTvz57+LbPZUwnAA1fI/tDg5QhVR2n0Jpf5maDC8LvMzrA3p0cOsD/ax+e9/PCu1CiGEEMeDRAYYY+NVSI7WGDW6NHnmuy+jtHY59dRPAbB797+y8qx5APQ1pNjz0w6sVahXD3NyzyMArHO246Wbefqf7pm1moUQQogkS2SAsTZCGxsHmNoQklKzF2AA5rS+hra2N2BtSG7x/aSVz7Au46Z+iZ7n44MuF73+JcKoB6ssP/FeIHjRkO8emNW6hRBCiCRKZIAxUYgKNVoxFmCO7lzKKXXqKTeilEf/4P9y+er5AOxIj1Dd+hoqIxlsa5U32J+iTIVBXeCZYA8vfE66MEIIIcSRSmSACW0VHbm4gHVCYPY7MACZzDI6F10NQMn5d1YuOh2A3U2N7H9kGQDRZQUW710LwAvuS0Reis3/+cjsFCyEEEIkVCIDTNkYtPFwlMaoWgdmhjexO5Rly34Xz2ulUNjCitUFWr1GSqpKQb2O/s2tKG1ZfNkeTNQNwE+99ZQe6aE8UJjlyoUQQojkSGSAKVajuAOjFFaPdmCOjQDjug2cdNLHAdix83be/qtX4KDp8oYpb30LYcmD+QV+aeRhnLBIQZV5MrWL9Z+7d5YrF0IIIZIjmQEmMmjj4yiFrc2BmY09YA5l4YL3kMstJwwHKZpvctnK1wCwNXAYWPcqAMI3F2nu3wTWstXpZtCE7F7z/GyWLYQQQiRGIgNMvmrQkYerdL0DM9PHCLwSpZyxZdV7vs65b1jIkoYFRMqwt3ghI7sbUW7IGee/hBPtBOBn3kZ67t1MlJcTq4UQQohfJJEBplQ1qNE5MLUOjHOMzIEZ1dpyMXPnXo61EZtf/Ay/cu2vklI+A06Roa1vxYQatbSP8/q24pXzVFTIY8EO1n/x/tkuXQghhDjmJTTAVNE2DjCjQ0iOm5rlqg4UL6v26et/iIp5kre+6ZcB2GIVI+tXAOBf1YMTbUcZ2O30sbswQM+j22azbCGEEOKYl8gAU611YJRy0G4cYDyvYZarOlA6vZjFndcCsOnFW1l+wXLO7Twz/r3vXEo9OXRQYsXiboLyegAeczez/dtPExWrh3xeIYQQ4kSXyABTqYQo46K0g1s7C8n3Gme5qoNbuvQj+H4bxeJ2XnrpX3jL+99Oq9dIUYXs2/JmrAH/zN10VKqk8gUiZXgk2MKmO34y26ULIYQQx6xEBphqGKKth3UcnFqAcY/RAOO6DZx80u8DsG373wHDvOvXfxWNYkc5RX5bPJS04Je2EXrr0RH06GE29e7mpZ9unMXKhRBCiGNXMgNMJURZD7SD48ardlwnN8tVHdr8+b9CQ+5VhOEwW7f+NQuWLeL1570WgOf2nEV1OIObHeK8uQZ/ZAsQH/i48/vr2PjY+tksXQghhDgmJTLAVMIQZR3Quj6E5LrHboBRyuHU024CYPeeuxkcXMelv3wZixvnU7Walzb/EgD+ORt5dXcvXnEQqyyPpnZQ+MbzPPU/T8xm+UIIIcQxJ5EBphpW0FZjtMZxR1chHbsBBqCl+QLa268EDE8/8wEKxS2869pfI6V8XhpuIb/7DJSylN7bxWXPPo6OIgZ1gRczA6Tv7eJn//Hj2X4LQgghxDEjoQGmCtbBKjU2B8Y59lYhvdwZy2+lseEcqtV+nnrqarzUIFe96UoAntlxNuFwI152kPz1Q6zc9hwQH/i4IzVEx+MV7rvzh7NZvhBCCHHMSGSACaMqCgejFa577A8hjXLdBs499y6y2dOoVPbx5FPv5+QV7axYfCZhGPDcs79MuacBJ5Un9f6NdPbvAeBhbxPrgt2c/KLPjz73rVl+F0IIIcTsS2SAMWGIVZpIMdaBcY/9DgyA57Ww8tx/Jp1eQqn0Ek+tez9v/NXX0OI1Mhw67Hz+1yjuaUF7ZTqv/AmLR7oAeM7dyU+DjZy2v4UffPrrs/wuhBBCiNmVqABTyOcBiEyIRWPGBRjnGF6F9HJB0M55K/+VVLCAQmEbzz//Ad7xq5fjoulSJbZsfifFXQtw3JDFb7qfkypdYA3bnf3cEzzNacUOfviJr8322xBCCCFmTaICTBTGS6bDKAQUoTaJGkIaL5VawMqV/4LvtzOS30jXwKf4jfe/i4yTop8Kz229nNK201DasnD1fZzqdoEJ2a+H+J7/OItVO/f9/j8TRdFsvxUhhBBixiUqwFTDOKxYE6G0R6iLKGWB5AUYgExmKStXfg3Pa2V4+Fl6Bv6UD/zOb9KeaaWkItbuvIji5vMBmHfpGpY370aFJYZ1if/2n6DZb+Hnf/DvVEpy7IAQQogTS6ICTFgtA2BsiKsdIrdQu0ejdXr2CpuEXPZUVp77VVy3kcHBtezY+Uf81u9dy2nzTsIoeGLPmQxvije9m3vuTzlz4U50NU9ZhfzIf4ooHfDUjf/J8GB+lt+JEEIIMXMSFWCiMB4uiUyI0h6RV6zdk0YpNXuFTVJDw6s4d8VdOE6Wvv6fsXHjDfzqB36VS86+CIB1XUvZ/8JqrFG0nv4oZ5+yGac8hFGWB/3n2ZepsPnTP2L/nv5ZfidCCCHEzEhUgBkdQnKqIUprIqcEgNaZ2SxrSjQ1rWTFOXeidUBP7wOs3/CHrH7HG3n7FVfhoNnQM5+dz1+OjTSNS57hnLOfwysNALDW28r6TC9dn/8pOzbund03IoQQQsyARAUYG8WTeHW1inZcbK0Do3V2NsuaMi0tF3HO2V9CKY99+37I+g2fYMWrV3LN+68mowN29rez8dkrsFWP3LwtrFj1JG5pEKxlk7uXRzI7GL7zCV54dMtsvxUhhBBiWiUqwITVEAAdhjhaY2odGMc5PgIMwJw5r+Pss/4WpRy6ur7Dxk230HnSEj70ux+hLdPC/qE2nnnmCkw5Tbp1N+e9+iHcSh5lDLudPtZkNhL+53q+8/WHqUZmtt+OEEIIMS0SFWCiKB5C0lGI1hrc0QCTvBVIr2Tu3Ms584zPAYrdu7/O5s230dTcxAc/+mFOmbeUoXwLTz5zOVGxkaCxj1WX/A9OVMaphvTrEe5JP8Pip/fx1Zv+nnuf24u1drbfkhBCCDGlkhVgRufAhBFKOfUAk8Ql1L/IvHlv5YzltwGwc9eX2bbtb/B9n1//0NVcfM6FFIuNPPH0G6mMtOJl81zwmu+hHYtXKlJQFX6UeoYz7UJKX/oKn/ybu3hqp0zwFUIIcfxIVIAJq7UOTBjiKAeceFm17zXOZlnTZsGCd3PaabcAsG373/HMMx+iUuniine+hbe+6SrCSo61z6ymMNiBG1S48NJv0uKm8YrDhCrifv9Z0s3L+JVtaZ7/yz/jT7/yPXb2Fn7BqwohhBDHvmQFmPoQUoTWCu3Gk3o9//gMMACdi67mtFNvQimPnt4HeOTRK9i56yusvOhcrrn6ajzbyFPPvp7BvoVoL+K0y/6FU5ftxS0ZrIKfexvZ1lDkNQ1v441re7j/z36fz337Ifrzldl+a0IIIcRRS1SAMbV9YJwwQmmNUztGwPebZrOsadfZ+ZtceOF/09S0iigq8OKLf8HjT7yTlrkjfOj6D9OabufZ51/Hvq6TURrmnPYoF1z2DdpSwwA85+7i7uDnjDSluCz1Ls5b8wxf//QNfPmB5yhV5SgCIYQQyZOoAGNr5/44kQY9dpDj8TqENF4ueyrnn/fvLD/9Vly3keHh53j88XfQ0/Mlrv2d3+Ck+SezcdMlPL3uckqDHbh+xBkXfpfTTv4ZLoaiqvCwt4lvpx7HNs/hCvtWFn7rHv72zz/Ff63dgTEy0VcIIURyJCrARCbEWosOHZRWuLUhJNdtmOXKZoZSmoULf41XX/Q/dLT/MmDYuevLrFv3dq54RyeXnP9qRobm8fjTb2T9C/+HSqGZjoVbueg1/86ShevQWEZUif/11vPf6XWkm5fw1sKlOHd9hVv/6nP8fPP+2X6LQgghxGFJVIAxYYS1VbTRWK3qHZgTJcCMCoK5nHXW37BixZdJpRZSKu/h2Wc/xPxlP+DDv/PrrDjlbPp6lvLY2ivZ/OKFmDBg8cnP8upL/51F7evBWgZ1gR/7z3NvZgMtTefwtu6T6fvbW/mLO77Gi93Ds/0WhRBCiFeUqAAT2SrWRjiRA1rh1gPM8beM+nC0zbmMV190D4sXfwClnNruve/mgtdXuO53r+Psk86ma+9yHnv8bezYcQ4KxbLlT3Dxpd9gXvNWsIZ+PcJ9/jP8b247bbnLeNN6l2c/+/v81b/fw76h0my/RSGEEOKgEhVgTGSxNsSJXKxS9Um8zgkaYAAcJ8Opp9zIBau+S0PD2YThMBs33sS27R/h8nes4PrfvZ4zl5zDrh0reOzxt7F3z6k4OuLUc37Gqy/+Jq2Zl8Aa9ush7vHX8UTTPhanruKSh/dw362/xz/+6FHy5XC236YQQggxQaICjDUGY6oQuROHkI6znXiPRkPDmVyw6j859dRP4TgZBgef5LHH38rAwFd5+/veznXXX89pC89jy+ZXs3btVfT0dOL5FV616sdccMG3Sfu9YA1deoDvB2tZ3zTCcvXLnP6jx/iXv7iBf/npC/SOlGf7bQohhBAAuLNdwJGwJsLaEG0crAbXPbGHkF5OKYfFndfSPvcKNm76ND0997N9xz/Qve8HLD/9L3j3Nb/G/v37+fEP7ueFF5pobNzH0mVP0tS0n1Wv/iH5wQbWPfsGTJTjJaePl5w+lnhzuaD8Swz857e595617FrwS3Se/2auOLuTOblgtt+yEEKIE1SyAkwYBxhlXCI3RKl46e/xdhbSZKVSCzjn7DvYv/9/2LjpTykWd/DUuvczf947OeWUG/nV33wv+/bt48c/uJ9nnp7LnDm7WLrsKbJNQ1z6mu8yuH8Oz7xwKdDIDmc/O9L7OTno4IzS+1Fb19E8uJr7fng2O9vfyOJVV/LGsxdJmBFCCDGjEhVgjI2HkJRxsF68hNpaheNkZrmyY49Sivb2K2htvYTNWz7H7t3/xt6ub7Nv//8wb97bWDD/XROCzNonFjFv/maWLH6Gprm9vPZ132PPjsVs23IOxm1hi9PN1sw+To0WMafvL6mObIfi4+RGPsf9PzyHXe1vpHPVlbzxnMW0Zv3ZfvtCCCGOcwkLMKMdGAdqAQabQik1u4Udw1y3geWnf5r5897Ghg2fYiS/kd27/43du/+NbPY0Fsx/N+/8jbfR1/cGHvjBfTz++DIWLVrPwkXPs2DJThYs2cn2jafStfNMqkEjm9w9bM7tZXl6IW+tXk2h+0p2F3dhy4+RHvkC9//oVbzUfjkLz7+SN56zRMKMEEKIaZGoAENksDYE42JqJ1Er0rNcVDI0NZ3HhRd+n/7+h9mz91vs338P+fwmXtx8K5u3/CVtbW/g8re9m8sql/GTe37CE4+fyuLFzzJv/iaWnv4iS09/kYHeeWx64SLKNPKC+xIvOC/RETSxJDufN5uzCbvfxu7yLmzxMfyRL3D/j85kz9zLWbDqKt54zhJaJMwIIYSYIokKMNbGAUZZB+vFK2KUluGjw6WUprX1UlpbL6Va/VO6u7/P3r3fYmj4Gfbvv5f9++/F99u54P+8g0v9X+GRB1bw5BNPsHDReua07aR5ThcXvva/GOifx47tKxgabqdbDdKtB3mMzTR5GZaYuVwWvRe3+53sKe3ClB/HyX+R+390Rj3MrD5nqYQZIYQQk5KoABPZiMhUUdbDuHGA0UoCzNHwvCYWLXofixa9j5GRjezZ+y26ur5LpbKPHTv/EfhHTjr7fFZc8iY2P3MxG9ZtxwZbaWvbSVvbTlasvJdyOUNv7yJ6exYzMDCPQV3gGb2DZ9wdpHyPxdm5XGTeRW7fr9BVeomXyo9i83/DmnuWs6ftjSy44K284exlEmaEEEIcsUQFGBtGVE2Io31wi8CJvYndVMnlTue0Uz/JKSf/IT09P2bP3m/S2/sTBgfXMji4luzcDJef+WYy6Xewc4vHxufWMxw9T1vbTua07WTBgk2EoUd/3wJ6exbT09NJScEmdw+b2IPjaRZlW1lh3kZzzzvpLe5ld+kxwvzfseZHp7F37hXMP/8q3nDOSRJmhBBCHJZkBRhrKIVVHDysmwckwEwlrX3a26+gvf0KyuVu9nZ9l717v0mhsI2urv8E/pN0dgmXv/3dNDX9Ptu3DLD+qed4cehJmudso61tJ3Pbd2CMZnCwnZ79nezrOolI+exwetjh9KBcaM80cWp0BW09b2O42M2ewqNUCn/PA/eeQtecK2g//62ce/JCTpqbw9EyQVsIIcSBEhdg8tUQlwBbG0KSTeymRxB0sHTJ/8eSxR9icHAte/Z+i337fkixuIMtWz8HfIE5c17LG972blqaP8OO7bvZ8NRzbHjxMdJNG2hr28mppz3OKac+Tj7fwv79nezZdRpGpevzZvCgKZNhiXk9HT1XUi700pV/jP7y7Tz+k2Z+VjmJYuMK3EXnMf+UczlncRudrWlZdSaEECJhAcZY8uUKWrtQW4XknWAnUc80pRTNzatobl7FaafexL79P2Lvnm8xMPg4vb0/obf3J3heC/M63sb/edMvk8tdyf79fax/+nm2rn8Ym3qKtradLFv2DMuWPUOplGHf/iXs2r4cY7L1eTO4kE77LI4uYd7+KzD5IV7UG3ky+hatztdgV8RAaQ6V6slUWlYQdJ7PglPPYUXnHDoaAwk1QghxgklUgMFaSuUSjnKJnHgfGM9vnOWiThyum2XB/HexYP67KBS2sWfvf9K199uUK93seumr7Hrpq2idoqnxXBafuopzLrgMR3+AzRt38eJzP6NgH6Z1zg4Wd65nced6wtBj7/4l7HxxOZhmirrCRncPG11wU5qF5mTeXf0E7osF8uV+etjPFn8rPfZfabV3Ud1m6Cq1EUWnELauJFi8isWnvopzFrXIzsBCCHGcS1aAMRHFchmtXYwTDyH5fvPs1nSCymSWccrJf8BJyz5GX/9D7N37n/T1/ZwwHKB/4BH6Bx6pXalpaDiD8167iobcbzDQ38aLz75Af/5BGlq2sGjeFjrnb8YYze79nezceAY6bCN0TXyMgbMf5UOHbaYzOpczzaW4Q1WKPcMMVPvosfvY6m5goPoVGqv/SOlF2F5sw9jTMW3nkl5yASedcgZndTbTmPJm9W8mhBBi6iQqwFhrKFeKKOXUD3IM/KZZrurEprVL25zLaJtzWTxHqbCFwYEnGBh8goGBtZRKuxgefp7h4eeBfwZg7pLFnNK0Cuxr6Nph6Nq3lqBhA4vad9LZsQNrIT/SSteepfTtPZ2ydulSA3TpgfhFA8jagDmmgTn2VVweXUSQD6n2jTAYDtBjutnOc/RX7qSxeDsj6xXri3NBLYe555FbdgEnn3war1rYTNp3Zu1vJ4QQ4uglKsAYoFgqg+PijAaYlASYY4VSmlz2VHLZU1m48L0AlEp7GRxcWws0TzAysoFicSfF4s74QSnoPLmVbHYFheFL2d/VS6HcR5DZz+Jlz3PK6U9SKmXp7e1kcKCD4cG5VMI0eVUm75TZSU/8n+IAvJxDq80xx5zOa835pAoW01dkJOynJ+pmh15Lb+ERGocq9D6jebLYgeOcgW4/n4aTL+S0k09m+bxGfFfP3h9RCCHEYUlUgLHGUqkUCBwHx4kDjOfJHJhjWSo1n1Tql+no+GUAwnCYwcEnGah1aYaGnqZa7WNg4McANM2FFp0mlz0b1OvJD2apDPfjqR3Mm7eFk095DMcJyedbGBlpIT/SymBfB+VKjqqKxlY4Afigsopmm6HVnsT55hzSFdA7yhQqg/SGXezgMfqGHiLXV6H7CZeHyx143lk4HefRfNKFLFzUyaKWDO0NAVqWdAshxDEjUQEGayhUywSOwnXjSbyuI8uok8R1G5gz53XMmfM6AIwpMzz8fD3QDAysJQwHGBp+DHgMFDS1OyzMnUFjwy9hOYXB/hRhfhORs4H0nF0sXvIMvl+gWGyqhZoW+vYvpFxqwmjoV3n6ybPFATwgAxkbMMd08ip7JpmqxtlVpVIZYqDSxQ77CH29PybbVWGrqwjCLCpsxtXz8VNLUE3L8OcupXHeycxrn0tnS4a2nC8roYQQYgYlK8BgqVRLWJ2pd2BkH5hk0zqgqek8mprOYwkfmjiPphZqSqWXGB5+juHh5+qPa5m3hGVNq8jmXocxcxnsj+jp2krkbMBv3s78BS+SSg1TqWQYGWlhaLiV3r2LCUvNVB1NQZUpOGV20Rv/tyANnnVosfNYZn6Ds0MPd2+VqJxnIOqlr7yFQuVZBhqfJcxZdCYknQlJBw5+mMGpBZwgtQyneSl+2zKa5p/E/FrAac54EnCEEGIKJSrAxENIJXAy9Um8ruwDc1x5xXk0tUATz6PZQbG4Y8Jjdcqho3khmfRSPP8iyuUMwwND2Eo3bmMXHe0PkU4PEkUu+XwLA4Nt7Nu9FFtsoeI4VFXEPjXIPj0Y/zcjBcpCk21gjn0tDeEb8MoRbrFKNFyiYPIUGGHYjFCp9FAtbaCr6VnKDRadjUhlQoJA45kMTtSCp+aRyizDbT6JoBZwFrS3saglQ1NaVkgJIcSRSFSAAUtULmK8CKUsAI4MIR33DjmPZnAt+fwWisXtFAo7MKY4cYLw6OMbIK08UqmFaL2casVBmTy+M0h7289IpYcARaHQyP7+dvbtWIYutlJ1fELHMqAKDFAAB6htL5O2Pi2miSwdLLQB6cjDjQy6GmLDKtWeIiVbZMSOUDAjVIu9hKVN7Gl4jlKDgWyEn43wUwpHZXGjZny1gFR6GUHLSQRzl9I8/2QWtM+hsyVDNkjYf1WFEGKaJep/FS0WUyqDH3dfrAXHkdOoTzQvn0cDYK2lXOmmWNhOobCdQnE7xeIOCoX4uzFlisXtwHYAHB/StXMjlfLQuoVsxsN3B1kw9+e4Xh6lLNVKipF8K31dC8j3LKUcpSlrKKoKxdpminFRE2v0rEPGBuRsO1nbSZYUfvhGnChCVUMYrlAZKFIwBfJmhGqpHzPyIj0NL1DIVSAX4WUi3IxG6TSeaSVw5pNOLyPVcjKpuctonn8yC9tbWdickeXgQogTzlEFmNtvv52/+qu/oqurixUrVvB3f/d3XHjhhQe99s477+RrX/sazz0Xz184//zz+cxnPnPI61+RNVAuo7z4Hw4byRbyIqaUIhXMIxXMo6Xl1RPus9ZQLneNBZvCdgr1cLMTaytE0T5Q4L1sA18/CJkTFGlp3kUU7sSYEGMrlCsBpVID5XKOSilLcbiFYr6Z0LhEWlFVEYOqwCCFsSc7SMjJ2TRZ20zOnkLaXMzCCJwwQlVCzEiZsi1QiIpUSwNEw9voz9xPvuE7mKzBy0botAIvjWdbSTnzCYL5pFIdeNm5uA1zCZrmkm2eS0NjC63ZgJasT2PKlf/eCCES74gDzN13380NN9zAHXfcwUUXXcQXv/hFrrjiCjZu3Eh7e/sB1z/44IO8973v5ZJLLiGVSvGXf/mXXH755Tz//PMsXLjwyF7cWmylhK0FGGNku3jxiymlSaUWkEotoJVLJtxnbUSptHdcsNlOsbCj1sHZhbVVLP0oDa4/9rhUeoTm5hFctw2oEFWHCKtbCcMyUVQgNIYw8qiUM5TLWfLDLRQG5hJVU4RKE2moqqi+QupQfOuStU3kbDsZezYtkcYNLTqMsAMVzP4yZVOkWhzCjOxhxF3HQJCnEoxggggCiw4M2rcoT2M9D+ukcMnhqQZcp5nAm0sm3YGXbcfLtRE0zSXT3E5jU2st9HjkAgk9Qohji7LW2iN5wEUXXcQFF1zA3//93wNgjKGzs5Pf/d3f5ROf+MQvfHwURbS0tPD3f//3XH311Yf1mkNDQzQ1NXHXZz5Kj9vNErOIORd8m7DUzhVvefhIyhfisBkTUi7vqXdu4o5N/L1Ueglro1d8vOPkcJwGIMAYTRRGRGEVY0oYWyGMoFpNUyplGRpqYWT/Qkwph8GjqiE6zP30AuuRtQFZmyJjPLwI3MigqhGEZcJKkRJFSpQpEVINq7gVS2a4QIphil6Bgp+n7I8QBiVsENVDD77GeB7GDXBVDk834DvNBF4b6dQ8UrkO3FwbqaZ20k1zaWpupSUb0Jr1yfiOhB4hTnCj/34PDg7S2Di1+7YdUQemUqmwdu1abrzxxvptWmtWr17Nww8fXpAoFApUq1VaW1sPeU25XKZcLtd/HxoaAsBYA2UDmfg+RepIyhfiiGjtkk4vJp1ezBz+z4T7jKlSKr00blhqR/17qbwXa6tE0QhRNDLxOV0YzSVxQ2eEpqb9dHTsQp22CWs0xliwEYaQKHKoVlOM5BsY2LeQ6kAb1XIDkfaoaDDaUlZVyqpKHyPxROODLGhK2QxZ20rOBmRsgGc1jgHHWJoii4oiVGSxUYipVAhLFapUKakyZVulYiNspUpquEIqHKbq7mTQW0+3nycM8hi/iEpZtG9QvibyPIwfoJ0snmrAc5pJ+W2kUx2kM/PxG2udnqaOOPTkAlozvszlEUIctiMKMD09PURRREdHx4TbOzo62LBhw2E9xx//8R+zYMECVq9efchrbrvtNj796U8f9D5VsdAUDyEpJRN4xezQ2iOTWUYms+yA+6y1hOEwlUoPlWpv/L3SQ7Uy9vPY7b1EUR6IsDYPCnTt33ANuG6VICiRyw0wr2NX7QXA4sRDqliMcSmWUwwPtBAOtRAWm6lWcpSiNPkoIEJTUlVKqkovw4f3/qwiwCOwqdp3jxQurtG4RpE1llZjUbXwY6MQW6piClUqtkKJKhVComoVJ18hKAyDu4+Cu50+P0/VG8GkRsCvogILvqqHHvwMnm4gcFoIvDmkU+1ksgtINc4laJxLqqGFINNEJp0hm3LJBi5Z3yXlaen4CHECmdFVSJ/97Gf5xje+wYMPPkgqdejuyY033sgNN9xQ/31oaIjOzk7AoqoGvBIAjs5Od8lCHDGlFJ7XiOc1kuWkX3h9FBWpVHoPCDvl8cGndl+12g8KFFHtO2hdpcGt0pAdhoU7D3h+azXGOESRQxR6hNUUlWKGUqGJcqGBSjlLpZqhWE1TNQ5WKYyyFKlQVJWJT3YYDRLPOgTk8K1LYD1cXLR1cA2kDDiRRRmLigyYEFsIiYarVKlStSFhFGJKZfyRIoohKm43Q94jVL0RIj+PckvgVlCuxboK62msozGOg3U9rBeg3ADtpHFVBldn8bwcvpMjCJoJvGa8VBNepgE31YiXbiCVbSTINpBJZ8kGDlk/DkZyLpYQx64jCjBtbW04jkN3d/eE27u7u5k3b94rPvZzn/scn/3sZ7n//vs555xzXvHaIAgIggMn6FprURXAjYeQHNnEThwHHCdNOr2IdHrRL7zWmJBqta/evTl4l2fsdmtDlDI4jol3r/ZLwDAc8gxUB6yDsRprFMYoIqMxkSaq+lSKWSqlHGElTVQNCKOAauRRNh5V4xKGHmHkUbVxwDoSyoKPT2CzBHhY6+JbB99Cxih0ZNGGseAThZhKlchUKFOhQoWyDQmtJbLghBavpMkUDQFVjOqlqrso6jKhUyHSJaxbwTglrFsCJ/7d+grraKxbC0WeB66P9VIoL42r41Dku1l8txHfbyIVNJNKNeOm40DkpeNAlM42kk5n4lAUuGQ8B9eRUCTEVDiiAOP7Pueffz5r1qzh7W9/OxBP4l2zZg3XX3/9IR/3f//v/+XWW2/l3nvvZdWqVUddrMKiqxpVCzCeJydRixOL1i5B0E4QHLji7+XioazBQ4adyriwU632EkUFIAIVoRWgDzKlprnnsOq0VmGtqk1e9ggrKaJKQFQNiMKAKPIII59q5FExHmHkEkYeURjfVgw9osjDGIfDSULaBgTkCKxLCg/fevjWwbUKZcExcUDS1qIMYC3KGDA23p4hMvG8oyjC5qtEhEREVAipYqgSEVlLGBmoGJyKwitV8PQQkeplSFfo0xWMU65/WacEXgnrlLFuFeNprKPqoci6PngBNsjguhk8J4vn5PC8RgK/iXTQRDrVip9uxE034GcaCTJxMMql02RqnSKZLC1OVEc8hHTDDTdwzTXXsGrVKi688EK++MUvks/nufbaawG4+uqrWbhwIbfddhsAf/mXf8nNN9/M17/+dZYuXUpXVxcAuVyOXO7IdtG1tf/xUV4cYHxfAowQhxIPZTXjec1ks6f8wuujqECl0ksYDhOG8QTkMBwhjEaIarfFP8ffw/BlP0cjtRAESlmUsmhtcN2QIFU8qvdgrSIKXUwYhxsT+rXw41GtfY3+HtVCTznyKNR+Dmvfo8jjSFtCrvVwSeHi4FoHD4cUGs86OFahrUJZhWfBt7WAZCzKxkNk9XBkDNaEUIowNv6KbERo45hUtYYoNOjQQrGKrg5h9ABDKmTAqWBUpRaIxgejSvy7G2JchXEdrOdiXS/ezMj1wAlQXoB2Ahwd4OgUrhPgOilcJ4PnpvG8DIGXwfNyeH4Wxwtw/DRuEOB6GdxUGs9P4QcZfN8jcDWBq/FdTeA6eI6S8CRmzREHmPe85z3s37+fm2++ma6uLs4991zuueee+sTenTt3ovVYi/RLX/oSlUqFd73rXROe55ZbbuFP//RPj/DVLU7VHevABBJghJgqjpMhnZ7cxHhjQqKoUAs/w2OBZ3z4OSAIDR8QhMJwBIhDkOtVwavC+E0Bj0IUupjRoBP69cATRh6RcYmMQ2QczCG+qsalYhxGjIOJxt/nYozGGJfDD0kax7q4xMHItToOSjiEte6VtgrPKhwL2hLPG7LEnSMLGIMyEbZisOUIYw3GhhhrCU1EpCIqmNpnYqiEEVFFQVRFh31UMVRUxIiKsMpgVAgqwhJinRCrQqwOQVdBVzG69rtTxeqo9hViHYt1HazW4DhYxwHHBcfDuh7K9bFuCu0FOG4KVwe4OoXrpXGdDL6bwnOz+H6WwMvGQcpP4/qp2vcAL5XB91N4QZrAcwhcpxai4i8JUSemo5rEe/311x9yyOjBBx+c8Pv27duP5iUOyTN+PcAEqZYpfW4hxORo7aJ1PIF5Mqy144LQy8PP8CGC0IEdoTAcxtoQAMcNcdwQLzi6btDhMEZjJwQctx50xoejiUHJnRCUIuNQjV4eoA68xhqHKHKxVnNgcHJwrIeLrneQ3FpQUlYRocCARuHWwpI2cVDCgqptD6ZsrZOEjc9uMQawWGPi38MIUzVYDFiDsZaICEtEpCyRshhriIAIQzwCZyhHFqIiXrWAVzV4cSIDFWGUQakIQwS1EGVVFeVEoKoYXQUdYnSEdWrXOBFWm/i7q7GOg3VccEfDlI/yfPBS8QRvL43jpvB0gOOm8d0MrpvG97L4Xo6Un8XzM7h+GsdP4QUZ3CCF56fxU2kCP5jQifJdjaMlRM20RJ2FhLV4UYB24gCTSkuAEeJ4pJTCdbO4bpYg6PjFDzgEay3GVIii4UN3gcIRIlPEmHK8yWBUxpgykSnFt0UlIlMeu9+UiaLafaaMtdX662ltQBsct/oKVU09Ex2qezTWHTKRg7ETu0eRcakeNFTVviL3IM8Zfx08OCnARVkPjcJB1787VqPROCg0Og5NxF/WgrIKBXi1eUuK0XlLHBCqlCG+0daCVO3LhhaqEWAx1mJthMVgrcViMBii2vYDkbWEWKqmQjEsQ3U/VCN0GOJqhVYGS9ydUsQdJ1QVq2pdKcdgVRWrQ4wzen8EysZhKm6bgbZYTfyvrXYw2kGpOGQp7cZBq9a1Uo439uX6aM9HOz6Orn05Po728JwU2vFxnQDPCfDcFK5XGyL0AhzXQ7s+jhvgeqM/e/F9nofnBriuxnUUnq59dzSuVjg6OcOCiQowCnCUh+PGSzuDoHlW6xFCHNuUUjhOgOME+H7btLyGMWE9zNQDTi34HCr0RKY07v7a9eNui8NTpRaeCrXvo9dXMKZMPI4U006Edl55Z+ipFk/SduIJ20bHK9dGv8b/Xv9ZHXif1XHX6mCPsyruZo17nLUTn7f+uxl7zMTfJz5//LODMg4OTi1A6Tho2bHAZaxGE++HpIgDVf33ceFq9AtbG94b3djeGpS12NGuVWShEneZjDFQC1VgMFisrXW24rgV/5+1GAVGWarGUqGMsaX4mtGmmDGoqDb3KlJx+LIWhwirahdRe24Vd7iUqt2nIlC17lktfMWhq3a7rnW11GgQq4UybbBKgVMLZlrVhg0dlHZQThzKVG0YsRROXxhKVICx1qJsgFv7/25S6UPv5iuEEDMhHjZzgZnbl8pai7UhxtS6Q9FoUDpEWBoXqKJxoWpiMIqfJxp9ngn31TpTdmxfIKUsjhPO2HueaqOr5A4alg4StCYEsVpwe3lIOyA81X9XGDsW9g4MXi/73Tgoq8E4KBtvbYCJv6vad201Dk4csBgNW2BRRKMhi5cFrlrHbPxt43+n/t3WfrZjOdnacQHNYqu2dn8cuOrDjLXwFf9kKJanb8g2UQEGQDspXOnACCFOYEoplPLQ2sNl5vbDGh2SG99ZioNUFWurGBtiTXXcbWF8++httoo1tetq18a31a6r/Rw/T6X2mLD2fJX69caUx/1cqb1GWH+9+HtUuy2egXPg39DiOAe/LylGA9iELlY9hKkJPzP6M2rCfVh18Nvqt489F+Ofm4M898seh1UUCtMXchMVYJSyKEfH48yAL/vACCHEjBk/JAdTezDfdBrtWNl6GKqOCz0HD1H10DQaoiaEtBBjK7UQFta6WhWsKY+FKlOp/zz2etWxgGVqgc6GY3XUAtdoAMPG83AOFbLifwsNzjF8hFg+b6btuRMVYKwFFUT1nx1HjhIQQgjxykY7Vgc96TQhxjpKlZeFnuq4YFaph58JX0S1QBRhrald87LvmAmPPfi10cvC3mgnLRzrsNUDWPzlOCVg+7T8TRIVYADcVJzmTOShlGzJLYQQ4vinlIPjOMCBx+wcy4aGhniFs0smJVkJwFrcVDwJKQr9WS5GCCGEELMlWQFGGVQ6nhAkAUYIIYQ4cSVqCMnPDaKWbIp/kQAjhBBCnLASFWCaT11LNhs3jZqyl85yNUIIIYSYLYkKMCbSDO1azqvO/hinnffG2S5HCCGEELMkUQGmo/ELXPz2t812GUIIIYSYZYmaxPuqVa+f7RKEEEIIcQxIVIARQgghhAAJMEIIIYRIIAkwQgghhEgcCTBCCCGESBwJMEIIIYRIHAkwQgghhEgcCTBCCCGESBwJMEIIIYRIHAkwQgghhEgcCTBCCCGESBwJMEIIIYRIHAkwQgghhEgcCTBCCCGESBx3tgs4HNZaAIaGhma5EiGEEEIcrtF/t0f/HZ9KiQgwvb29AHR2ds5yJUIIIYQ4Ur29vTQ1NU3pcyYiwLS2tgKwc+fOKf8DiCMzNDREZ2cnu3btorGxcbbLOaHJZ3HskM/i2CKfx7FjcHCQxYsX1/8dn0qJCDBax1N1mpqa5D+Mx4jGxkb5LI4R8lkcO+SzOLbI53HsGP13fEqfc8qfUQghhBBimkmAEUIIIUTiJCLABEHALbfcQhAEs13KCU8+i2OHfBbHDvksji3yeRw7pvOzUHY61jYJIYQQQkyjRHRghBBCCCHGkwAjhBBCiMSRACOEEEKIxJEAI4QQQojEOeYDzO23387SpUtJpVJcdNFFPPbYY7Nd0nHvtttu44ILLqChoYH29nbe/va3s3HjxgnXlEolrrvuOubMmUMul+NXfuVX6O7unqWKTxyf/exnUUrxsY99rH6bfBYza/fu3fzGb/wGc+bMIZ1Oc/bZZ/PEE0/U77fWcvPNNzN//nzS6TSrV6/mxRdfnMWKj09RFHHTTTexbNky0uk0J598Mn/+538+4cwd+Symx09/+lOuuuoqFixYgFKK7373uxPuP5y/e19fH+973/tobGykubmZ3/7t32ZkZOTICrHHsG984xvW931711132eeff95+8IMftM3Nzba7u3u2SzuuXXHFFfYrX/mKfe655+y6devsW97yFrt48WI7MjJSv+bDH/6w7ezstGvWrLFPPPGEffWrX20vueSSWaz6+PfYY4/ZpUuX2nPOOcd+9KMfrd8un8XM6evrs0uWLLG/+Zu/aR999FG7detWe++999rNmzfXr/nsZz9rm5qa7He/+1379NNP27e+9a122bJltlgszmLlx59bb73Vzpkzx37/+9+327Zts9/85jdtLpezf/M3f1O/Rj6L6fHDH/7QfvKTn7Tf/va3LWC/853vTLj/cP7ub3rTm+yKFSvsI488Yv/3f//XnnLKKfa9733vEdVxTAeYCy+80F533XX136MosgsWLLC33XbbLFZ14tm3b58F7E9+8hNrrbUDAwPW8zz7zW9+s37N+vXrLWAffvjh2SrzuDY8PGxPPfVUe99999nXve519QAjn8XM+uM//mP7mte85pD3G2PsvHnz7F/91V/VbxsYGLBBENh///d/n4kSTxhXXnml/a3f+q0Jt73zne+073vf+6y18lnMlJcHmMP5u7/wwgsWsI8//nj9mh/96EdWKWV379592K99zA4hVSoV1q5dy+rVq+u3aa1ZvXo1Dz/88CxWduIZHBwExg7VXLt2LdVqdcJns3z5chYvXiyfzTS57rrruPLKKyf8zUE+i5n2ve99j1WrVvHud7+b9vZ2Vq5cyZ133lm/f9u2bXR1dU34PJqamrjooovk85hil1xyCWvWrGHTpk0APP300zz00EO8+c1vBuSzmC2H83d/+OGHaW5uZtWqVfVrVq9ejdaaRx999LBf65g9zLGnp4coiujo6Jhwe0dHBxs2bJilqk48xhg+9rGPcemll3LWWWcB0NXVhe/7NDc3T7i2o6ODrq6uWajy+PaNb3yDJ598kscff/yA++SzmFlbt27lS1/6EjfccAN/8id/wuOPP87v/d7v4fs+11xzTf1vfrD/3ZLPY2p94hOfYGhoiOXLl+M4DlEUceutt/K+970PQD6LWXI4f/euri7a29sn3O+6Lq2trUf02RyzAUYcG6677jqee+45Hnroodku5YS0a9cuPvrRj3LfffeRSqVmu5wTnjGGVatW8ZnPfAaAlStX8txzz3HHHXdwzTXXzHJ1J5b/+I//4N/+7d/4+te/zqte9SrWrVvHxz72MRYsWCCfxQnimB1Camtrw3GcA1ZTdHd3M2/evFmq6sRy/fXX8/3vf58f//jHLFq0qH77vHnzqFQqDAwMTLhePpupt3btWvbt28d5552H67q4rstPfvIT/vZv/xbXdeno6JDPYgbNnz+fM888c8JtZ5xxBjt37gSo/83lf7em3x/+4R/yiU98gl/7tV/j7LPP5v3vfz8f//jHue222wD5LGbL4fzd582bx759+ybcH4YhfX19R/TZHLMBxvd9zj//fNasWVO/zRjDmjVruPjii2exsuOftZbrr7+e73znOzzwwAMsW7Zswv3nn38+nudN+Gw2btzIzp075bOZYm94wxt49tlnWbduXf1r1apVvO9976v/LJ/FzLn00ksP2FJg06ZNLFmyBIBly5Yxb968CZ/H0NAQjz76qHweU6xQKKD1xH/CHMfBGAPIZzFbDufvfvHFFzMwMMDatWvr1zzwwAMYY7jooosO/8UmPQV5Gn3jG9+wQRDYr371q/aFF16wH/rQh2xzc7Pt6uqa7dKOax/5yEdsU1OTffDBB+3evXvrX4VCoX7Nhz/8Ybt48WL7wAMP2CeeeMJefPHF9uKLL57Fqk8c41chWSufxUx67LHHrOu69tZbb7Uvvvii/bd/+zebyWTsv/7rv9av+exnP2ubm5vtf/3Xf9lnnnnGvu1tb5Olu9PgmmuusQsXLqwvo/72t79t29ra7B/90R/Vr5HPYnoMDw/bp556yj711FMWsF/4whfsU089ZXfs2GGtPby/+5ve9Ca7cuVK++ijj9qHHnrInnrqqcfXMmprrf27v/s7u3jxYuv7vr3wwgvtI488MtslHfeAg3595StfqV9TLBbt7/zO79iWlhabyWTsO97xDrt3797ZK/oE8vIAI5/FzPrv//5ve9ZZZ9kgCOzy5cvtP/3TP0243xhjb7rpJtvR0WGDILBveMMb7MaNG2ep2uPX0NCQ/ehHP2oXL15sU6mUPemkk+wnP/lJWy6X69fIZzE9fvzjHx/034hrrrnGWnt4f/fe3l773ve+1+ZyOdvY2GivvfZaOzw8fER1KGvHbVsohBBCCJEAx+wcGCGEEEKIQ5EAI4QQQojEkQAjhBBCiMSRACOEEEKIxJEAI4QQQojEkQAjhBBCiMSRACOEEEKIxJEAI4QQQojEkQAjhBBCiMSRACOEEEKIxJEAI4QQQojEkQAjhBBCiMT5/wF1ka7xRLOGCgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -142,10 +143,47 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "from estimark.estimation import get_weighted_moments\n",
+    "from estimark.scf import scf_data\n",
+    "from estimark.parameters import age_mapping\n",
+    "\n",
+    "\n",
+    "moments = get_weighted_moments(\n",
+    "    data=scf_data,\n",
+    "    variable=\"wealth_income_ratio\",\n",
+    "    weights=\"weight\",\n",
+    "    groups=\"age_group\",\n",
+    "    mapping=age_mapping,\n",
+    ")\n",
+    "moments\n",
+    "\n",
+    "moments_values = []\n",
+    "for key in moments:\n",
+    "    moments_values.append([np.mean(age_mapping[key]), moments[key][0]])\n",
+    "\n",
+    "moments_values = np.asarray(moments_values).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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REZFCpfAhIiIihUrhQ0RERAqVwoeIiIgUKoUPERERKVQKHyIiIlKoitw6H06nk6NHjxIcHOyRJZZFRESk4BmGQVJSEtHR0Xh5Xbxvo8iFj6NHj17WBaRERESk6Dh06FCuyz6cT5ELH8HBwYCr+JCQEJOrKV3sdjtz587lxhtvxMfHx+xySh21v7nU/uZS+5vLE+2fmJhITEyM+3v8Yopc+MgZagkJCVH4KGR2u52AgABCQkL0P78J1P7mUvubS+1vLk+2f36mTGjCqYiIiBQqhQ8REREpVAofIiIiUqiK3JyP/HI4HNjtdrPLKFHsdjve3t6kp6fjcDjMLqdY8vHxwWq1ml2GiEiRVuzCh2EYxMbGEh8fb3YpJY5hGFSoUIFDhw5pjZWrEBYWRoUKFdSGIiIXUOzCR07wKFeuHAEBAfoF70FOp5Pk5GSCgoIuuUCM5GUYBqmpqcTFxQEQFRVlckUiIkVTsQofDofDHTzCw8PNLqfEcTqdZGZm4ufnp/Bxhfz9/QGIi4ujXLlyGoIRETmPYvUNkzPHIyAgwORKRC4s5/OpOUkiIudXrMJHDg21SFGmz6eIyMUVy/AhIiIixZfCRwkzadIkwsLCzC5DRETkghQ+CsmQIUOwWCw8/PDDeZ4bNmwYFouFIUOGFH5h51i6dClWq1WnMouISIFR+ChEMTExTJs2jbS0NPe29PR0pk6dSuXKla/6/c2c4OhwOHA6naYd/2I08VPyIyndjqNofoRFShyFj0LUvHlzYmJimDlzpnvbzJkzqVy5Ms2aNcu175w5c7j22msJCwsjPDycW265hT179rif379/PxaLhenTp9OxY0f8/PyYMmVKnmOeOHGCli1bcvvtt5ORkYHT6eS1116jWrVq+Pv706RJE7777jv3e/bs2ROAMmXKXLQ3Jmd4Z9asWdSvXx+bzcbBgwfJyMjg6aefpmLFigQGBtKmTRsWLVrkft2BAwfo2bMnZcqUITAwkAYNGjB79mz384sXL6Z169bYbDaioqJ47rnnyMrKcj9ftWpV3nvvvVy1NG3alNGjR7sfWywWxo8fT69evQgMDGTs2LEA/Pzzz7Rq1Qo/Pz8iIiK4/fbb3a+52rqleDqemM4XS/fRe/xfNB+7kGdWWbl9/Aqe+34TX604wPqDZ8jMUiIR8bRitc7H+RiGQZrdnKXA/X2sl31mw3333cfEiRMZMGAAAF988QX33ntvri86gJSUFEaOHEnjxo1JTk5m1KhR3H777WzYsCHXGhzPPfccb7/9Ns2aNcPPz4/ff//d/dyhQ4fo2rUrbdu25fPPP8dqtTJ27Fi+/vprPv74Y2rVqsWSJUsYOHAgkZGRXHPNNUyePJlBgwaxY8cOQkJC3OtWnE9qaipvvPEGn332GeHh4ZQrV47hw4ezdetWpk2bRnR0ND/88AM33XQTmzdvplatWgwbNozMzEyWLFlCYGAgW7duJSgoCIAjR47QvXt3hgwZwuTJk9m+fTsPPvggfn5+ucJFfowePZrXX3+d9957D29vb3799Vduv/12/v3vfzN58mQyMzNzhYerqVuKl0OnU5m/7TizN8ey+sBpDOOf5xyGhS1HE9lyNBFWHwJc/5+3rFqGa2pEcE2NcBpEh2CxWEizO0i3O0jLdGDz9iIy2Hbe3wdZDic7jyez43giZQNtNIgOISLIdt79Dp5OxeE0qFkuSGdNSYlW7MNHmt1B/VG/X3rHArD1P90I8L28Jhw4cCDPP/88Bw4cAGDZsmVMmzYtT/jo3bt3rsdffPEFkZGRbN26lYYNG7q3jxgxgjvuuCPPcXbs2EHXrl25/fbbee+997BYLGRkZPDqq6/yxx9/0K5dOwCqV6/O0qVL+eSTT+jQoQNlypQBoFy5cpecuGq32/noo49o0qQJAAcPHmTixIkcPHiQ6OhoAJ5++mnmzJnDxIkTefXVVzl48CC9e/emUaNG7uPn+Oijj4iJiWHcuHFYLBbq1q3L0aNHefbZZxk1atRlLXzWv39/7r33Xvfjvn370rdvX8aMGePe5qm6pWhzOA3WHzzD/O1xzN92nJ3Hk3M936JKGW5pHMUNdSKYN38B5eq0YMfxFLYcTWDT4QROp2Ty566T/LnrJABeFnAaeY8T6GulWmQg1SOCqBYRSEKanU2H49l6LJF0e+7ek3LBNupHh1AtIpBj8ensOZHM/lMp2B2uN76pQQVevq0hkcF5Q4pISVDsw0dxExkZSY8ePZg0aRKGYdCjRw8iIiLy7Ldr1y5GjRrFypUrOXnypHs+xcGDB3OFj5YtW+Z5bVpaGh06dKB///65hih2795NamoqXbt2zbV/ZmZmnmGf/PD19aVx48bux5s3b8bhcFC7du1c+2VkZLhXpH388cd55JFHmDt3Ll26dKF3797u99i2bRvt2rXL9Rdf+/btSU5O5vDhw5c1L+bcdtmwYQMPPvjgefe92rql6Np6NJER09fnChxWLwstq5Sha/3ydG8URXSYq3fPbrcT4Qc3NShPz6Y+gKtndefxZP7ac5K/9pxixd5TJKVn5TqGn48XmVlOUjIdbDmSyJYjiXnqCLZ5UzcqmFPJmew7lUJcUgZxO06waMeJPO9ldxjM+TuWlftOMebWhvRsHKVeEClxin348PexsvU/3Uw79pW47777GD58OAAffvjheffp2bMnVapUYcKECURHR+N0OmnYsCGZmZm59gsMDMzzWpvNRpcuXfjll1945plnqFixIgDJya5fwL/++qt729mvuVz+/v65fikmJydjtVpZu3ZtnmXFc4YoHnjgAbp168avv/7K3Llzee2113j77bd57LHH8nVMLy8vDCP3n53nm1B6brtcbPioMOqWwuV0GnyxbB9vztlBpsNJsJ8319ctx/V1y9GxdiRhAb75eh+LxUKdCsHUqRDMve2r4XAanEjKwObthZ+PFZu3F15eFjKznBw8ncKeEynsO5nCvhMpBNisNKkURqNKoVQLD8TLy/X/SkpGFttjk9h6LJEDJ1OICvOnRmQgNcsFER3qz7bYRJ6esYltxxJ5/Jv1zN50TL0gUuIU+/BhsVgue+jDbDfddBOZmZlYLBa6dcsbnE6dOsWOHTuYMGECHTp0AFynwOaXl5cXX331Ff3796dz584sWrSI6OjoXBNDO3bsmOd1TqcTHx/XX3wOx+XPo2nWrBkOh4O4uDh33ecTExPDww8/zMMPP8zzzz/PhAkTeOyxx6hXrx7ff/89hmG4Q82yZcsIDg6mUqVKgKvn6NixY+73SkxMZN++fZesrXHjxsyfPz/XUIyn6paiJS4xnadmbHQPk3SpV443ejcm/DzzLC6X1ctChVC/PNt9vb2oWS6YmuWCL/kegTZvWlQpQ4sqZc77fIPoUH4a1p6PFu1m3ILdzPk7luV7T/H8zXW5q2WMO8SIFGfF61u7hLBarWzbts3973OVKVOG8PBwPv30U6Kiojh48CDPPffcZR9jypQp9OvXj+uvv55FixZRoUIFnn76aZ588kmcTifXXnstCQkJLFu2jJCQEO655x5iYmKwWCz88ssvdO/eHX9//3xPrKxduzYDBgxg0KBB7kmwJ06cYP78+TRu3JgePXowYsQIbr75ZmrXrs2ZM2dYuHAh9erVA+DRRx/lvffe47HHHmP48OHs2LGDl156iZEjR7rne1x//fVMmjSJnj17EhYWxqhRo/J18baXXnqJG264gRo1atC3b1+ysrKYPXs2zz777FXXLYXH7nASl5RBbEI6sQnpHE9MJyUji1S7g9SMLFIzHczfHsfplExs3l68cEt9BrapXOyGLXy9vRjRpTZd65fnmRmb2HoskedmbmbG2sOMvb0hdSuEmF2iyFVR+DBJSMiFf3l4eXkxbdo0Hn/8cRo2bEidOnV4//336dSp02Udw9vbm2+++Ya7777bHUBefvllIiMjee2119i7dy9hYWE0b96cf/3rXwBER0czevRonnvuOe69914GDRrEpEmT8n3MiRMn8sorr/DUU09x5MgRIiIiaNu2Lbfccgvg6lEZNmwYhw8fJiQkhJtuuol3330XgIoVKzJ79myeeeYZmjRpQtmyZbn//vt54YUX3O///PPPs2/fPm655RZCQ0N5+eWX89Xz0alTJ2bMmMHLL7/M66+/TkhICNddd51H6paCYXc42Xo0kbUHzrD2wBk2HIrnaEIaxnkme56rXlQIH/Rrmq+eiKKsQXQos4a3Z9Jf+3ln3k7WHjhDj/eX8sC11XiiS61i1+srksNinDuAbrLExERCQ0NJSEjI8wWdnp7Ovn37qFatGn5+ebs+5eo4nU4SExMJCQm5rDNLJLcr/Zza7XZmz55N9+7d3cNfpdHhM6n85+etLNl1Is9ZIgA+VgvlQ/yoEOJH+RA/gv28CfD1JsDXir+vlfIhfvRsEoXN+/LmZBX19j8an8Z/ft7KnL9jAageGchX97ehYtiF5zMVJ0W9/Us6T7T/xb6/z6XYLCJFgmEYfLvmEC//so3kDNcZJaH+Pu75Ec0rl6FmuSDCA31L5byH6DB/Pr6nBfO3HeffP2xh74kU+oz/i68faEP1SK05I8WLwoeImO54YjrPfb+JhdmnnrasUobRvRpQPyqkVAaNi7mhXnnqRYUw8POV7D2Rwl2fLGfyfW2oH615IFJ8qG9dREyTlG7nq+X7ufHdJSzccQJfby/+1b0u04e2o2HFUAWPC4gO8+fboe2oHxXCyeRM+n66nLUHTptdlki+qedDRAqVYRisOxjP9NUH+WXTMVIzXad1N64Uytt9mlCrfPGeJFpYIoJsfPNQW+6ftJo1B84w8LNVvNe3Kd0aVDC7NJFLUvgQkUKzeOcJxv66NdeKozUiAxnYtgoD21bBx6rO2MsR6u/DV/e3YejXa1my8wRDv1pLj8ZRvNSzPuWCNSlfii6FDxEpFHO2xDJ86jqynAZ+Pl70aBRN39YxtKxSptitw1GU+Pta+WxQS96eu4PPlu7j103H+HPnCf7VvR53t4pR20qRpPAhIgXu103HeHzaehxOg1saRzH29kaE+ut0Sk/x9fbi+e716NkkmudmbmLLEdeiZD+sP8J/+zQhpmyA2SWK5KI+ThEpUD9tOOIOHrc3q8h7dzdV8CggDSuG8uOj7XmhRz38fays3HeaWz5YypKdJy79YpFCpPAhIgVm5rrDPDl9Aw6nwZ0tKvHfPk3w1ryOAuVt9eKBDtWZ++R1NIkJIyHNzuCJq/hw4e48F2UUMYt+C4ipLBYLP/74o9llSAGYsvIAT83YiNOAfq1jeLN3Y6w6dbbQxJQNYPpDbenbKgbDgLd+38GjU9a5F3ATMZPCRyGKjY3lscceo3r16thsNmJiYujZsyfz5883u7QCN3r0aJo2bZpn+7Fjx7j55psLvyApMIZh8L8/dvHvH7ZgGHBP2yqMva2R1uwwgZ+Pldd7N+bV2xvhY7Xw25ZYbh23lO/WHiY1UyFEzKMJp4Vk//79tG/fnrCwMN566y0aNWqE3W7n999/Z9iwYWzfvt3sEk1RoYLWJChJHE6DUT9tYcrKgwA8fn1NnuxaW2dcmKx/m8rUqRDMo1PWsudECk/P2MiYWX/Ts2k0fVvF0KhiqP4bSaFSz0chefTRR7FYLKxatYrevXtTu3ZtGjRowMiRI1mxYgUABw8e5NZbbyUoKIiQkBDuuusujh8/7n6PnN6Dr776iqpVqxIaGkrfvn1JSkpy7/Pdd9/RqFEj/P39CQ8Pp0uXLqSkpACuK7uOGDEiV1233XYbQ4YMcT9u3LgxY8eOZdCgQQQFBVGlShVmzZrFiRMn3LU1btyYNWvWuF8zadIkwsLC+PHHH6lVqxZ+fn5069aNQ4cOuZ8fM2YMGzduxGKxYLFY3FfKPXfYZfPmzVx//fXu+h966CGSk/9ZE2LIkCHcdttt/Pe//yUqKorw8HCGDRuG3W6/qv8+cvXS7Q6GTVnHlJUHsVjg5VsbMPLGOvpSKyJaVCnDb09cxzPd6lC5bABJGVlMXXmQXuOWcftHf3HgVIrZJUopUvzDh2FAZoo5t3xO3jp9+jRz5sxh2LBhBAYG5nk+LCwMp9PJrbfeyunTp1m8eDHz5s1j79693H333bn23bNnDz/++CO//PILv/zyC4sXL+b1118HXEMY/fr147777mPbtm0sWrSIO+6447Inmb333nu0b9+e9evX06NHD+655x4GDRrEwIEDWbduHTVq1GDQoEG53jc1NZWxY8cyefJkli1bRnx8PH379gXg7rvv5qmnnqJBgwYcO3aMY8eO5fm5AFJSUujWrRtlypRh9erVzJgxgz/++IPhw4fn2m/hwoXs2bOHhQsX8uWXXzJp0iR3mJHCdyYlk583HmXAZyuZ83csvlYvPuzfnHvaVTW7NDlH2UBfhnWuyaKnOzH1wTbc2jQaX28vNhyKp+cHS1m4Pc7sEqWUKP7DLvZUeDXanGP/6yj45g0T59q92zXLvG7duhfcZ/78+WzevJl9+/YRExMDwOTJk2nQoAGrV6+mVatWgOuy95MmTSI42LUE9T333MP8+fMZO3Ysx44dIysrizvuuIMqVaoA0KhRo8v+sW6++WaGDh0KwKhRoxg/fjytWrWiT58+ADz77LO0a9eO48ePu4dN7HY748aNo02bNgB8+eWX1KtXj1WrVtG6dWuCgoLw9va+6DDL1KlTSU9PZ/Lkye6QNm7cOHr27Mkbb7xB+fLlAShTpgzjxo3DarVSt25devTowfz583nwwQcv+2eVK7MjNolfNx9jyc4TbDwc787hwTZvPhnUgmtqRJhboFyUl5eFa2pEcE2NCJ5LSOPRKetYfzCe+75czRM31OLx62tpjo4UqOLf81EM5KfnYdu2bcTExLiDB0D9+vUJCwtj27Zt7m1Vq1Z1Bw+AqKgo4uJcf600adKEG264gUaNGtGnTx8mTJjAmTNnLrvexo0bu/+d84V/dojJ2ZZzXABvb293QAKoW7duntovZdu2bTRp0iRX71D79u1xOp3s2LHDva1BgwZYrVb347PbQAre9NUH6f7+n7w/fxcbDrmCR+3yQTxwbTV+HN5ewaOYiQr1Z9pDbRnYtjKGAe/9sYv7v1xNQqqGMqXgFP+eD58AVw+EWcfOh1q1amGxWDwyqdTHJ/fiTBaLBafTCYDVamXevHn89ddfzJ07lw8++IB///vfrFy5kmrVquHl5ZUnCJ1vrsTZx8gZrz/ftpzjFraLtYEUHMMweHvuTsYt3A1Ah1oR9GwcTYfaEUSF+ptcnVwNm7eVV25rRNOYMvz7h80s3HGC1q/+Qdvq4XSsHUmnOpFUiwjU/B3xmOLf82GxuIY+zLjl83/EsmXL0q1bNz788EP35M+zxcfHU69ePQ4dOuSepAmwdetW4uPjqV+//mU0h4X27dszZswY1q9fj6+vLz/88AMAkZGRHDt2zL2vw+Fgy5Yt+X7vi8nKyso1CXXHjh3unwvA19cXh8Nx0feoV68eGzduzNVGy5Ytw8vLizp16nikTrkyGVkORkzf4A4ej19fk8n3teauVjEKHiXInS0q8f0j11AjMpCMLCeLd57gP79s5fq3F3PdWwuZvfnYpd9EJB8uO3wsWbKEnj17Eh0dnedMBbvdzrPPPkujRo0IDAwkOjqaQYMGcfSoST0TRciHH36Iw+GgdevWfP/99+zatYtt27bx/vvv065dO7p06UKjRo0YMGAA69atY9WqVQwaNIiOHTvSsmXLfB1j5cqVvPrqq6xZs4aDBw8yc+ZMTpw44Q4A119/Pb/++iu//vor27dv55FHHiE+Pt4jP5+Pjw+PPfYYK1euZO3atQwZMoS2bdvSunVrwDVctG/fPjZs2MDJkyfJyMjI8x4DBgzAz8+PwYMHs2XLFhYuXMhjjz3GPffc4x7qkcKXkGpn0Oer+GnDUby9LLzZu7HOYinBGlYM5Y+RHZn75HX8q3td2tcMx8dq4dDpNJ6Ytp61By5/KFfkXJcdPlJSUmjSpAkffvhhnudSU1NZt24dL774IuvWrWPmzJns2LGDXr16eaTY4qx69eqsW7eOzp0789RTT9GwYUO6du3K/PnzGT9+PBaLhZ9++okyZcpw3XXX0aVLF6pXr8706dPzfYyQkBCWLFlC9+7dqV27Ni+88AJvv/22exGv++67j8GDB7tDTfXq1encubNHfr6AgACeffZZ+vfvT/v27QkKCspVe+/evbnpppvo3LkzkZGRfPPNN+d9j99//53Tp0/TqlUr7rzzTm644QbGjRvnkRrl8qTbHUxcto+u7y5m5b7TBNm8mXhvK+5qFXPpF0uxZrFYqF0+mIeuq8GUB9qyYdSN3NSgAnaHwSNfryUuMd3sEqWYsxhXsdi/xWLhhx9+4LbbbrvgPqtXr6Z169YcOHCAypUrX/I9ExMTCQ0NJSEhgZCQkFzPpaens2/fPqpVq4afn9+Vli0X4HQ6SUxMJCQkBC+v/OfSSZMmMWLECI/1ohR3V/o5tdvtzJ49m+7du+eZ11KY0u0Opq8+xEeLdnM80dVDFVPWn0/vaUm9qJBLvLr4KirtX1QlZ2Rx+4fL2BWXTMsqZZj6YFt8vT03cq/2N5cn2v9i39/nKvAJpwkJCVgsFsLCws77fEZGRq4u+MTERMDVEOdOhrTb7RiGgdPp1ATDApCTQ3PaOL9y9tV/Exen04lhGNjt9lxn5VxKzufdrAXTnE6DHzYc5d35/4SOqFA/HulYjd7NKuLr7VWiF3Mzu/2LOpsXfNS/CXd8vJI1B84wZtYWRves57H3V/ubyxPtfzmvLdDwkZ6ezrPPPku/fv0umIJee+01xowZk2f73LlzCQjIfTZJzjoRycnJZGZmFkjNQq4VU/MjPT0dwzDcwbG0y8zMJC0tjSVLlpCVdfnXz5g3b14BVHVxR1Jgxj4r+5Jc8zjCfA26VnTStlwy3ic288fczYVek1nMaP/ipF9VCxO2ezFl1SGcp/bTtpxnr5Sr9jfX1bR/ampqvvctsGEXu91O7969OXz4MIsWLbpg+Dhfz0dMTAwnT54877DLoUOHqFq1qoZdCoBhGCQlJREcHKzJhFchPT2d/fv3ExMTc9nDLvPmzaNr166F1u2cnJHF+wv2MHnFQRxOgwBfK491rsE9bStj82CXenFgRvsXV+MW7uF/C/bgY7Xw6cDmXFsz/KrfU+1vLk+0f2JiIhEREeYNu9jtdu666y4OHDjAggULLlqEzWbDZrPl2e7j45OnARwOBxaLBS8vr8uakyD5kzNsktPGcmW8vLywWCzn/Qznx5W+7nIdOJXCXZ8sdw+xdG9UgRdvqV/qT50trPYvzp7oUodtscnM3Xqc+yevZUSX2gzrXBOrB1ZFVfub62ra/3Je5/FvmJzgsWvXLv744w/Cw68+EZ/rKjprRApccfl8Tl5+gOOJGcSU9WfSva34aECLUh88JH+8vCz8r28z+rSohNOAd+btZMjEVZxMznsKvcj5XHb4SE5OZsOGDWzYsAHAvXbDwYMHsdvt3HnnnaxZs4YpU6bgcDiIjY0lNjbWI3M0clLV5YwriRS2nM9nUf/rbfORBAAev74WneqUM7kaKW78fa281acJ/+3TBD8fL/7cdZIe7//Jyr2nzC5NioHLHnZZs2ZNrrUhRo4cCcDgwYMZPXo0s2bNAqBp06a5Xrdw4UI6dep05ZXiWj48LCzMfR2PgIAAzU3wIKfTSWZmJunp6Rp2uQKGYZCamkpcXBxhYWGXdaZLYXM6DbYedU0QblQp1ORqpDi7s0UlGlUM5dEpa9lzIoX+n63kg37N6N4oyuzSpAi77PDRqVOni3YrF3SXc85VUXUhMc8zDIO0tDT8/f0V6q5CWFjYRa/eWxTsO5VCckYWfj5e1IwMMrscKebqVAhm1vBrefb7Tfyy6RhPTFtPgK9VPWpyQcXuwnIWi4WoqCjKlSun88E9zG63s2TJEq677roiP2RQVPn4+BTpHo8cW7KHXOpFheBtVS+XXL1Amzf/69sMA/h10zEe/notk+9rQ+tqZc0uTYqgYhc+clit1mLxS744sVqtZGVl4efnp/BRwm0+7AofjSpqyEU8x+pl4d27mpKSkcWiHSe4f9JqvnmoLQ31OZNz6E8ekVIoZ7Kpwod4mq+3F+MHtKB1tbIkZWQx6ItV7I67vIULpeRT+BApZZxOg7812VQKkL+vlc8Ht6RRxVBOp2Qy8LNVHI1PM7ssKUIUPkRKmf2abCqFINjPhy/va03NckHEJqZz36TVJKZrnp64KHyIlDKbNdlUCknZQF8m3duKyGAb22OTGDZlHXaHLkApCh8ipY4mm0phqlQmgC8GtyLA18qfu07yr5mbi80qwFJwFD5ESpmcng+dgSCFpVGlUD7s3xwvC8xYe5gPFuw2uyQxmcKHSCmSa7KpwocUos51y/HybQ0B17Vgvl972OSKxEwKHyKlSM5kU5u3F7XKabKpFK4BbarwcMcaADz7/Sb+2n3S5IrELAofIqWIJpuK2f6vWx1uaRxFltNg6Ndr2XVca4CURvrtI1KKbNHiYmIyLy8L/+3ThFZVy5CUnsWQiauJS0o3uywpZAofIqWIVjaVosDPx8qn97SkWkQgR+LTuH/SGlIzs8wuSwqRwodIKeF0Gvx9xDXZVGe6iNnKBPoycUgrygb6svlIAk9+uxmnzsAtNRQ+REqJA6dTScrIwtfbi1rlNdlUzFc1IpAJg1pi8/ZiwY4TTN/rhUMJpFRQ+BApJc6ebOqjyaZSRLSoUob37m6KlwVWxHnxzPebtQpqKaDfQCKlRM5k08YacpEi5uZGUbzbpzFeFoOfN8XyyNfrSLc7zC5LCpDCh0gpoWXVpSjr3qgCD9RxYvP24o9tx7n/y9WkZGgSakml8CFSCjidhrvnQ5NNpahqUMbg80HNCfS1smz3KQZ9sYqENF0JtyRS+BApBfafStFkUykW2lQry9cPtCHEz5u1B85w9yfLiU3QOiAljcKHSAlnGAZv/b4DgKYxYZpsKkVes8plmD60HZHBNrbHJtF7/F/sjks2uyzxIP0WEinhZqw9zG9bYvH2svBij/pmlyOSL/WiQpj5yDVUz16I7M6P/2LdwTNmlyUeovAhUoLtP5nC6Fl/AzDyxto0qqT5HlJ8xJQNYMbD7WgSE0Z8qp3+E1awYPtxs8sSD1D4ECmh7A4nI6ZvIDXTQZtqZRl6XQ2zSxK5bOFBNr55sA2d6kSSbnfy4OS1rNh7yuyy5CopfIiUUB/M38WGQ/EE+3nzzt1NsXpZzC5J5IoE+HozYVBLejSKwuE0eHrGRpJ1Gm6xpvAhUgKt3n+acQt3A/Dq7Y2oGOZvckUiV8fH6sXrvV2f5cNn0hj76zazS5KroPAhUsKcSclkxLQNOA24o3lFejaJNrskEY8I9vPhrT6NAfhm1UEW7zxhckVypRQ+REqQLIeTx75Zz5H4NCqXDWBMrwZmlyTiUdfUiGDINVUBePa7TSSkahGy4kjhQ6QEeWPOdpbuPom/j5VPB7Ug2M/H7JJEPO7Zm+pSLSKQ2MR0xvz8t9nlyBVQ+BApIX5cf4QJf+4D4O27mlC3QojJFYkUDH9fK//t0wQvC8xcf4Q5W2LNLkkuk8KHSAmw5UgCz36/CYBhnWvQvVGUyRWJFKwWVcowtKPr9PF//7CZo/FpJlckl0PhQ6SYO5mcwUOT15CR5aRznUhGdq1jdkkihWJEl1rUiwrhVEomD321hrRMh9klST4pfIgUYw6nwWNT13M0IZ1qEYG817eZ1vOQUsPmbeXTe1pQNtCXLUcSefq7jRiGYXZZkg8KHyLF2LgFu1m+9xQBvq5fwqH+mmAqpUtM2QA+HtgCH6uFXzcd44MFu80uSfJB4UOkmFqx9xT/m78TgLG3N6RW+WCTKxIxR+tqZXn51oYAvDNvJ3O2HDO5IrkUhQ+RYuj0WQuJ9W5eidubVTK7JBFT9W1d2b3+x5PTN7L1aKK5BclFKXyIFDOGYfDMjI3EJqZTPTKQ/9yqhcREAF7oUY8OtSJIszu4/8vVOgOmCFP4EClmJi7bz/ztcfh6e/FBv2YE2rzNLkmkSPC2ejGuX3NqlgviWEI693y+ktMpmWaXJeeh8CFSjGw6HM9rv7kuqPVCj3o0iA41uSKRoiU0wIfJ97UmOtSPPSdSuHfSalJ0BdwiR+FDpJjYdDieQV+swu4wuLF+ee5pW8XskkSKpOgwfybf34YyAT5sPBTPw1+vJTPLaXZZchaFD5FiYNW+0/SfsJL4VDtNYsJ4q08TLBat5yFyITXLBTHx3tYE+Fr5c9dJRn67AYdTa4AUFZcdPpYsWULPnj2Jjo7GYrHw448/5nreMAxGjRpFVFQU/v7+dOnShV27dnmqXpFSZ/HOEwz6YiXJGVm0rV6WKQ+00XoeIvnQNCaMT+5xrQHyy6ZjvDp7m9klSbbLDh8pKSk0adKEDz/88LzPv/nmm7z//vt8/PHHrFy5ksDAQLp160Z6evpVFytS2szZcowHvlxNut21dPqke1sTpAmmIvnWoVYk79zVFIDPl+7jpw1HzC1IALjs32I333wzN99883mfMwyD9957jxdeeIFbb70VgMmTJ1O+fHl+/PFH+vbte3XVipQShmHw9cqDvPTTFpwG9GgUxbt3N8XXWyOlIperZ5NodsQmMW7hbp79fhO1ygVTP1pXfTaTR3+T7du3j9jYWLp06eLeFhoaSps2bVi+fLknDyVSYmVmOfnXD1t48UdX8LirZSXe79dMwUPkKjzZtTbX1Y4k3e5k6NdriE/VKbhm8mj/bWxsLADly5fPtb18+fLu586VkZFBRkaG+3FiomtVOrvdjt1u92R5cgk57a12N4fdbicxE+75YjXrDiVgscDTXWvx4LVVcTqycOqCnQVKn39zFUb7v927Ibd/vIJDp9N4/Jt1fDqwuS7EmM0T7X85rzV98Pi1115jzJgxebbPnTuXgIAAEyqSefPmmV1CqXQwGT7fYSU+MwF/q8GgWk4qJW3jt980Sa4w6fNvroJu//4x8G6ClSW7TvH4p7/To7JOwT3b1bR/ampqvvf1aPioUKECAMePHycqKsq9/fjx4zRt2vS8r3n++ecZOXKk+3FiYiIxMTHceOONhIRoTK4w2e125s2bR9euXfHx0dkUhem3LbGM+34LGVlOqkcE8PGAZlSLCDS7rFJFn39zFWb7l6t9jKe/28zcI17c0bE5N9QrV6DHKw480f45Ixf54dHwUa1aNSpUqMD8+fPdYSMxMZGVK1fyyCOPnPc1NpsNm82WZ7uPj49+AZhEbV94DMPg48V7eWPOdgDqhzn5emgbygar188s+vybqzDa/86WldlyNIlJf+3n/2ZuYfYTHahURv/PwdW1/+W87rJnsCUnJ7NhwwY2bNgAuCaZbtiwgYMHD2KxWBgxYgSvvPIKs2bNYvPmzQwaNIjo6Ghuu+22yz2USIlmdzj51w+b3cFjUNvKPFjXSbCfvvhECtq/utejSUwYielZDJ+6XiugFrLLDh9r1qyhWbNmNGvWDICRI0fSrFkzRo0aBcD//d//8dhjj/HQQw/RqlUrkpOTmTNnDn5+fp6tXKQYS0y3c9+k1Xyz6hBeFnipZ31e7FEXzX0TKRy+3l6M69eMED9vNhyK563ft5tdUqly2cMunTp1wjAuvEStxWLhP//5D//5z3+uqjCRkupkcgYDP1vJ9tgk/H2sfNCvGV3ql9dZFiKFLKZsAG/1acLQr9Yy4c99tKkWTpf65S/9QrlqWjhApBCdSMqg36cr2B6bRGSwjRkPt9MvOxETdWtQgfvaVwPgqRkbORKfZnJFpYPCh0ghiUtKp9+EFeyKS6ZCiB/fDm1Hw4qhZpclUuo9d3NdmlQKJSHNzvCp67A7NP+joCl8iBSCuMR0+n26gt1xyUSF+jHtobY6lVakiPD19mJc/+YE+3mz/mA8L/+y1eySSjyFD5ECdjwxnb6frmDPiRSis4NHVQUPkSIlpmwA72ZfgG7y8gNMX33Q3IJKOIUPkQK0bPdJbh23jL0nU6gY5s+0h9pRJVzBQ6Qo6lK/PCO71gbgxR//Zt3BMyZXVHIpfIgUgHS7g//8vJUBn60kNjGd6hGBTHuoLZXDtZCRSFE2vHNNujUoT6bDycNfrSUuMd3skkokhQ8RD/v7aAI9P1jKF8v2ATCwbWV+efxaYsoqeIgUdV5eFt6+qym1ygURl5TBw1+vJSNLV3X0NIUPEQ8xDIMJS/Zy24fL2BWXTGSwjYn3tuKV2xoR4Gv6NRxFJJ+CbN5MGNSSED9v1h2M56Wf/r7o+lZy+RQ+RDzgTEomD3y5hrGzt2F3GHRrUJ7fR1xH5zq6YJVIcVQ1IpAP+jfHywLTVh/i86X7zC6pRFH4ELlKaw+cocf7fzJ/exy+3l6Mvb0hHw9sQdlAX7NLE5Gr0LF2JP/qXg+AsbO3MWdLrMkVlRwKHyJXyOk0+HTJHu7+ZDlHE9KpFhHID49ew4A2VbBYdJEWkZLg/murcU/bKhgGjJi+ng2H4s0uqURQ+BC5ArEJ6QyeuIpXZ28ny2nQs0k0Pz92LQ2itWKpSElisVh4qWd9OteJJN3u5IEvV3PodKrZZRV7Ch8il+nXTcfo9t4S/tx1Ej8f1zDL+32bEmTTpFKRksjb6sUH/ZtTLyqEk8mZ3DdpNQlpuhDk1VD4EMmnxHQ7I6dvYNjUdSSk2WlUMZRfHuugYRaRUiDI5s0XQ1pSPsTGrrhkhk9dh8OpM2CulMKHSD7sjkui+//+ZOb6I3hZXAsRff/INdQsF2R2aSJSSKJC/fliSCv8faz8uesk78/fZXZJxZbCh8glbDmSwF2frODwmTRiyvrz7dB2PN2tDr7e+t9HpLRpEB3Ka3c0AuD9BbtYsvOEyRUVT/rtKXIRq/efpt+nKzidkkmjiqH8NOxaWlYta3ZZImKi25pVpH+byhgGPDFtPUfj08wuqdhR+BC5gMU7T3DP5ytJysiidbWyTH2wjdbuEBEARt1Sn4YVQziTamf41HXYHU6zSypWFD5EzmPOlmM88OVq0u1OOtWJ5Mt7WxPs52N2WSJSRPj5WPmofwuCs5dgf/237WaXVKwofIicY8nOEwyfuh67w6BHoyg+vacl/r5Ws8sSkSKmcngA/+3TBIDPl+5jzpZjJldUfCh8iJxl69FEHp2yzr1w2Pv9mmliqYhcULcGFXjouuoA/N93mzh8RguQ5Yd+q4pkO5aQxn2TVpOckUXb6mX5b5/GWL20foeIXNwz3erQJCaMxPQsRkzbQJbmf1ySwocIrgXE7p24mtjEdGqVC+KTgS2xeWuoRUQuzcfqxQd9mxFk82bNgTO8v2C32SUVeQofUuplZjl59Ot1bI9NIjLYxsR7WxEaoMmlIpJ/lcMDGHt7QwDGLdjFir2nTK6oaFP4kFLNMAyen7mZpbtPEuBrZeKQVlQqE2B2WSJSDN3atCJ9WlTCacCIaRs4k5JpdklFlsKHlGrv/bGL79cdxupl4cP+zWlYUVelFZErN7pXA6pHBBKbmM7/fb8Jw9D1X85H4UNKrW/XHOJ/2ddmePnWhnSuW87kikSkuAu0ebvOkrN6MW/rcb78a7/ZJRVJCh9SKv256wT/mrkZgGGda9C/TWWTKxKRkqJhxVCeu7kuAGNnb2PdwTMmV1T0KHxIqbPtWCKPfO1ay+PWptE8fWMds0sSkRLm3vZV6d6oAnaHwbAp6ziVnGF2SUWKwoeUKgdPpXLvxH/W8njzzsZYLFrLQ0Q8y2Kx8EbvxlSPCORYQjojpm/A4dT8jxwKH1IqHE9MZ9RPW+jyzmJiE9OpqbU8RKSABfv5MH5gC/x9rPy566R7jpkofEgJF5eUzn9+3kqHNxcyefkBMh1O2lYvy5f3tdZaHiJS4OpUCObVO1zrf3ywYBeLdsSZXFHR4G12ASIFZd7W4zz+zXrS7A4AWlYpw8gba3NNjQiTKxOR0uT2ZpVYs/8MU1YeZMT0Dfz6eAcqhvmbXZap1PMhJdKiHXEMm7KONLuDJjFhTL6vNTMebqfgISKmGNWzPo0rhRKfaufxb9ZjL+XXf1H4kBLnrz0nGfrVWjIdTno0iuL7h9txXe1ITSwVEdPYvK2M69ecYJs3aw+c4d15O80uyVQKH1KirD1wmge+XENGlpMu9crx7t1N8bbqYy4i5qscHsDrvRsD8NGiPSzZecLkisyj38pSYmw+nMCQL1aTmumgQ60IxvVvjq+3PuIiUnT0aBzFwLauRQ1HfruBuMR0kysyh34zS4mweOcJBn6+kqSMLFpXK8un97TEz0en0YpI0fNCj/rUrRDMyeTMUrv+h8KHFGtZDidvztnO4C9WkZBmp3nlML4Y0gp/XwUPESma/HysjOvfnABfK3/tOcWHC3ebXVKhU/iQYutYQhr9Jqzgo0V7ABjYtjJTH2xLkE1nkItI0VazXBCv3OZa/+N/83exIzbJ5IoKl8KHFEuLdsTR/X9/snr/GYJs3ozr34xXbmukoRYRKTbuaF6Jbg3K43AavPLrVgyj9Ay/KHxIsTP371ju/3INZ1LtNIgO4ZfHruWWxtFmlyUictn+3b0+vlYv/tx1kgXbS8/qpx4PHw6HgxdffJFq1arh7+9PjRo1ePnll0tVopOCs2z3SYZPXY8j+4q03z9yDVUjAs0uS0TkilQOD+C+a6sBMPbXbWRmlY7FxzwePt544w3Gjx/PuHHj2LZtG2+88QZvvvkmH3zwgacPJaXMuoNneHDyGjIdTro1KM/bfZpomEVEir1hnWsQEWRj78kUJi/fb3Y5hcLj4eOvv/7i1ltvpUePHlStWpU777yTG2+8kVWrVnn6UFKKbDuWyJAvVrnX8Hi/XzMtHiYiJUKwnw/PdKsNuCafnk7JNLmigufx0wKuueYaPv30U3bu3Ent2rXZuHEjS5cu5Z133jnv/hkZGWRkZLgfJyYmAmC327Hb7Z4uTy4ip72LWrvvP5XCPZ+vJjE9i2YxoYzr2xgvw4ndXrK6J4tq+5cWan9zlfb2v7VxBSYt28+22CT++/s2xvSsX6jH90T7X85rLYaHJ2M4nU7+9a9/8eabb2K1WnE4HIwdO5bnn3/+vPuPHj2aMWPG5Nk+depUAgICPFmaFEMHkmDCDitJdgsVAwyGN3AQoDNpRaQE2p0AH2z1xoLB/zVxEF3MvgJTU1Pp378/CQkJhISEXHRfj4ePadOm8cwzz/DWW2/RoEEDNmzYwIgRI3jnnXcYPHhwnv3P1/MRExPDyZMnL1m8eJbdbmfevHl07doVHx8fs8vhty2xPPP9FjKynNQtH8SkIS0ID7KZXVaBKWrtX9qo/c2l9ncZ/s0Gft8aR6uqZfjq3pZYvQrngpieaP/ExEQiIiLyFT48/jfkM888w3PPPUffvn0BaNSoEQcOHOC11147b/iw2WzYbHm/UHx8fEr1B9BMZre9YRh8tGgPb/2+A4Dr65bj/X7NSs3iYWa3f2mn9jdXaW//F25pwJJdp1i9/wwfLd7HyBvrFOrxr6b9L+d1Hp+xl5qaipdX7re1Wq04nSVrfF4KRmaWk6dnbHIHj3vbV2XCoJalJniISOkWUzaA1+5oBMAHC3ezaEfJXPvD4+GjZ8+ejB07ll9//ZX9+/fzww8/8M4773D77bd7+lBSwpxIymDAZyv4ft1hrF4WXr6tIS/1bFBo3Y4iIkXBbc0qMqBNZQwDnpy+gaPxaWaX5HEe/3Pygw8+4MUXX+TRRx8lLi6O6Ohohg4dyqhRozx9KClBthxJ4KHJaziakE6wnzfj+jenY+1Is8sSETHFi7fUZ9PhBDYfSWDY1HVMf6gdvt4lZ3kBj/8kwcHBvPfeexw4cIC0tDT27NnDK6+8gq+vr6cPJSXEzxuPcufHf3E0IZ3qEYH8OKy9goeIlGp+PlY+GtCcED9v1h+M57XftpldkkeVnBglxY7DafDW79t57Jv1pNuddKwdyQ/D2lMjMsjs0kRETBdTNoC372oKwMRl+5mz5Zi5BXmQwoeY4nhiOvd8vpIPF+4BYOh11fliSCtC/UvvLHcRkXN1rV+eoR2rA/DiT3+TlF4yFmFT+JBCt2D7cW7+35/8tecU/j5W3ru7Kc93r6eJpSIi5zGya22qRQRyIimD9+fvMrscj1D4kEKTkeXg5V+2ct+kNZxOyaR+VAi/PH4ttzWraHZpIiJFls3byqjs5dYnLtvP7rgkkyu6egofUihiE9K5c/xyPl+6D4Ah11Rl5qPXaH6HiEg+dK5Tji71ypPlNBg9ayseXpy80Cl8SIHbHZdE7/F/sflIAmUCfPhsUEtG92qAn4/V7NJERIqNUbfUx9fbi6W7TzJnS6zZ5VwVhQ8pUGsPnOHOj5dzJD6NahGBzBp+LV3qlze7LBGRYqdyeAAPX+eafPrKr9tIy3SYXNGVU/iQAjN/23EGfLaC+FQ7TWLC+O7hdsSULWaXaRQRKUIe6VSTimH+HIlPY/yi3WaXc8UUPqRAfLvmEA99tZZ0u5NOdSL55sE2JfqKtCIihcHf18qLt9QD4OMlezlwKsXkiq6Mwod43C+bjvLs95twOA16N6/EhEEtCfDVheFERDyhW4MKdKgVQWaWk9Gz/i6Wk08VPsSjVuw9xcjpGzEMGNi2Mv/t0xgfqz5mIiKeYrFYGN2rAT5WCwt3nGDu1uNml3TZ9K0gHrMjNokHJ68h0+GkW4PyjOnVEItFC4eJiHhajcggHsqefDpm1t+kZmaZXNHlUfgQjziWkMaQiatISs+iZZUy/K9vM61YKiJSgIZ3rkXFMH+OJqTzwYLiNflU4UOuWkKanSFfrOZYQjo1IgP5bHBLreEhIlLA/H2tjO7VAIAJS/YWq5VPFT7kqsQlpjPoi1XsOJ5EuWAbX97XmrAAX7PLEhEpFbrWL0+XeuXIchq8+GPxmXyq8CFXbPPhBHqNW8bGQ/GE+vsw8d5WVCqjdTxERArTSz0bYPP2YvneU8zaeNTscvJF4UOuyM8bj9Lnk7+ITXQNtfw0rD0NokPNLktEpNSJKRvAY9fXBFwrnyal202u6NIUPuSyOJ0G78zdwWPfrCfd7qRznUh+GNaeqhGBZpcmIlJqPXhddapFBHIiKYOvVxw0u5xLUviQfEu3O3jsm/W8nz2r+qHrqvPZ4FaE+PmYXJmISOlm87YyrLOr9+OLZftItxft674ofEi+nEnJZOBnK/l18zF8rBb+26cJ/+peT6fTiogUEb2aRBMV6seJpAx+WH/E7HIuSuFDLunAqRR6j/+LNQfOEOznzeT72nBni0pmlyUiImfx9fbi/murAfDpkr04nEX3zBeFD7moDYfiueOjv9h7MoWKYf58/8g1tKsRbnZZIiJyHv1aVybU34d9J1OY+3es2eVckMKHXNDinSfo++lyTqVk0iA6hB8evYba5YPNLktERC4g0ObNoHZVAPh48Z4iu+6Hwoec14Ltx3nwyzWk2510qhPJt0PbUS7Ez+yyRETkEgZfUxWbtxcbDyewfO8ps8s5L4UPyWP+tjiGfrWWTIeTmxpU4NN7WhJo8za7LBERyYeIIBt3tYwB4OPFe02u5vwUPiSXjacsDJ+2EbvDoEejKD7o3wxfb31MRESKkwc7VMfLAkt2nuDvowlml5OHvlXE7bctsUza6UWW06Bnk2j+17cpPlZ9REREipvK4QH0aBwNwCdFsPdD3ywCwB9bj/PkjM04sXBrkyjevasJ3goeIiLF1sMdqwPw6+ZjnEzOMLma3PTtIqw9cJphU9fhcBq0jHDyxh0NFTxERIq5BtGhNK4UisNpMGdL0TrtVt8wpdzO40ncN2kNGVlOOtWOoH8Np1YtFREpIXo0igJg9uZjJleSm8JHKXYkPo1Bn68iIc1Os8phvH93E9ThISJScnTPDh8r9p4qUkMv+qoppc6kZDLo85XEJqZTs1wQXwxuhb+v1eyyRETEg2LKBtC4UihOgyI19KLwUQolpNkZMmk1e06kEBXqx+T7WlMm0NfsskREpAAUxaEXhY9S5nRKJv0nrGDjoXjCAnyYfF9rosP8zS5LREQKSFEcelH4KEXiktLp9+kK/j6aSHigL1MfaEstXatFRKREK4pDLwofpcSxhDT6frKCHceTKBdsY/rQttSPDjG7LBERKQQ5Qy+/bioaQy8KH6XAodOp3PXJcvaeTKFimD/fDm1HzXLq8RARKS1yhl5W7isaQy8KHyVcbEI6fT9dwaHTaVQJD2D60LZUjQg0uywRESlERW3oReGjBDudksnAz1dyJD6NquEBfDu0HZXKBJhdloiImKAoDb0ofJRQyRlZDJm4it1xyVQI8ePrB9pQPsTP7LJERMQkRWnoReGjBEq3O3jwyzVsOpxA2UBfvn6gtXo8RERKuaI09KLwUcLYHU6GT13P8r2nCLJ58+W9rTW5VEREgKIz9FIg4ePIkSMMHDiQ8PBw/P39adSoEWvWrCmIQ8k5Xpr1N39sO47N24vPBrekUaVQs0sSEZEiImfo5WRyBhlZDtPq8Pb0G545c4b27dvTuXNnfvvtNyIjI9m1axdlypTx9KHkHFNXHmTqyoNYLDCuf3PaVg83uyQRESlCYsoGsOCpjlSLCMRiMe8K5h4PH2+88QYxMTFMnDjRva1atWqePoycY+2B07w0awsAz3SrQ9f65U2uSEREiqLqkUFml+D58DFr1iy6detGnz59WLx4MRUrVuTRRx/lwQcfPO/+GRkZZGT8M+s2MTERALvdjt1u93R5JdLxxHQe/motdofBzQ3K88A1la+o7XJeo3Y3h9rfXGp/c6n9zeWJ9r+c11oMwzCu+Ejn4efnOp1z5MiR9OnTh9WrV/PEE0/w8ccfM3jw4Dz7jx49mjFjxuTZPnXqVAICdIbGpWQ54YO/rexPthAVYPBkQwc2q9lViYhIaZOamkr//v1JSEggJOTil+/wePjw9fWlZcuW/PXXX+5tjz/+OKtXr2b58uV59j9fz0dMTAwnT568ZPGlnWEY/PunrcxYe4RQf29mPtyWymWvPLDZ7XbmzZtH165d8fHx8WClkh9qf3Op/c2l9jeXJ9o/MTGRiIiIfIUPjw+7REVFUb9+/Vzb6tWrx/fff3/e/W02GzabLc92Hx8ffQAvYfLy/cxYewQvC3zQrzk1ynvmzBa1vbnU/uZS+5tL7W+uq2n/y3mdx0+1bd++PTt27Mi1befOnVSpUsXThyrVlu0+yZiftwLw7E11ua52pMkViYiI5I/Hw8eTTz7JihUrePXVV9m9ezdTp07l008/ZdiwYZ4+VKm172QKj05Zh8NpcEfzijx0XXWzSxIREck3j4ePVq1a8cMPP/DNN9/QsGFDXn75Zd577z0GDBjg6UOVSonpdh74cjUJaXaaVQ7j1dsbmXqutoiIyOXy+JwPgFtuuYVbbrmlIN66VHM4DR6bup49J1KICvXjk3ta4OejU1tERKR40bVdipHXZm9j8c4T+Pl4MWFQS8oF6yq1IiJS/Ch8FBPztx3ns6X7AHi7T1MaVtQ1W0REpHhS+CgGEtLs/OuHzQA82KEaPRpHmVyRiIjIlVP4KAbG/rqV44kZVI8I5Kkb65hdjoiIyFVR+CjiFu88wbdrDmOxwJt3NtYEUxERKfYUPoqwpHQ7z3+/CYAh11SlZdWyJlckIiJy9RQ+irDXf9vO0YR0KpcN4JluGm4REZGSQeGjiPpr90mmrDwIwBu9GxPgWyBLsoiIiBQ6hY8iKDkji2dnuoZbBratTLsa4SZXJCIi4jkKH0XQ2F+3ceh0GhXD/Hnu5npmlyMiIuJRCh9FzKIdcXyzyjXc8lafxgTZNNwiIiIli8JHEZKQaufZs85uuaZGhMkViYiIeJ7CRxEyatYW92Jiz95U1+xyRERECoTCRxExe/MxftpwFC8LvH1XE/x9tZiYiIiUTAofRcCJpAz+nX3tlkc61aBZ5TImVyQiIlJwFD6KgBd/3MKZVDt1KwTzxA21zS5HRESkQCl8mGzB9uPM+TsWq5eFd+5qiq+3/pOIiEjJpm86E6XbHbw0628A7r+2GvWjQ0yuSEREpOApfJjoo4W7OXQ6jQohfjxxQy2zyxERESkUCh8m2XsimY8X7wXgpZ71CdRiYiIiUkoofJjAMAxemvU3mQ4n19WO5KaGFcwuSUREpNAofJhg9uZY/tx1El9vL/7TqwEWi8XskkRERAqNwkchS87I4j+/uCaZPtKxBlUjAk2uSEREpHApfBSy//2xk+OJGVQJD+CRTjXMLkdERKTQKXwUoj0nkpm4bD8Ao3s2wM9HS6iLiEjpo/BRiF7+ZStZToPOdSLpXLec2eWIiIiYQuGjkCzYfpxFO07gY7Xw4i31zS5HRETENAofhSAzy8nLv2wD4N721ageGWRyRSIiIuZR+CgEk/7ax76TKUQE2Xjs+ppmlyMiImIqhY8CFpeUzvvzdwPwfzfVIdjPx+SKREREzKXwUcDemrOD5IwsmlQK5c7mlcwuR0RExHQKHwVo8+EEZqw9DMBLvRrg5aWVTEVERBQ+CtB7f+wE4Lam0TSvXMbkakRERIoGhY8CsvlwAvO3x+FlgSe61Da7HBERkSJD4aOA/G/+LgBubVqRarp+i4iIiJvCRwHYciSBP7Ydx8sCw3VqrYiISC4KHwXg/exej55NoqmhBcVERERyUfjwsK1HE5m79TgWC1pQTERE5DwUPjwsp9fjlsbR1CwXbHI1IiIiRY/Chwdtj01kzt+x6vUQERG5CIUPD/ogexn17g2jqF1evR4iIiLnU+Dh4/XXX8disTBixIiCPpSpdsclM3vLMQAeu0G9HiIiIhdSoOFj9erVfPLJJzRu3LggD1MkfLFsH4YBXeqVp26FELPLERERKbIKLHwkJyczYMAAJkyYQJkyJXtp8TMpmcxc57qGywMdqplcjYiISNHmXVBvPGzYMHr06EGXLl145ZVXLrhfRkYGGRkZ7seJiYkA2O127HZ7QZXnUV8t30e63Un9qGCaVwouNnWfK6fu4lp/caf2N5fa31xqf3N5ov0v57UFEj6mTZvGunXrWL169SX3fe211xgzZkye7XPnziUgIKAgyvOoLCd8ts4KWGgeGM9vv/1mdklXbd68eWaXUKqp/c2l9jeX2t9cV9P+qamp+d7X4+Hj0KFDPPHEE8ybNw8/P79L7v/8888zcuRI9+PExERiYmK48cYbCQkp+nMnftxwlMSVWygXbOP5AR3w9S6+JxDZ7XbmzZtH165d8fHxMbucUkftby61v7nU/ubyRPvnjFzkh8fDx9q1a4mLi6N58+bubQ6HgyVLljBu3DgyMjKwWq3u52w2GzabLc/7+Pj4FPkPoGEYTFp+EIDB11Ql0D/vz1EcFYe2L8nU/uZS+5tL7W+uq2n/y3mdx8PHDTfcwObNm3Ntu/fee6lbty7PPvtsruBR3K3cd5q/jybi5+NF/9aVzS5HRESkWPB4+AgODqZhw4a5tgUGBhIeHp5ne3H3+dJ9ANzRvBJlAn1NrkZERKR4KL4TFEy2/2QKf2w7DsB97XV6rYiISH4V2Km2Z1u0aFFhHKZQTcxeVKxTnUhqlgsyuxwREZFiQz0fVyA5I4vv1mYvKnZtdZOrERERKV4UPq7AzxuPkpLpoHpEIO1rhptdjoiISLGi8HEFpq1ynV7bt3UMFovF5GpERESKF4WPy7T1aCIbDyfgY7VwR/NKZpcjIiJS7Ch8XKbpq129Hl3rlyciqGQsKiYiIlKYFD4uQ7rdwQ/rjwDQt5UWFRMREbkSCh+XYfbmYySmZ1GpjD/X1owwuxwREZFiSeHjMkxbfQiAu1vG4OWliaYiIiJXQuEjn/acSGbVvtN4WeDOlppoKiIicqUUPvJpenavR+c65YgK9Te5GhERkeJL4SMfMrOcfJ+9omlfXb1WRETkqih85MMf245zKiWT8iE2OteJNLscERGRYk3hIx9yhlz6tIjB26omExERuRr6Jr2EE0kZ/LnrBAB3ttBEUxERkaul8HEJv2w6itOApjFhVI0INLscERGRYk/h4xJ+3HAUgNuaRptciYiISMmg8HER+06msPFQPFYvC7c0UfgQERHxBIWPi/hpg+s6LtfWjNBF5ERERDxE4eMCDMPgp5whl2bq9RAREfEUhY8L2HQ4gX0nU/D3sXJj/QpmlyMiIlJiKHxcwI/ZQy5d65cn0OZtcjUiIiIlh8LHeWQ5nPy88RigIRcRERFPU/g4j7/2nOJkcgZlAnzoUEvLqYuIiHiSwsd55Ay59GgchY+WUxcREfEofbOeIy3Twe9bYgG4rWlFk6sREREpeRQ+zvHHtuOkZDqoVMafFlXKmF2OiIhIiaPwcY452b0evZpEY7FYTK5GRESk5FH4OIvd4WTJTtcVbLvWL29yNSIiIiWTwsdZ1uw/Q1JGFuGBvjSpFGZ2OSIiIiWSwsdZFmw/DkDHOpF4eWnIRUREpCAofJxlwfY4AG6oqyEXERGRgqLwke3AqRT2nEjB28tCh9oRZpcjIiJSYil8ZMvp9WhZtQwhfj4mVyMiIlJyKXxk05CLiIhI4VD4AFIysli59zQAneuWM7kaERGRAmIYsO4rSDpuahkKH8DS3SfJdDipXDaAGpGBZpcjIiJSMI6uh1nD4YMWkJVpWhkKH8DC7CGX6+uW06qmIiJScm2b5bqveQN4+5pWRqkPH4ZhuOd7XK8hFxERKakMA7Zmh496PU0tpdSHj7+PJhKXlEGAr5U21cuaXY6IiEjBOLEdTu8Bqy/UutHUUkp9+Mjp9bi2ZgQ2b6vJ1YiIiBSQnF6PGteDX4ippSh8aMhFRERKg20/u+5NHnKBUh4+TiZnsPFwPKBTbEVEpAQ7vReObwaLFep0N7saz4eP1157jVatWhEcHEy5cuW47bbb2LFjh6cP4xF/7jqBYUCD6BDKh/iZXY6IiEjB2PaL677qtRBg/vxGj4ePxYsXM2zYMFasWMG8efOw2+3ceOONpKSkePpQV23FHtfCYtfW1LVcRESkBNtWNM5yyeHt6TecM2dOrseTJk2iXLlyrF27luuuu87Th7sqK/edAtBZLiIiUnIlHoXDq13/rnuLubVk83j4OFdCQgIAZcue/ws+IyODjIwM9+PExEQA7HY7dru9wOqKTUxn/6lUvCzQtGJwgR6ruMhpA7WFOdT+5lL7m0vtX3C8/p6FFXBWao3DPwLO08aeaP/Lea3FMAzjio90CU6nk169ehEfH8/SpUvPu8/o0aMZM2ZMnu1Tp04lICCgoEpjzQkLX+22EhNo8HRjR4EdR0RExEzX7HqNyORtbInux57yNxfYcVJTU+nfvz8JCQmEhFz8VN4CDR+PPPIIv/32G0uXLqVSpUrn3ed8PR8xMTGcPHnyksVfjRd+2sr0NYe575oqPH9znQI7TnFit9uZN28eXbt2xcfHx+xySh21v7nU/uZS+xeQ1FN4v1cPi+HEPmwthFU5726eaP/ExEQiIiLyFT4KbNhl+PDh/PLLLyxZsuSCwQPAZrNhs9nybPfx8SnQD+DqA2cAaFczUh/0cxR028vFqf3NpfY3l9rfw/bMA8MJFRrjE1nzkrtfTftfzus8Hj4Mw+Cxxx7jhx9+YNGiRVSrVs3Th7hqcYnp7D2RgsUCratqsqmIiJRQ7rNceplbxzk8Hj6GDRvG1KlT+emnnwgODiY2NhaA0NBQ/P39PX24K7Jyn+sU23oVQggNUMIWEZESxjBgxXjY/Yfrcf0SHj7Gjx8PQKdOnXJtnzhxIkOGDPH04a6ITrEVEZESKzMVfn4cNs9wPW4xBCKL1tzGAhl2KepW7nX1fLSpFm5yJSIiIh50Zj9MHwix2Uup3/QatH7I7KryKPB1Poqak8kZ7IpLBqBNNfV8iEgp48iC9HhIPQ1pZyDtNGQkQ1Z69i3DdZ8eDymnIOUEpJ507W/xAh9/8LaBtz94+4LT4boZDnBmuSY3Wn1dN28bWG2ue98A8Al0vd43+94nIPvmj8XLl8jELVj2B4GPzfXF6WUFLGCxuO4BrD7gHwb+ZV3vY7GY1pRFSupp2P4rzHvR9d81MBL6THItp14ElbrwsSp7vkfdCsGUCfQ1uRoRKdWcDkg5CcmxkHTcdZ92BhyZ4LC7bs7s+6wM182RfW84c7+XYfwTIOypYE8He1r2e51zK4K8gWsA9lzGi7x8wL+M6/Lw3v7g4wfefq5gY/UFL2/Xzerjuvf2y94ne9/s4INPYHY4yr55+/4TmnKClJfVFb68vF3/znl/s8KPww6n9sDOOa7boZX/fCYqtoC7voLQiubUlg+lLnys3Js930O9HiLiCU4nZCZDZorrPiPJdZ96GpLjICUu+/4EpMVDRiKkJ0JGgmvfc0NEYbGFQkAZ15e3LTi7J8Pm+oL29gW/MAgIh8AI11/R/tm/M7PSXOEnJ9h4WbO/hK3/fBlnZfwTdHJ6UuyprrkI9lRXW9nTskNSGtjTcGamkBR/mpDgQCyG09WL4nQABrhH8w3X+6XHu97baXe1b0qcKU0Iluw2y+nhyQk3Oe3olx1arGe1k1d2iLH+82+rDWxB4Bv0z73DDukJ2Z+XBNctNbsnKuWkqw3OVb4h1L8NrnnMVUcRVvrCR3bPR5vqmu8hUqoYhisUpCf+8ws9Ld71SzwtHq/U09Q7uhWvP1a4hhDcvQ/n9BrY013BIecLIT2Rs74dr4DF9eUeXB6CKri+7K2+2X+t+4DVO/ux7awvuuy/us+V6y97/+wvQNs/75fzpegX5nrfIsRht7No9my6d+9+6fUiDMMVXNLOuEJeZrIrxGSlu8OMK5xkDwXl6j1K+6dXKCstOxBlh6GccJQTms6+P+9/47N6m8xg9YWqHaDOzVC7G4RVNqeOK1C0Pn0F7ExKJttjkwBorZ4PkaLLMLK/6P/5y5jMlH/+inbfp5/1F3ama0giM/Wf3obkOEg+nv0FdfFeBitQG+D4FdZs8QLf4H/+cvUvA0Hlsm/ls3sPsnsZ/ELBFuIaLgiIKHJBoMizWFzzPXwDIfTCi1h6lGHkntuS85nL+QxmpWc/Tsv9Gc2ZE+PMOmtejJF9c7q2ZaW75t1kJmffJ7nCpl/IOZ+VcNfnKCDCFVL9y2TPiyl+StUnftV+V69HrXJBRATlXVVVRAqA0+masJh4FJJiXcEgI+mfX7KZKa4ehJTsSY2pJ13dywU1N8Fi/eeXul+Ya/KiXxgOWwj7Dx+nas3aWN29Bd5njf37ZP/b95/X2nK+HIJdPQ2a/FhyWSzZIdEbsAGBJhdUvJWq8LFir9b3ELkqGcnZY84nXMMNOV3YWdm9E6mnXD0NyXGuoJEc55pE6cy68mNarP+cHZEzju4eU7eddVZF9r2Pv+uvw5zehqDyrr8Y/UJcYeECIcFpt7Nl9mwqX98dq5b3FilQpSp8aH0PkbM4HXBwhSsspJ81hyEjMbtnIueW6JobkXLCNQxyRSyuEBBcwTUMYQs5a4JdsOtxQDgEhrvuA8Jd23wDXT0OIlKilJrwkZBqZ1tsIqCeDxH2Lobf/wXHt1z+a739ISjSNdzgPr0x+z4g3BUyzr6FREFgOc1rEBG3UvPbYFdcEr5WLyqW8adccNE+BUmkwJzeC3NfhO2/uB7bQqFCo+w5DDm3kOzeiOybb/YEyaBIV4iwBZn7M4hIsVdqwkfLqmXZNPpGYhNMOiVKpDAZhus0xMSj2bcjrl6OdZNdEzktVmh1P3R6HgLUEygihavUhA8Am7eVKuGaoSzFnNPhmqeRcBgSDrnuE49C0jHXKplJx1zPX2jtgRo3QLdXoVzdwq1bRCRbqQofIkWO0+m+zoYl6TjlE9Zj2ZQEmYnZ190488+ppymn/vl3fs8eCYiAkGgIqeiae1GnB9S8QaeEioipFD5EPMWelr1i5jlnjqTHQ+qZ7FNU4yA5+z71lCtcZC985Q20Bdibj2NZrK5QEVrJdQuJhuBo19kkwVH/rJZZxJdYFpHSSeFD5FxOpysoJB939TSk5NxOuIJEzpLa7pAR7wodjowrP6YtBMO/DPGZXoRWqIpXQNnsC2aFuVYyzDn9NDDC1ZsRXKHYrmwoIqLwIaWDI8t1/YbMFNfaFUnHIPFY9jyJY/+svpkzX+JKF8WyeP2z+mXOmSM5j4PKuc4WCYxw/TsgO1T4lwFvX7LsdpZkX9vCS4tciUgJpvAhxY89/axhi/h/eh7S47MvT378n1U2k+Nc10u43KW6LV7/XEch56qeAdnXUjg7VNhC3Mtzu6/bofkUIiIXpfAh5nI6XVcITT3tChBpp7P/ffb9qexQkX2RsPNdSjq/LFbXOhVBFVwTMHPmSYREZ8+ViNKiWCIiBUy/XcVzDMM1wdI9fHHirDM1Tv4zwfLcCZkXudLoBVl9s3siws66OFho9pBGzuqa2cMctuB/roBp9VXPhIiIyRQ+5MKcDldgOO/tTO7eiZSTrtCRlXZlx8q5BHnOLaAs+Jd13edMtswJFcHlXYFDIUJEpFhS+CiNMlP+mQ+RdMzdU2FNPEq7/VvxnvC668yO1FNX1ivhF+oavsiZLxFw1tkaAWXPmZAZ6uq18LZ5+qcUEZEiSuGjJElPzL3aZXJsdrCIdc2VSDnhGgqxp5z35V5AOYCks7dasnsizjrV8+yeiZx/B4RnX7G0AvgGFPzPKiIixZbCR3HhyIKEgxB/yHWdjoQjkHg4+/6IK3RkJOb//bz9XHMizlqYyhEQycY9sTS+pgveoVGuIY6AcE28FBERj9K3SlHisEP8QTi9z3X10dN74NQe17/jD+Rv7Qn/Mq6ltM9e5TK4wlkTMCNd975BeeZMOO12DsXPplGN60HrTIiISAFR+DBD2hk4sRNO7oAT2bdTu13Bw3Bc+HXefhBWOfs6HRUhNOc+Z4ntirrcuYiIFHkKHwUpM9V1GfO4rRC3HU5sc90nx174Nd7+ULY6lK2WfasB4TVc98FR4OVVePWLiIgUAIUPT8nKgGMb4cg6OLYBjm5w9Wxc6GyRkEoQWRsi6mTf184OGBV0CqmIiJRoCh9XKvU0HFwBh1bAwZVwdP35LywWWA4qNITIelCuLpSrD5F1XAtfiYiIlEIKH/mVmeoKGnsXuW7HNgFG7n0CwqFSK4hqCtFNXfchUYVdqYiISJGm8HEx8QdhxxzY+RvsX5r34mQRtaFyW4hpCzFtXHMzNGQiIiJyUQof54rbBptnuEJH3N+5nwupCNU7uW7VrnPNzxAREZHLovABrgucbfke1n8NR9b+s93iBZXbQe2bXLeIWurZEBERuUqlO3yc2AF/vgNbf4SsdNc2L2+o1Q3q3wq1urqWDhcRERGPKZ3h48wBWPQ6bJr2z6mwkXWh2UBofLdrBVAREREpEKUrfCQdhz//C2smgtPu2lb3Frj2SajYQkMqIiIihaD0hI89C2Faf7Cnuh5X7wzXvwiVWphbl4iISClTesJHxebgbYPyDeCGUa6zVURERKTQlZ7w4RcKDy2CsCoaXhERETFR6QkfAGWqml2BiIhIqadLpIqIiEihUvgQERGRQlVg4ePDDz+katWq+Pn50aZNG1atWlVQhxIREZFipEDCx/Tp0xk5ciQvvfQS69ato0mTJnTr1o24uLiCOJyIiIgUIwUSPt555x0efPBB7r33XurXr8/HH39MQEAAX3zxRUEcTkRERIoRj4ePzMxM1q5dS5cuXf45iJcXXbp0Yfny5Z4+nIiIiBQzHj/V9uTJkzgcDsqXL59re/ny5dm+fXue/TMyMsjIyHA/TkxMBMBut2O32z1dnlxETnur3c2h9jeX2t9can9zeaL9L+e1pq/z8dprrzFmzJg82+fOnUtAQIAJFcm8efPMLqFUU/ubS+1vLrW/ua6m/VNTU/O9r8fDR0REBFarlePHj+fafvz4cSpUqJBn/+eff56RI0e6HycmJhITE8ONN95ISEiIp8uTi7Db7cybN4+uXbvi4+NjdjmljtrfXGp/c6n9zeWJ9s8ZucgPj4cPX19fWrRowfz587ntttsAcDqdzJ8/n+HDh+fZ32azYbPZ8mz38fHRB9Akantzqf3NpfY3l9rfXFfT/pfzugIZdhk5ciSDBw+mZcuWtG7dmvfee4+UlBTuvffegjiciIiIFCMFEj7uvvtuTpw4wahRo4iNjaVp06bMmTMnzyRUERERKX0KbMLp8OHDzzvMIiIiIqWb6We7nMswDODyJq6IZ9jtdlJTU0lMTNSYqwnU/uZS+5tL7W8uT7R/zvd2zvf4xRS58JGUlARATEyMyZWIiIjI5UpKSiI0NPSi+1iM/ESUQuR0Ojl69CjBwcFYLBazyylVck5zPnTokE5zNoHa31xqf3Op/c3lifY3DIOkpCSio6Px8rr4AupFrufDy8uLSpUqmV1GqRYSEqL/+U2k9jeX2t9can9zXW37X6rHI0eBXFhORERE5EIUPkRERKRQKXyIm81m46WXXjrvirNS8NT+5lL7m0vtb67Cbv8iN+FURERESjb1fIiIiEihUvgQERGRQqXwISIiIoVK4UNEREQKlcJHKff6669jsVgYMWKEe1t6ejrDhg0jPDycoKAgevfuzfHjx80rsoQ5cuQIAwcOJDw8HH9/fxo1asSaNWvczxuGwahRo4iKisLf358uXbqwa9cuEysuORwOBy+++CLVqlXD39+fGjVq8PLLL+e6FoXa33OWLFlCz549iY6OxmKx8OOPP+Z6Pj9tffr0aQYMGEBISAhhYWHcf//9JCcnF+JPUXxdrP3tdjvPPvssjRo1IjAwkOjoaAYNGsTRo0dzvUdBtb/CRym2evVqPvnkExo3bpxr+5NPPsnPP//MjBkzWLx4MUePHuWOO+4wqcqS5cyZM7Rv3x4fHx9+++03tm7dyttvv02ZMmXc+7z55pu8//77fPzxx6xcuZLAwEC6detGenq6iZWXDG+88Qbjx49n3LhxbNu2jTfeeIM333yTDz74wL2P2t9zUlJSaNKkCR9++OF5n89PWw8YMIC///6befPm8csvv7BkyRIeeuihwvoRirWLtX9qairr1q3jxRdfZN26dcycOZMdO3bQq1evXPsVWPsbUiolJSUZtWrVMubNm2d07NjReOKJJwzDMIz4+HjDx8fHmDFjhnvfbdu2GYCxfPlyk6otOZ599lnj2muvveDzTqfTqFChgvHWW2+5t8XHxxs2m8345ptvCqPEEq1Hjx7Gfffdl2vbHXfcYQwYMMAwDLV/QQKMH374wf04P229detWAzBWr17t3ue3334zLBaLceTIkUKrvSQ4t/3PZ9WqVQZgHDhwwDCMgm1/9XyUUsOGDaNHjx506dIl1/a1a9dit9tzba9bty6VK1dm+fLlhV1miTNr1ixatmxJnz59KFeuHM2aNWPChAnu5/ft20dsbGyu9g8NDaVNmzZqfw+45pprmD9/Pjt37gRg48aNLF26lJtvvhlQ+xem/LT18uXLCQsLo2XLlu59unTpgpeXFytXriz0mku6hIQELBYLYWFhQMG2f5G7sJwUvGnTprFu3TpWr16d57nY2Fh8fX3dH74c5cuXJzY2tpAqLLn27t3L+PHjGTlyJP/6179YvXo1jz/+OL6+vgwePNjdxuXLl8/1OrW/Zzz33HMkJiZSt25drFYrDoeDsWPHMmDAAAC1fyHKT1vHxsZSrly5XM97e3tTtmxZ/ffwsPT0dJ599ln69evnvrBcQba/wkcpc+jQIZ544gnmzZuHn5+f2eWUOk6nk5YtW/Lqq68C0KxZM7Zs2cLHH3/M4MGDTa6u5Pv222+ZMmUKU6dOpUGDBmzYsIERI0YQHR2t9pdSy263c9ddd2EYBuPHjy+UY2rYpZRZu3YtcXFxNG/eHG9vb7y9vVm8eDHvv/8+3t7elC9fnszMTOLj43O97vjx41SoUMGcokuQqKgo6tevn2tbvXr1OHjwIIC7jc89u0jt7xnPPPMMzz33HH379qVRo0bcc889PPnkk7z22muA2r8w5aetK1SoQFxcXK7ns7KyOH36tP57eEhO8Dhw4ADz5s1z93pAwba/wkcpc8MNN7B582Y2bNjgvrVs2ZIBAwa4/+3j48P8+fPdr9mxYwcHDx6kXbt2JlZeMrRv354dO3bk2rZz506qVKkCQLVq1ahQoUKu9k9MTGTlypVqfw9ITU3Fyyv3rz2r1YrT6QTU/oUpP23drl074uPjWbt2rXufBQsW4HQ6adOmTaHXXNLkBI9du3bxxx9/EB4enuv5Am3/q5quKiXC2We7GIZhPPzww0blypWNBQsWGGvWrDHatWtntGvXzrwCS5BVq1YZ3t7extixY41du3YZU6ZMMQICAoyvv/7avc/rr79uhIWFGT/99JOxadMm49ZbbzWqVatmpKWlmVh5yTB48GCjYsWKxi+//GLs27fPmDlzphEREWH83//9n3sftb/nJCUlGevXrzfWr19vAMY777xjrF+/3n02RX7a+qabbjKaNWtmrFy50li6dKlRq1Yto1+/fmb9SMXKxdo/MzPT6NWrl1GpUiVjw4YNxrFjx9y3jIwM93sUVPsrfEie8JGWlmY8+uijRpkyZYyAgADj9ttvN44dO2ZegSXMzz//bDRs2NCw2WxG3bp1jU8//TTX806n03jxxReN8uXLGzabzbjhhhuMHTt2mFRtyZKYmGg88cQTRuXKlQ0/Pz+jevXqxr///e9cv2zV/p6zcOFCA8hzGzx4sGEY+WvrU6dOGf369TOCgoKMkJAQ49577zWSkpJM+GmKn4u1/759+877HGAsXLjQ/R4F1f4WwzhraT8RERGRAqY5HyIiIlKoFD5ERESkUCl8iIiISKFS+BAREZFCpfAhIiIihUrhQ0RERAqVwoeIiIgUKoUPERERKVQKHyLiEcuXL8dqtdKjRw+zSxGRIk4rnIqIRzzwwAMEBQXx+eefs2PHDqKjo80uSUSKKPV8iMhVS05OZvr06TzyyCP06NGDSZMm5Xp+1qxZ1KpVCz8/Pzp37syXX36JxWIhPj7evc/SpUvp0KED/v7+xMTE8Pjjj5OSklK4P4iIFAqFDxG5at9++y1169alTp06DBw4kC+++IKcTtV9+/Zx5513ctttt7Fx40aGDh3Kv//971yv37NnDzfddBO9e/dm06ZNTJ8+naVLlzJ8+HAzfhwRKWAadhGRq9a+fXvuuusunnjiCbKysoiKimLGjBl06tSJ5557jl9//ZXNmze793/hhRcYO3YsZ86cISwsjAceeACr1conn3zi3mfp0qV07NiRlJQU/Pz8zPixRKSAqOdDRK7Kjh07WLVqFf369QPA29ubu+++m88//9z9fKtWrXK9pnXr1rkeb9y4kUmTJhEUFOS+devWDafTyb59+wrnBxGRQuNtdgEiUrx9/vnnZGVl5ZpgahgGNpuNcePG5es9kpOTGTp0KI8//nie5ypXruyxWkWkaFD4EJErlpWVxeTJk3n77be58cYbcz1322238c0331CnTh1mz56d67nVq1fnety8eXO2bt1KzZo1C7xmETGf5nyIyBX78ccfufvuu4mLiyM0NDTXc88++ywLFizg22+/pU6dOjz55JPcf//9bNiwgaeeeorDhw8THx9PaGgomzZtom3bttx333088MADBAYGsnXrVubNm5fv3hMRKT4050NErtjnn39Oly5d8gQPgN69e7NmzRqSkpL47rvvmDlzJo0bN2b8+PHus11sNhsAjRs3ZvHixezcuZMOHTrQrFkzRo0apbVCREoo9XyISKEbO3YsH3/8MYcOHTK7FBExgeZ8iEiB++ijj2jVqhXh4eEsW7aMt956S2t4iJRiCh8iUuB27drFK6+8wunTp6lcuTJPPfUUzz//vNlliYhJNOwiIiIihUoTTkVERKRQKXyIiIhIoVL4EBERkUKl8CEiIiKFSuFDRERECpXCh4iIiBQqhQ8REREpVAofIiIiUqgUPkRERKRQ/T85rnR3cSGBpwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "(25.0, 95.0)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -158,20 +196,32 @@
     "plt.figure()\n",
     "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n",
     "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n",
+    "plt.plot(moments_values[0], moments_values[1], label=\"scf\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Age\")\n",
     "plt.title(\"TRP Variable Medians\")\n",
-    "plt.grid()"
+    "plt.grid()\n",
+    "plt.xlim([25, 95])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "(25.0, 95.0)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -195,8 +245,16 @@
     "plt.ylabel(\"Fraction of Savings\")\n",
     "plt.title(\"TRP Portfolio Share Median Conditional on Survival\")\n",
     "plt.ylim(0, 1)\n",
-    "plt.grid()"
+    "plt.grid()\n",
+    "plt.xlim(25, 95)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/content/tables/TRP2/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/TRP2/WealthPortfolioSub(Stock)Market_estimate_results.csv
index d5039fd..bfc3182 100644
--- a/content/tables/TRP2/WealthPortfolioSub(Stock)Market_estimate_results.csv
+++ b/content/tables/TRP2/WealthPortfolioSub(Stock)Market_estimate_results.csv
@@ -1,6 +1,6 @@
 CRRA,2.0
 WealthShare,0.5743521335960693
-time_to_estimate,147.2053246498108
+time_to_estimate,162.47532606124878
 params,"{'CRRA': 2.0, 'WealthShare': 0.5743521335960693}"
 criterion,0.4458024296501866
 start_criterion,0.6579094364692223
@@ -13,7 +13,7 @@ success,
 n_criterion_evaluations,
 n_derivative_evaluations,
 n_iterations,
-history,"{'params': [{'CRRA': 2.0, 'WealthShare': 0.5743521335960693}, {'CRRA': 2.0057622172772716, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.5341055300190553}, {'CRRA': 2.0, 'WealthShare': 0.6604525788109887}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.6981892091423285}, {'CRRA': 2.1710652519999476, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.42455490995539763}, {'CRRA': 2.0003794395506773, 'WealthShare': 0.7}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.610473783157256}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.6929097307178849}, {'CRRA': 2.0002737674430797, 'WealthShare': 0.7}, {'CRRA': 2.0654817477777443, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.0824844264080964, 'WealthShare': 0.7}, {'CRRA': 2.065402120932167, 'WealthShare': 0.6656239501989735}, {'CRRA': 2.0548120947925477, 'WealthShare': 0.6539332911160185}, {'CRRA': 2.044311346272638, 'WealthShare': 0.6186634798687072}, {'CRRA': 2.022155673136319, 'WealthShare': 0.5521964604597503}, {'CRRA': 2.0, 'WealthShare': 0.5926914145852586}, {'CRRA': 2.0110778365681594, 'WealthShare': 0.5854299701642288}, {'CRRA': 2.0, 'WealthShare': 0.575568939600389}, {'CRRA': 2.0, 'WealthShare': 0.5746603409618074}, {'CRRA': 2.0004091274956592, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0005952318714106, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0027694591420397, 'WealthShare': 0.5766201784551704}, {'CRRA': 2.00127207137848, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0027562097811846, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0009426369415775, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0027694591420397, 'WealthShare': 0.5727407480643028}, {'CRRA': 2.001939781666317, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0027694591420397, 'WealthShare': 0.5763886645337926}, {'CRRA': 2.0015015208404607, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0, 'WealthShare': 0.5715826744848365}, {'CRRA': 2.0, 'WealthShare': 0.5771215927337404}, {'CRRA': 2.0, 'WealthShare': 0.5744322058607663}, {'CRRA': 2.0, 'WealthShare': 0.5729674040250493}, {'CRRA': 2.0, 'WealthShare': 0.5743878402957667}, {'CRRA': 2.00069236478551, 'WealthShare': 0.5736597688105594}, {'CRRA': 2.0, 'WealthShare': 0.5743787046937943}, {'CRRA': 2.0, 'WealthShare': 0.5742783967162427}, {'CRRA': 2.0001730911963773, 'WealthShare': 0.5743521335960693}, {'CRRA': 2.0, 'WealthShare': 0.5743036661217499}, {'CRRA': 2.0, 'WealthShare': 0.574308860796975}, {'CRRA': 2.000021636399547, 'WealthShare': 0.5743520965868928}, {'CRRA': 2.0, 'WealthShare': 0.5743413153962957}, {'CRRA': 2.0, 'WealthShare': 0.5743467244961825}, {'CRRA': 2.0000027045499436, 'WealthShare': 0.5743521343530072}, {'CRRA': 2.0, 'WealthShare': 0.574353485871041}, {'CRRA': 2.0, 'WealthShare': 0.5743512473691439}, {'CRRA': 2.0, 'WealthShare': 0.5743512473691439}, {'CRRA': 2.0, 'WealthShare': 0.5743512473691439}, {'CRRA': 2.0, 'WealthShare': 0.5743512473691439}, {'CRRA': 2.0, 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101.28027972998098, 102.24289976098225, 103.19377581300796, 104.1606048209942, 105.13335475799977], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106]}"
 convergence_report,
 multistart_info,"{'start_parameters': [{'CRRA': 2.0, 'WealthShare': 0.5743521335960693}, {'CRRA': 2.329504871165134, 'WealthShare': 0.5983394633010707}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated.
 
@@ -14281,7 +14281,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([2.        ,
         74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,
         87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99,
        100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
-       113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 129 entries., 'history': {'params': [{'CRRA': 2.0, 'WealthShare': 0.5743521335960693}, {'CRRA': 2.0057622172772716, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.5341055300190553}, {'CRRA': 2.0, 'WealthShare': 0.6604525788109887}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.6981892091423285}, {'CRRA': 2.1710652519999476, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.42455490995539763}, {'CRRA': 2.0003794395506773, 'WealthShare': 0.7}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.610473783157256}, {'CRRA': 2.1772453850905515, 'WealthShare': 0.6929097307178849}, {'CRRA': 2.0002737674430797, 'WealthShare': 0.7}, {'CRRA': 2.0654817477777443, 'WealthShare': 0.39710674850551764}, {'CRRA': 2.0824844264080964, 'WealthShare': 0.7}, {'CRRA': 2.065402120932167, 'WealthShare': 0.6656239501989735}, {'CRRA': 2.0548120947925477, 'WealthShare': 0.6539332911160185}, {'CRRA': 2.044311346272638, 'WealthShare': 0.6186634798687072}, {'CRRA': 2.022155673136319, 'WealthShare': 0.5521964604597503}, {'CRRA': 2.0, 'WealthShare': 0.5926914145852586}, {'CRRA': 2.0110778365681594, 'WealthShare': 0.5854299701642288}, {'CRRA': 2.0, 'WealthShare': 0.575568939600389}, {'CRRA': 2.0, 'WealthShare': 0.5746603409618074}, {'CRRA': 2.0004091274956592, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0005952318714106, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0027694591420397, 'WealthShare': 0.5766201784551704}, {'CRRA': 2.00127207137848, 'WealthShare': 0.5771215927381091}, {'CRRA': 2.0027562097811846, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0009426369415775, 'WealthShare': 0.5715826744540294}, {'CRRA': 2.0027694591420397, 'WealthShare': 0.5727407480643028}, {'CRRA': 2.001939781666317, 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90.72116064300644, 91.6921483890037, 92.6905002410058, 93.62124952999875, 94.60418957300135, 95.52817838598276, 96.44443713600049, 97.4040424109844, 98.39551835498423, 99.40065861400217, 100.33405920598307, 101.28027972998098, 102.24289976098225, 103.19377581300796, 104.1606048209942, 105.13335475799977], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106]}, 'multistart_info': {...}}, {'solution_x': array([2.        , 0.57432735]), 'solution_criterion': 0.4458028691730516, 'states': [State(trustregion=Region(center=array([2.32950487, 0.59833946]), radius=0.2329504871165134, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=1.1376532643504116, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
        [0., 0.]]), scale=0.2329504871165134, shift=array([2.32950487, 0.59833946])), vector_model=VectorModel(intercepts=array([ 0.10967108,  0.18679163,  0.17047419,  0.15225095,  0.10738307,
         0.04952748, -0.01386253, -0.20896003, -0.29786164, -0.20376537,
        -0.43093765, -0.31057081, -0.45374443, -0.33744469, -0.28762446,
@@ -16075,7 +16075,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([2.        ,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.2329504871165134, shift=array([2.32950487, 0.59833946])), candidate_index=45, candidate_x=array([2.        , 0.57432735]), index=45, x=array([2.        , 0.57432735]), fval=0.4458028691730516, rho=-inf, accepted=True, new_indices=array([44]), old_indices_used=array([31, 33, 35, 37, 39, 41, 42, 43]), old_indices_discarded=array([36, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Relative criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 46 entries., 'history': {'params': [{'CRRA': 2.329504871165134, 'WealthShare': 0.5983394633010707}, {'CRRA': 2.1560603645411036, 'WealthShare': 0.3918924693210807}, {'CRRA': 2.535951865145124, 'WealthShare': 0.5776992626307629}, {'CRRA': 2.123057877185144, 'WealthShare': 0.6159726415308767}, {'CRRA': 2.5258860213311127, 'WealthShare': 0.7}, {'CRRA': 2.535951865145124, 'WealthShare': 0.47147644457636784}, {'CRRA': 2.535951865145124, 'WealthShare': 0.391892699232989}, {'CRRA': 2.17162970475579, 'WealthShare': 0.7}, {'CRRA': 2.535951865145124, 'WealthShare': 0.6573485816461966}, {'CRRA': 2.4936261817459973, 'WealthShare': 0.7}, {'CRRA': 2.123057877185144, 'WealthShare': 0.6966217727463357}, {'CRRA': 2.3069649918183193, 'WealthShare': 0.3918924693210807}, {'CRRA': 2.3409251915198666, 'WealthShare': 0.7}, {'CRRA': 2.303307612635025, 'WealthShare': 0.6666936063216878}, {'CRRA': 2.226281374175139, 'WealthShare': 0.6635630384112645}, {'CRRA': 2.3163300498333323, 'WealthShare': 0.6550672754690356}, {'CRRA': 2.316100954394881, 'WealthShare': 0.5724891216623547}, {'CRRA': 2.2963203776642804, 'WealthShare': 0.6272645666600325}, {'CRRA': 2.2875090746838853, 'WealthShare': 0.5668137799199323}, {'CRRA': 2.2287686688213557, 'WealthShare': 0.567746839255583}, {'CRRA': 2.1120383256255533, 'WealthShare': 0.571021097939005}, {'CRRA': 2.022375948441008, 'WealthShare': 0.6963462608589613}, {'CRRA': 2.0734401938441027, 'WealthShare': 0.6738536570050909}, {'CRRA': 2.053473927506567, 'WealthShare': 0.5760001038650844}, {'CRRA': 2.0, 'WealthShare': 0.5768000417782413}, {'CRRA': 2.103223496989995, 'WealthShare': 0.47357654478824635}, {'CRRA': 2.0, 'WealthShare': 0.6147099038059903}, {'CRRA': 2.0, 'WealthShare': 0.6155300639473469}, {'CRRA': 2.0, 'WealthShare': 0.5675163087361096}, {'CRRA': 2.0, 'WealthShare': 0.578854759606989}, {'CRRA': 2.006451468561875, 'WealthShare': 0.5768000417782413}, {'CRRA': 2.0, 'WealthShare': 0.5747451911662258}, {'CRRA': 2.0, 'WealthShare': 0.5811966597281005}, {'CRRA': 2.0, 'WealthShare': 0.5749764450131662}, {'CRRA': 2.0, 'WealthShare': 0.571519456905979}, {'CRRA': 2.0, 'WealthShare': 0.5748160481236353}, {'CRRA': 2.0000000000166507, 'WealthShare': 0.5763580583066945}, {'CRRA': 2.0, 'WealthShare': 0.5747857302362299}, {'CRRA': 2.0008064335702342, 'WealthShare': 0.5739387575959914}, {'CRRA': 2.0, 'WealthShare': 0.5743648099814643}, {'CRRA': 2.0008064335702342, 'WealthShare': 0.5751712435516987}, {'CRRA': 2.0, 'WealthShare': 0.5743273542830748}, {'CRRA': 2.0, 'WealthShare': 0.5735209207128404}, {'CRRA': 2.0, 'WealthShare': 0.5743111629226524}, {'CRRA': 2.000000000000104, 'WealthShare': 0.5739241374979577}, {'CRRA': 2.0, 'WealthShare': 0.5743273542830748}], 'criterion': [1.1376532643504116, 12351.650840786147, 1.2538698331529483, 1.124920310008188, 2.333770229634127, 384.51716814207737, 12320.484820119167, 2.04515269756047, 2.012022418023596, 2.30171353021148, 1.998205997046724, 12331.438239910885, 2.161514646979643, 1.8689911306637086, 1.7774416863317588, 1.768097533824131, 0.9123743204734925, 1.454268971099252, 0.8945461780306248, 0.8013237801506291, 0.609662713649639, 1.976240150682986, 1.7927073946829333, 0.5228661467683627, 0.45188348926193916, 8956.83916910396, 1.011722162051279, 1.0254898136708404, 0.509785858605815, 0.46278444819130427, 0.4604703901447806, 0.44602473036636775, 0.4803294848706739, 0.4463036516494008, 0.45521816538065824, 0.4460895887921832, 0.4501869856209165, 0.44607019694322236, 0.44700027330229375, 0.4458067647350837, 0.4477916082303869, 0.44580286917305156, 0.4466053238372076, 0.4458050369288744, 0.44596802300107613, 0.44580286917305156], 'runtime': [0.0, 0.9999205150234047, 1.042348563001724, 1.2060594680078793, 1.2512016250111628, 1.2940277350135148, 1.3404012850078288, 1.382541952014435, 1.4255456420069095, 1.4664261150173843, 1.509574131021509, 1.5698904170130845, 1.6181903959950432, 2.72489052900346, 3.610019290004857, 4.489838520996273, 5.36605305300327, 6.26870435901219, 7.152188330015633, 8.034251216013217, 8.921899683016818, 9.809157022013096, 10.689685078017646, 11.569655978004448, 12.463332080020336, 13.350026212021476, 14.231462486000964, 15.117791127006058, 15.998188733996358, 16.885555474000284, 17.77190736401826, 18.652493191009853, 19.549426873010816, 20.436544855008833, 21.321539232012583, 22.20621671099798, 23.099391905008815, 23.97293193000951, 24.857777486002306, 25.740992621023906, 26.62867444602307, 27.51660074701067, 28.401994186016964, 29.29081090600812, 30.173362355999416, 31.049803502013674], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]}}], 'exploration_sample': array([[ 2.        ,  0.57435213],
+        [0., 0.]]]), scale=0.2329504871165134, shift=array([2.32950487, 0.59833946])), candidate_index=45, candidate_x=array([2.        , 0.57432735]), index=45, x=array([2.        , 0.57432735]), fval=0.4458028691730516, rho=-inf, accepted=True, new_indices=array([44]), old_indices_used=array([31, 33, 35, 37, 39, 41, 42, 43]), old_indices_discarded=array([36, 38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Relative criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 46 entries., 'history': {'params': [{'CRRA': 2.329504871165134, 'WealthShare': 0.5983394633010707}, {'CRRA': 2.1560603645411036, 'WealthShare': 0.3918924693210807}, {'CRRA': 2.535951865145124, 'WealthShare': 0.5776992626307629}, {'CRRA': 2.123057877185144, 'WealthShare': 0.6159726415308767}, {'CRRA': 2.5258860213311127, 'WealthShare': 0.7}, {'CRRA': 2.535951865145124, 'WealthShare': 0.47147644457636784}, {'CRRA': 2.535951865145124, 'WealthShare': 0.391892699232989}, {'CRRA': 2.17162970475579, 'WealthShare': 0.7}, {'CRRA': 2.535951865145124, 'WealthShare': 0.6573485816461966}, {'CRRA': 2.4936261817459973, 'WealthShare': 0.7}, {'CRRA': 2.123057877185144, 'WealthShare': 0.6966217727463357}, {'CRRA': 2.3069649918183193, 'WealthShare': 0.3918924693210807}, {'CRRA': 2.3409251915198666, 'WealthShare': 0.7}, {'CRRA': 2.303307612635025, 'WealthShare': 0.6666936063216878}, {'CRRA': 2.226281374175139, 'WealthShare': 0.6635630384112645}, {'CRRA': 2.3163300498333323, 'WealthShare': 0.6550672754690356}, {'CRRA': 2.316100954394881, 'WealthShare': 0.5724891216623547}, {'CRRA': 2.2963203776642804, 'WealthShare': 0.6272645666600325}, {'CRRA': 2.2875090746838853, 'WealthShare': 0.5668137799199323}, {'CRRA': 2.2287686688213557, 'WealthShare': 0.567746839255583}, {'CRRA': 2.1120383256255533, 'WealthShare': 0.571021097939005}, {'CRRA': 2.022375948441008, 'WealthShare': 0.6963462608589613}, {'CRRA': 2.0734401938441027, 'WealthShare': 0.6738536570050909}, {'CRRA': 2.053473927506567, 'WealthShare': 0.5760001038650844}, {'CRRA': 2.0, 'WealthShare': 0.5768000417782413}, {'CRRA': 2.103223496989995, 'WealthShare': 0.47357654478824635}, {'CRRA': 2.0, 'WealthShare': 0.6147099038059903}, {'CRRA': 2.0, 'WealthShare': 0.6155300639473469}, {'CRRA': 2.0, 'WealthShare': 0.5675163087361096}, {'CRRA': 2.0, 'WealthShare': 0.578854759606989}, {'CRRA': 2.006451468561875, 'WealthShare': 0.5768000417782413}, {'CRRA': 2.0, 'WealthShare': 0.5747451911662258}, {'CRRA': 2.0, 'WealthShare': 0.5811966597281005}, {'CRRA': 2.0, 'WealthShare': 0.5749764450131662}, {'CRRA': 2.0, 'WealthShare': 0.571519456905979}, {'CRRA': 2.0, 'WealthShare': 0.5748160481236353}, {'CRRA': 2.0000000000166507, 'WealthShare': 0.5763580583066945}, {'CRRA': 2.0, 'WealthShare': 0.5747857302362299}, {'CRRA': 2.0008064335702342, 'WealthShare': 0.5739387575959914}, {'CRRA': 2.0, 'WealthShare': 0.5743648099814643}, {'CRRA': 2.0008064335702342, 'WealthShare': 0.5751712435516987}, {'CRRA': 2.0, 'WealthShare': 0.5743273542830748}, {'CRRA': 2.0, 'WealthShare': 0.5735209207128404}, {'CRRA': 2.0, 'WealthShare': 0.5743111629226524}, {'CRRA': 2.000000000000104, 'WealthShare': 0.5739241374979577}, {'CRRA': 2.0, 'WealthShare': 0.5743273542830748}], 'criterion': [1.1376532643504116, 12351.650840786147, 1.2538698331529483, 1.124920310008188, 2.333770229634127, 384.51716814207737, 12320.484820119167, 2.04515269756047, 2.012022418023596, 2.30171353021148, 1.998205997046724, 12331.438239910885, 2.161514646979643, 1.8689911306637086, 1.7774416863317588, 1.768097533824131, 0.9123743204734925, 1.454268971099252, 0.8945461780306248, 0.8013237801506291, 0.609662713649639, 1.976240150682986, 1.7927073946829333, 0.5228661467683627, 0.45188348926193916, 8956.83916910396, 1.011722162051279, 1.0254898136708404, 0.509785858605815, 0.46278444819130427, 0.4604703901447806, 0.44602473036636775, 0.4803294848706739, 0.4463036516494008, 0.45521816538065824, 0.4460895887921832, 0.4501869856209165, 0.44607019694322236, 0.44700027330229375, 0.4458067647350837, 0.4477916082303869, 0.44580286917305156, 0.4466053238372076, 0.4458050369288744, 0.44596802300107613, 0.44580286917305156], 'runtime': [0.0, 1.0910818520060275, 1.1341954840172548, 1.174569241993595, 1.2188461210171226, 1.2618069940072019, 1.310763092013076, 1.3714275379898027, 1.4230002140102442, 1.4640654250106309, 1.5259943609999027, 1.5890706040081568, 1.6390339419886004, 2.80703255100525, 3.753295647999039, 4.735832233011024, 5.716721776989289, 6.7134101029951125, 7.820864937995793, 8.799175521009602, 9.79561957999249, 10.829584349005017, 11.837481166003272, 12.855290658015292, 13.864373106014682, 14.868337448016973, 15.902677142992616, 16.923706478992244, 17.918750646000262, 18.945733688015025, 19.922439482004847, 20.90384982599062, 21.913415457005613, 22.891732410003897, 23.886497592990054, 24.891776389995357, 25.895821689016884, 26.914386837015627, 27.90278085099999, 28.877567287010606, 29.869356768991565, 30.878341228002682, 31.89229446600075, 32.82814773899736, 33.844578666990856, 34.95237154699862], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]}}], 'exploration_sample': array([[ 2.        ,  0.57435213],
        [ 3.125     ,  0.65625   ],
        [ 6.5       ,  0.525     ],
        [14.375     ,  0.56875   ],
diff --git a/content/tables/TRP2/WealthPortfolio_estimate_results.csv b/content/tables/TRP2/WealthPortfolio_estimate_results.csv
index 24ce0a5..39b00f0 100644
--- a/content/tables/TRP2/WealthPortfolio_estimate_results.csv
+++ b/content/tables/TRP2/WealthPortfolio_estimate_results.csv
@@ -1,9518 +1,31 @@
-CRRA,3.385996288692849
-WealthShare,0.5363280846095942
-time_to_estimate,313.1915364265442
-params,"{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}"
-criterion,0.3474700534738468
-start_criterion,0.4278966614508608
-start_params,"{'CRRA': 3.4330422683751007, 'WealthShare': 0.5365121417593192}"
+CRRA,3.394330573661952
+WealthShare,0.537543812530427
+time_to_estimate,274.5125665664673
+params,"{'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}"
+criterion,0.3476591866783385
+start_criterion,0.4310570192429367
+start_params,"{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}"
 algorithm,multistart_tranquilo_ls
 direction,minimize
 n_free,2
-message,
+message,Absolute params change smaller than tolerance.
 success,
 n_criterion_evaluations,
 n_derivative_evaluations,
 n_iterations,
-history,"{'params': [{'CRRA': 3.342803558834963, 'WealthShare': 0.571579946415527}, {'CRRA': 3.056793450976187, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.639051810868848, 'WealthShare': 0.47451590578279923}, {'CRRA': 3.0465553068010784, 'WealthShare': 0.5659057617222264}, {'CRRA': 3.499963089386706, 'WealthShare': 0.7}, {'CRRA': 3.639051810868848, 'WealthShare': 0.30018655956366036}, {'CRRA': 3.389650999018406, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.0465553068010784, 'WealthShare': 0.6955569808291606}, {'CRRA': 3.639051810868848, 'WealthShare': 0.6269704836311257}, {'CRRA': 3.458253701473133, 'WealthShare': 0.7}, {'CRRA': 3.065655877321031, 'WealthShare': 0.7}, {'CRRA': 3.216095887232247, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.2804577967265995, 'WealthShare': 0.7}, {'CRRA': 3.341655905914133, 'WealthShare': 0.6457318631541257}, {'CRRA': 3.4909276848519055, 'WealthShare': 0.65805184835626}, {'CRRA': 3.3918447454600544, 'WealthShare': 0.6431963336353185}, {'CRRA': 3.3358005784674627, 'WealthShare': 0.5303859151898463}, {'CRRA': 3.353195731808456, 'WealthShare': 0.6121255531315644}, {'CRRA': 3.37709830857061, 'WealthShare': 0.5237599163349254}, {'CRRA': 3.354816979554147, 'WealthShare': 0.5402514372242311}, {'CRRA': 3.3964310109161833, 'WealthShare': 0.5359725202217566}, {'CRRA': 3.477984745267262, 'WealthShare': 0.555327475149672}, {'CRRA': 3.3547576687619083, 'WealthShare': 0.5390257965509951}, {'CRRA': 3.3756148229595264, 'WealthShare': 0.53783266343029}, {'CRRA': 3.3638607590913794, 'WealthShare': 0.5381377261906201}, {'CRRA': 3.396367948582899, 'WealthShare': 0.540549077411684}, {'CRRA': 3.3859973023212477, 'WealthShare': 0.5363276703954324}, {'CRRA': 3.4068901490316383, 'WealthShare': 0.535660453630372}, {'CRRA': 3.3963876242032662, 'WealthShare': 0.5351576026260113}, {'CRRA': 3.380763751111572, 'WealthShare': 0.5353809598457648}, {'CRRA': 3.3833626506414505, 'WealthShare': 0.5356032815238324}, {'CRRA': 3.386269719812858, 'WealthShare': 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-convergence_report,"{'one_step': {'relative_criterion_change': 0.00010929897962966284, 'relative_params_change': 0.013891995372670137, 'absolute_criterion_change': 3.797812229655584e-05, 'absolute_params_change': 0.04702480409066778}, 'five_steps': {'relative_criterion_change': 0.00010929897962966284, 'relative_params_change': 0.013891995372670137, 'absolute_criterion_change': 3.797812229655584e-05, 'absolute_params_change': 0.04702480409066778}}"
-multistart_info,"{'start_parameters': [{'CRRA': 3.4330422683751007, 'WealthShare': 0.5365121417593192}, {'CRRA': 3.342803558834963, 'WealthShare': 0.571579946415527}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated.], 'exploration_sample': [{'CRRA': 3.4330422683751007, 'WealthShare': 0.5365121417593192}, {'CRRA': 3.125, 'WealthShare': 0.65625}, {'CRRA': 6.5, 'WealthShare': 0.5249999999999999}, {'CRRA': 8.1875, 'WealthShare': 0.5031249999999999}, {'CRRA': 14.375, 'WealthShare': 0.56875}, {'CRRA': 12.6875, 'WealthShare': 0.678125}, {'CRRA': 16.625, 'WealthShare': 0.48124999999999996}, {'CRRA': 17.75, 'WealthShare': 0.6124999999999999}, {'CRRA': 4.25, 'WealthShare': 0.4375}, {'CRRA': 9.875, 'WealthShare': 0.39375}, {'CRRA': 11.0, 'WealthShare': 0.35}, {'CRRA': 12.125, 'WealthShare': 0.30624999999999997}, {'CRRA': 3.6875, 'WealthShare': 0.328125}, {'CRRA': 8.75, 'WealthShare': 0.26249999999999996}], 'exploration_results': array([3.62115019e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
+history,"{'params': [{'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}, {'CRRA': 3.016256765973506, 'WealthShare': 0.29061885915302493}, {'CRRA': 3.602860785674221, 'WealthShare': 0.5097704838031792}, {'CRRA': 3.016256765973506, 'WealthShare': 0.5760370098839758}, {'CRRA': 3.5760478459618126, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.2878392037263486}, {'CRRA': 3.4478588052515033, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.0207215987193226, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.6327324745145311}, {'CRRA': 3.5030790241126377, 'WealthShare': 0.7}, {'CRRA': 3.016256765973506, 'WealthShare': 0.6807913272054849}, {'CRRA': 3.234215209095579, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.292914640909409, 'WealthShare': 0.7}, {'CRRA': 3.303133696480183, 'WealthShare': 0.6468852446280728}, {'CRRA': 3.456209780749042, 'WealthShare': 0.6330260460920982}, {'CRRA': 3.393118516188777, 'WealthShare': 0.5377100904840499}, {'CRRA': 3.5397695211139557, 'WealthShare': 0.5509522833458697}, {'CRRA': 3.4758674679770443, 'WealthShare': 0.5400001711234805}, {'CRRA': 3.434064260747328, 'WealthShare': 0.5243866818724292}, {'CRRA': 3.4137951701197853, 'WealthShare': 0.5371316890165021}, {'CRRA': 3.3827347013962545, 'WealthShare': 0.5356230431833575}, {'CRRA': 3.3886868172194666, 'WealthShare': 0.5404246379718759}, {'CRRA': 3.3929877161471165, 'WealthShare': 0.5351278082679701}, {'CRRA': 3.394411039178595, 'WealthShare': 0.5376829528332149}, {'CRRA': 3.3950422594077354, 'WealthShare': 0.5375349901544691}, {'CRRA': 3.394403048866773, 'WealthShare': 0.5380060531468045}, {'CRRA': 3.394330571104913, 'WealthShare': 0.5375428125336963}, {'CRRA': 3.3942641227083596, 'WealthShare': 0.5372265179426579}, {'CRRA': 3.394173480699256, 'WealthShare': 0.5375027375064382}, {'CRRA': 3.394410295274285, 'WealthShare': 0.537555952565116}, {'CRRA': 3.394368932528548, 'WealthShare': 0.5375301416190236}, {'CRRA': 3.394326995715963, 'WealthShare': 0.5375626935376785}, {'CRRA': 3.3943246528733915, 'WealthShare': 0.5375346281603528}, {'CRRA': 3.3943356046345925, 'WealthShare': 0.5375424051802747}, {'CRRA': 3.394329304009661, 'WealthShare': 0.5375449965801891}, {'CRRA': 3.3943299520790617, 'WealthShare': 0.5375417122135792}, {'CRRA': 3.3943315684886763, 'WealthShare': 0.5375428848222087}, {'CRRA': 3.3943296151296067, 'WealthShare': 0.5375425190865739}, {'CRRA': 3.394329649205157, 'WealthShare': 0.5375432004343061}, {'CRRA': 3.3943315656125383, 'WealthShare': 0.537542917197843}, {'CRRA': 3.3943295696848708, 'WealthShare': 0.5375427637114514}, {'CRRA': 3.3943305792741665, 'WealthShare': 0.5375438125003273}, {'CRRA': 3.394330680756775, 'WealthShare': 0.5375438065037508}, {'CRRA': 3.394331046758134, 'WealthShare': 0.5375419308777375}, {'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}], 'criterion': [0.6607127140516494, nan, 0.7133179969678111, 0.9122250510532794, 1.738814245006418, 50904625.67032898, 116246.71705175268, 2.371483432043779, 1.21707149810191, 1.7742326776217476, 2.1844788091340064, nan, 1.9431589702823127, 1.4900363321437669, 1.2619256393554017, 0.35160518753317954, 0.42838937661127785, 0.36008666150771423, 0.4076867472422282, 0.35498950666354673, 0.3567456338142511, 0.37666426015405113, 0.354837869983808, 0.35106354160290787, 0.36144004176498795, 0.363428759218245, 0.3478920125068537, 0.36129474761431013, 0.36389252870011624, 0.3506283566979166, 0.35511565098469156, 0.35306860499987824, 0.3541434194955959, 0.3617849686943767, 0.3540838274551238, 0.35370468558579427, 0.3585447569605501, 0.35897473002769464, 0.3580444096251574, 0.35816686951261745, 0.3603114273224131, 0.3557617475147986, 0.35761591726779984, 0.3641098629626268, 0.3476591866783385], 'runtime': [0.0, 1.0335186279844493, 1.075547086977167, 1.1165464899968356, 1.158358821994625, 1.2079391409934033, 1.2510499019990675, 1.304747892980231, 1.3741786059981678, 1.4210400559823029, 1.4693867799942382, 1.5261096809990704, 1.604769954981748, 2.7414454839890823, 3.706038322998211, 4.649230179988081, 5.615710240992485, 6.626046998979291, 7.616200939984992, 8.57367010199232, 9.614224569988437, 10.586447503999807, 11.524956123990705, 12.481786197982728, 13.458031254005618, 14.387363051995635, 15.386936976981815, 16.391233448986895, 17.398493871995015, 18.405060699005844, 19.37944886999321, 20.31278933599242, 21.267557780985953, 22.213215073978063, 23.17634274897864, 24.143351596983848, 25.07568769500358, 26.016631281003356, 26.956457163003506, 27.89484377898043, 28.803292337979656, 29.715133089979645, 30.6246158569993, 31.623351027985336, 32.615992790990276], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}"
+convergence_report,"{'one_step': {'relative_criterion_change': 0.0063703811355266165, 'relative_params_change': 0.0033118460013637236, 'absolute_criterion_change': 0.002214721524408214, 'absolute_params_change': 0.008410565685333831}, 'five_steps': {'relative_criterion_change': 0.0063703811355266165, 'relative_params_change': 0.0033118460013637236, 'absolute_criterion_change': 0.002214721524408214, 'absolute_params_change': 0.008410565685333831}}"
+multistart_info,"{'start_parameters': [{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated.
+
+The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.125, 'WealthShare': 0.65625}, {'CRRA': 6.5, 'WealthShare': 0.5249999999999999}, {'CRRA': 8.1875, 'WealthShare': 0.5031249999999999}, {'CRRA': 14.375, 'WealthShare': 0.56875}, {'CRRA': 12.6875, 'WealthShare': 0.678125}, {'CRRA': 16.625, 'WealthShare': 0.48124999999999996}, {'CRRA': 17.75, 'WealthShare': 0.6124999999999999}, {'CRRA': 4.25, 'WealthShare': 0.4375}, {'CRRA': 9.875, 'WealthShare': 0.39375}, {'CRRA': 11.0, 'WealthShare': 0.35}, {'CRRA': 12.125, 'WealthShare': 0.30624999999999997}, {'CRRA': 3.6875, 'WealthShare': 0.328125}, {'CRRA': 8.75, 'WealthShare': 0.26249999999999996}], 'exploration_results': array([3.64521041e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
        4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
        3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
        2.18671524e+03, 2.20657404e+03])}"
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-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=42, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.1613435859183303, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
-       [0., 0.],
-       [0., 0.],
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-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.]]), square_terms=array([[[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
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-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=43, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.973724940098803, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
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-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.]]), square_terms=array([[[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
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-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
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-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=44, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.294460801782931, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
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-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.]]), square_terms=array([[[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=45, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.10609740718266, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
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-       [0., 0.],
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-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.],
-       [0., 0.]]), square_terms=array([[[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
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-
-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
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-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]],
-
-       [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=46, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.472944030316748, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+algorithm_output,"{'states': [State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.33095587758238637, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.6607127140516494, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
+       [0., 0.]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9577,11 +90,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=47, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.4984741260479146, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=0, candidate_x=array([3.30955878, 0.57146613]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.33095587758238637, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=469100.56003831374, linear_terms=array([ 1029983.98164179, -1223830.90282946]), square_terms=array([[ 1132738.22199273, -1343499.04166053],
+       [-1343499.04166053,  1596421.69293623]]), scale=array([0.29330201, 0.21091794]), shift=array([3.30955878, 0.48908206])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9646,11 +159,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=48, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.5307005658244153, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=13, candidate_x=array([3.3031337 , 0.64688524]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=-7.348577092374725e-06, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.16547793879119319, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  4,  8,  9, 11, 12, 13]), model=ScalarModel(intercept=54.73615878023596, linear_terms=array([  -1.98581212, -208.37733252]), square_terms=array([[8.02353181e-02, 3.56619407e+00],
+       [3.56619407e+00, 3.99052236e+02]]), scale=array([0.146651  , 0.13759244]), shift=array([3.30955878, 0.56240756])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9715,11 +228,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=49, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.910540070145298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=14, candidate_x=array([3.45620978, 0.63302605]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=-0.01443461348866765, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  4,  8,  9, 11, 12, 13]), old_indices_discarded=array([ 1,  5,  6,  7, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.08273896939559659, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  4,  8,  9, 12, 13, 14]), model=ScalarModel(intercept=0.5004665658183998, linear_terms=array([-0.07613957,  0.50653673]), square_terms=array([[ 0.01791485, -0.08989396],
+       [-0.08989396,  0.99787974]]), scale=0.08273896939559659, shift=array([3.30955878, 0.57146613])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9784,11 +297,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=50, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.40293874104694194, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=15, candidate_x=array([3.39311852, 0.53771009]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=2.0029050777898285, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  4,  8,  9, 12, 13, 14]), old_indices_discarded=array([ 1,  5,  6,  7, 10, 11]), step_length=0.09012047727108692, relative_step_length=1.0892144044023249, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.16547793879119319, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  4,  8,  9, 12, 13, 14, 15]), model=ScalarModel(intercept=0.34878877603921216, linear_terms=array([-0.03705295,  0.0190055 ]), square_terms=array([[ 0.05682432, -0.30245945],
+       [-0.30245945,  3.1391181 ]]), scale=array([0.146651, 0.146651]), shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9853,11 +366,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=51, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.8774236451798154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=16, candidate_x=array([3.53976952, 0.55095228]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-3.581626565424316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  4,  8,  9, 12, 13, 14, 15]), old_indices_discarded=array([ 1,  3,  5,  6,  7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.08273896939559659, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  8,  9, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.3569052397831979, linear_terms=array([-0.02064809,  0.04404888]), square_terms=array([[ 0.01408808, -0.07135611],
+       [-0.07135611,  0.97836934]]), scale=0.08273896939559659, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9922,11 +435,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=52, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.797912506109746, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=17, candidate_x=array([3.47586747, 0.54000017]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-0.606420050178191, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  8,  9, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 3,  4,  5,  6,  7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.041369484697798296, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  9, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3925932650868588, linear_terms=array([-0.01003772,  0.07129721]), square_terms=array([[ 0.00325831, -0.01173561],
+       [-0.01173561,  0.18224875]]), scale=0.041369484697798296, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -9991,11 +504,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=53, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.0745063691750953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=18, candidate_x=array([3.43406426, 0.52438668]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-3.0969880233882745, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  9, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.020684742348899148, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 15, 17, 18]), model=ScalarModel(intercept=0.3485820297714826, linear_terms=array([-0.00074319, -0.00146985]), square_terms=array([[0.00101407, 0.00939171],
+       [0.00939171, 0.28345108]]), scale=0.020684742348899148, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10060,11 +573,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=54, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.659692155177659, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=19, candidate_x=array([3.41379517, 0.53713169]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-9.757146948690874, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.010342371174449574, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 18, 19]), model=ScalarModel(intercept=0.3516051875331796, linear_terms=array([0.00219875, 0.02465401]), square_terms=array([[0.00023781, 0.00248109],
+       [0.00248109, 0.10837521]]), scale=0.010342371174449574, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10129,12 +642,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=55, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.1722794926592712, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=20, candidate_x=array([3.3827347 , 0.53562304]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-1.1807624978148843, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.005171185587224787, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 19, 20]), model=ScalarModel(intercept=0.35160518753318, linear_terms=array([ 0.00046497, -0.0103518 ]), square_terms=array([[4.56360182e-05, 2.67054733e-04],
+       [2.67054733e-04, 1.94955566e-02]]), scale=0.005171185587224787, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10199,12 +711,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=56, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.672202788033976, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=21, candidate_x=array([3.38868682, 0.54042464]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-7.710929850876411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.0025855927936123935, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 20, 21]), model=ScalarModel(intercept=0.3516051875331794, linear_terms=array([-0.00349048,  0.01334719]), square_terms=array([[ 7.31586941e-05, -3.77051863e-04],
+       [-3.77051863e-04,  7.76160919e-03]]), scale=0.0025855927936123935, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10269,12 +780,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=57, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.575101714153926, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=22, candidate_x=array([3.39298772, 0.53512781]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-0.34754006801086396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.0012927963968061968, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 21, 22]), model=ScalarModel(intercept=0.3516051875331797, linear_terms=array([-0.00568513,  0.00024143]), square_terms=array([[ 1.55033113e-04, -9.21536859e-05],
+       [-9.21536859e-05,  1.58282752e-03]]), scale=0.0012927963968061968, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10339,12 +849,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=58, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.607483524882199, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=23, candidate_x=array([3.39441104, 0.53768295]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=0.09656327619380714, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([15, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0012928078477874524, relative_step_length=1.0000088575287525, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0006463981984030984, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 22, 23]), model=ScalarModel(intercept=0.3510635416029081, linear_terms=array([-2.40997084e-04,  8.69427787e-05]), square_terms=array([[3.02309191e-05, 5.56820834e-05],
+       [5.56820834e-05, 3.87753918e-04]]), scale=0.0006463981984030984, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10409,12 +918,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=59, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.116210148416972, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=24, candidate_x=array([3.39504226, 0.53753499]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=-42.68165354816944, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0003231990992015492, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 23, 24]), model=ScalarModel(intercept=0.351063541602908, linear_terms=array([-0.00064703, -0.02503445]), square_terms=array([[9.71662717e-06, 8.83261117e-05],
+       [8.83261117e-05, 2.28719466e-03]]), scale=0.0003231990992015492, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10479,12 +987,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=60, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.637175211044107, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=25, candidate_x=array([3.39440305, 0.53800605]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=-0.5180215287252007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0001615995496007746, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 24, 25]), model=ScalarModel(intercept=0.35106354160290787, linear_terms=array([0.00404705, 0.00612794]), square_terms=array([[5.08648953e-05, 7.71881874e-05],
+       [7.71881874e-05, 1.60475065e-04]]), scale=0.0001615995496007746, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10549,12 +1056,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=61, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.495381314866356, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=26, candidate_x=array([3.39433057, 0.53754281]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=0.4386967610903063, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.00016159954960085255, relative_step_length=1.0000000000004823, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=0.0003231990992015492, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 23, 24, 25, 26]), model=ScalarModel(intercept=0.3519952953954319, linear_terms=array([0.00185825, 0.00707496]), square_terms=array([[1.77486787e-05, 6.80886070e-05],
+       [6.80886070e-05, 4.34934176e-04]]), scale=0.0003231990992015492, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10619,12 +1125,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=62, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.9461181950584567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=27, candidate_x=array([3.39426412, 0.53722652]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.8921007111780193, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=0.0001615995496007746, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.35566758027340173, linear_terms=array([0.00116083, 0.00029621]), square_terms=array([[ 6.02224251e-06, -1.84823048e-06],
+       [-1.84823048e-06,  1.31048561e-05]]), scale=0.0001615995496007746, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10689,12 +1194,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=63, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.17433364530333087, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=28, candidate_x=array([3.39417348, 0.53750274]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-13.34379427948951, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 24, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=8.07997748003873e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 26, 27, 28]), model=ScalarModel(intercept=0.35346633961662144, linear_terms=array([-0.00450256, -0.00069175]), square_terms=array([[ 9.11343323e-05, -9.14334018e-06],
+       [-9.14334018e-06,  1.04148138e-05]]), scale=8.07997748003873e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10759,12 +1263,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=64, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.2958563051011525, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=29, candidate_x=array([3.3944103 , 0.53755595]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.6064474452786758, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=4.039988740019365e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 26, 28, 29]), model=ScalarModel(intercept=0.35254587409801086, linear_terms=array([-0.00267665,  0.00103318]), square_terms=array([[ 7.31999705e-05, -6.40560604e-05],
+       [-6.40560604e-05,  6.40002230e-05]]), scale=4.039988740019365e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10829,12 +1332,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=65, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.798689482010735, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=30, candidate_x=array([3.39436893, 0.53753014]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-2.5703145265315235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=2.0199943700096824e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 29, 30]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([ 0.00165623, -0.00605254]), square_terms=array([[ 1.99272995e-05, -7.34672931e-05],
+       [-7.34672931e-05,  8.04360412e-04]]), scale=2.0199943700096824e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10899,12 +1401,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=66, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.118373866218775, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=31, candidate_x=array([3.394327  , 0.53756269]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.8852747038198089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1.0099971850048412e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 30, 31]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([0.00284406, 0.00307702]), square_terms=array([[6.07999388e-05, 4.61180689e-05],
+       [4.61180689e-05, 7.99271139e-05]]), scale=1.0099971850048412e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -10969,12 +1470,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=67, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.9267899386607525, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=32, candidate_x=array([3.39432465, 0.53753463]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.5242284841117368, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=5.049985925024206e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 31, 32]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([-0.00552597,  0.00028898]), square_terms=array([[2.90175622e-04, 1.48921274e-05],
+       [1.48921274e-05, 1.22979131e-05]]), scale=5.049985925024206e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11039,12 +1539,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=68, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.9926911255982227, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=33, candidate_x=array([3.3943356 , 0.53754241]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-2.5783615029704325, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=2.524992962512103e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 32, 33]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([ 0.00611459, -0.00627501]), square_terms=array([[ 0.00029739, -0.00029901],
+       [-0.00029901,  0.00032332]]), scale=2.524992962512103e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11109,12 +1608,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=69, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.187283369156568, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=34, candidate_x=array([3.3943293, 0.537545 ]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.7543682741070091, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1.2624964812560515e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 33, 34]), model=ScalarModel(intercept=0.34789201250685414, linear_terms=array([0.00377478, 0.00572821]), square_terms=array([[0.00010336, 0.00010671],
+       [0.00010671, 0.00013639]]), scale=1.2624964812560515e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11179,12 +1677,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=70, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.583719877415852, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=35, candidate_x=array([3.39432995, 0.53754171]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.8632597138234605, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35]), model=ScalarModel(intercept=0.3478920125068536, linear_terms=array([-0.00677101, -0.0011257 ]), square_terms=array([[0.00058792, 0.00026872],
+       [0.00026872, 0.00014363]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11249,12 +1746,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=71, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.5034406606890045, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=36, candidate_x=array([3.39433157, 0.53754288]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.633231931013853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36]), model=ScalarModel(intercept=0.35366192634166027, linear_terms=array([0.00210695, 0.00093369]), square_terms=array([[1.50096793e-04, 1.03796987e-04],
+       [1.03796987e-04, 9.01207269e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11319,13 +1815,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=72, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.590225638860531, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=37, candidate_x=array([3.39432962, 0.53754252]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-5.068479687698034, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37]), model=ScalarModel(intercept=0.3546765935712899, linear_terms=array([ 2.93648505e-04, -8.21538214e-05]), square_terms=array([[6.70773601e-05, 4.72517413e-05],
+       [4.72517413e-05, 5.27614345e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11390,13 +1884,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=73, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.130819247036456, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=38, candidate_x=array([3.39432965, 0.5375432 ]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-35.37352019968308, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3550492056644534, linear_terms=array([-0.00018027, -0.00010127]), square_terms=array([[6.40799410e-05, 5.32720617e-05],
+       [5.32720617e-05, 5.31281796e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11461,13 +1953,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=74, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.8904752260218096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=39, candidate_x=array([3.39433157, 0.53754292]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-67.43828414236042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.3556865640928767, linear_terms=array([6.27777937e-04, 6.15492284e-05]), square_terms=array([[2.96767876e-05, 3.02354652e-05],
+       [3.02354652e-05, 4.47668081e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11532,13 +2022,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=75, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.886828232764809, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=40, candidate_x=array([3.39432957, 0.53754276]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-20.185564466637064, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.3561559441108768, linear_terms=array([-4.87400437e-05, -2.89196899e-04]), square_terms=array([[3.09430241e-05, 3.31085919e-05],
+       [3.31085919e-05, 4.73955318e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11603,13 +2091,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=76, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.463103258193003, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=41, candidate_x=array([3.39433058, 0.53754381]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-29.628047642446706, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.3561366959098984, linear_terms=array([-6.93364006e-05, -3.19511637e-04]), square_terms=array([[2.21115355e-05, 2.09212529e-05],
+       [2.09212529e-05, 3.09525060e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11674,13 +2160,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=77, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.0380243312593045, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=42, candidate_x=array([3.39433068, 0.53754381]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-31.62404440213817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.3562075049579002, linear_terms=array([-0.00068234,  0.00129522]), square_terms=array([[2.86604859e-05, 1.03948890e-05],
+       [1.03948890e-05, 4.95585714e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11745,13 +2229,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=78, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.33396677704667227, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=43, candidate_x=array([3.39433105, 0.53754193]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-11.19745237584309, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.3580325302964799, linear_terms=array([-4.05258899e-05, -2.76046390e-03]), square_terms=array([[1.11011536e-05, 1.00347631e-05],
+       [1.00347631e-05, 5.07447680e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11816,13 +2298,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=79, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.55387001273663, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=44, candidate_x=array([3.39433057, 0.53754381]), index=44, x=array([3.39433057, 0.53754381]), fval=0.3476591866783385, rho=0.08512327562783835, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([34, 35]), step_length=9.999999999835112e-07, relative_step_length=0.9999999999835112, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 45 entries., 'multistart_info': {'start_parameters': [array([3.38599629, 0.53632808]), array([3.30955878, 0.57146613])], 'local_optima': [{'solution_x': array([3.38600552, 0.5363475 ]), 'solution_criterion': 0.3498739082027467, 'states': [State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.33859962886928496, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.35186136241590255, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
+       [0., 0.]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11887,13 +2367,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=80, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.691756352470469, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=0, candidate_x=array([3.38599629, 0.53632808]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.33859962886928496, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=369.3047862599575, linear_terms=array([ -28.95098571, -951.22393704]), square_terms=array([[   1.52027413,   36.97632351],
+       [  36.97632351, 1225.95318706]]), scale=array([0.30007611, 0.23187401]), shift=array([3.38599629, 0.46812599])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -11958,13 +2436,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=81, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.665722020129945, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=13, candidate_x=array([3.57925722, 0.64353416]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.006707567772728848, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.16929981443464248, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  6,  7,  9, 11, 12, 13]), model=ScalarModel(intercept=94.41995342870698, linear_terms=array([ -15.08159299, -300.11654154]), square_terms=array([[  1.3680995 ,  23.86671057],
+       [ 23.86671057, 478.11434348]]), scale=array([0.15003805, 0.15003805]), shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12029,13 +2505,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=82, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.756647691038717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=14, candidate_x=array([3.47111498, 0.62625929]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.0089977765317957, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  6,  7,  9, 11, 12, 13]), old_indices_discarded=array([ 1,  4,  5,  8, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.08464990721732124, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  6,  7,  9, 12, 13, 14]), model=ScalarModel(intercept=72.18515796962691, linear_terms=array([  -2.18870311, -131.66054637]), square_terms=array([[7.99971049e-02, 1.93330404e+00],
+       [1.93330404e+00, 1.20466753e+02]]), scale=0.08464990721732124, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12100,13 +2574,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=83, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.6306215819220474, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=15, candidate_x=array([3.35409792, 0.61473791]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.011095543683425996, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  6,  7,  9, 12, 13, 14]), old_indices_discarded=array([ 1,  4,  5,  8, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.04232495360866062, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 12, 14, 15]), model=ScalarModel(intercept=0.36046321375915297, linear_terms=array([0.00766002, 0.0291521 ]), square_terms=array([[0.01001177, 0.04007171],
+       [0.04007171, 0.27551852]]), scale=0.04232495360866062, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12171,13 +2643,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=84, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.607551605787527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=16, candidate_x=array([3.34370853, 0.53810205]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-4.153031657668594, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.02116247680433031, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.35186136241590255, linear_terms=array([-0.00440049, -0.01618229]), square_terms=array([[ 0.00131763, -0.00867742],
+       [-0.00867742,  0.11383658]]), scale=0.02116247680433031, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12242,13 +2712,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=85, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.661798816678489, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=17, candidate_x=array([3.40687548, 0.54073538]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-2.78269311081207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.010581238402165155, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3518613624159025, linear_terms=array([-0.00096437,  0.02136954]), square_terms=array([[ 0.00019867, -0.00151342],
+       [-0.00151342,  0.13402431]]), scale=0.010581238402165155, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12313,13 +2781,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=86, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.5497455483986289, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=18, candidate_x=array([3.39657469, 0.53476673]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-4.83251680273393, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.0052906192010825775, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.35186136241590255, linear_terms=array([ 0.00388767, -0.01001705]), square_terms=array([[0.0001384 , 0.00093236],
+       [0.00093236, 0.01909587]]), scale=0.0052906192010825775, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12384,13 +2850,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=87, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.745060277058298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=19, candidate_x=array([3.38082158, 0.53880141]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-1.3165886967946658, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.0026453096005412888, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.35186136241590227, linear_terms=array([0.00496819, 0.01548193]), square_terms=array([[0.00020462, 0.00157692],
+       [0.00157692, 0.01517865]]), scale=0.0026453096005412888, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12455,13 +2919,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=88, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.594542262199481, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=20, candidate_x=array([3.38407802, 0.53445856]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.40098042292643604, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.0013226548002706444, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.35186136241590243, linear_terms=array([-0.00150683, -0.00060889]), square_terms=array([[ 5.30718411e-05, -2.03830316e-04],
+       [-2.03830316e-04,  1.28374838e-03]]), scale=0.0013226548002706444, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12526,14 +2988,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=89, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.8986314033575424, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=21, candidate_x=array([3.38726811, 0.53670065]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-4.413438836397079, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.0006613274001353222, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.35186136241590227, linear_terms=array([ 0.00540516, -0.00662359]), square_terms=array([[ 2.49564799e-04, -7.58319149e-05],
+       [-7.58319149e-05,  1.23203840e-04]]), scale=0.0006613274001353222, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12598,14 +3057,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=90, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.1162137473194216, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=22, candidate_x=array([3.38561337, 0.53686727]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.9028902120417925, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.0003306637000676611, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.3518613624159023, linear_terms=array([0.00039012, 0.00458225]), square_terms=array([[3.28424186e-06, 1.93706240e-05],
+       [1.93706240e-05, 4.57908157e-04]]), scale=0.0003306637000676611, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12670,14 +3126,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=91, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.941595710857604, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=23, candidate_x=array([3.3859868 , 0.53599756]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-1.1051418335755745, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=0.00016533185003383055, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.35186136241590277, linear_terms=array([-0.00610776, -0.002185  ]), square_terms=array([[4.96549620e-04, 1.30746529e-04],
+       [1.30746529e-04, 4.32830458e-05]]), scale=0.00016533185003383055, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12742,14 +3195,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=92, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-6.3482879339830305, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=24, candidate_x=array([3.38615619, 0.5363701 ]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-0.42697540633936, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=8.266592501691527e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.3518613624159029, linear_terms=array([ 0.00164726, -0.0012274 ]), square_terms=array([[ 5.67000117e-05, -2.49751626e-05],
+       [-2.49751626e-05,  1.41854562e-05]]), scale=8.266592501691527e-05, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12814,14 +3264,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=93, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.6942135594727958, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=25, candidate_x=array([3.38592579, 0.53637125]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-4.2001211099573394, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=4.133296250845764e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.3518613624159023, linear_terms=array([-0.00092668,  0.00604743]), square_terms=array([[ 1.97553241e-05, -1.00185371e-04],
+       [-1.00185371e-04,  6.37747750e-04]]), scale=4.133296250845764e-05, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12886,14 +3333,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=94, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.9443477528113076, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=26, candidate_x=array([3.38599931, 0.53628686]), index=0, x=array([3.38599629, 0.53632808]), fval=0.35186136241590255, rho=-2.3857285246543043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=2.066648125422882e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.35186136241590305, linear_terms=array([-0.00658781, -0.00697952]), square_terms=array([[0.00053214, 0.00050411],
+       [0.00050411, 0.00048155]]), scale=2.066648125422882e-05, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -12958,14 +3402,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=95, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.15419976120778, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=27, candidate_x=array([3.38600565, 0.53634651]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=0.1195339674487201, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=2.0666481254274287e-05, relative_step_length=1.0000000000022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=4.133296250845764e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 24, 25, 26, 27]), model=ScalarModel(intercept=0.3562116006533669, linear_terms=array([-0.00062403, -0.00305196]), square_terms=array([[1.23665333e-05, 1.44354361e-05],
+       [1.44354361e-05, 4.14676152e-05]]), scale=4.133296250845764e-05, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13030,14 +3471,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=96, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.7049284565591707, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=28, candidate_x=array([3.38601338, 0.53638711]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-4.633566569945299, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=2.066648125422882e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 25, 26, 27, 28]), model=ScalarModel(intercept=0.35834280468937524, linear_terms=array([-2.54536772e-04,  3.85161332e-07]), square_terms=array([[1.26549824e-05, 6.78576180e-06],
+       [6.78576180e-06, 1.10909614e-05]]), scale=2.066648125422882e-05, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13102,14 +3540,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=97, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.347021233223264, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=29, candidate_x=array([3.38602631, 0.53634589]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-27.81046068624094, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1.033324062711441e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 27, 28, 29]), model=ScalarModel(intercept=0.3545141129592593, linear_terms=array([0.00133765, 0.00205926]), square_terms=array([[2.39965733e-05, 6.29895540e-06],
+       [6.29895540e-06, 6.25900413e-05]]), scale=1.033324062711441e-05, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13174,14 +3609,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=98, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.9239849966720888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=30, candidate_x=array([3.3860002 , 0.53633773]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-4.586787832478547, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=5.166620313557205e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 27, 29, 30]), model=ScalarModel(intercept=0.3547672833035468, linear_terms=array([ 0.00062164, -0.0003902 ]), square_terms=array([[ 1.09719357e-05, -9.78731670e-06],
+       [-9.78731670e-06,  2.11834349e-05]]), scale=5.166620313557205e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13246,14 +3678,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=99, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.8569134802127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=31, candidate_x=array([3.38600114, 0.53634902]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-5.494269391580166, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=2.5833101567786023e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 30, 31]), model=ScalarModel(intercept=0.3508146100819427, linear_terms=array([-0.00290084, -0.00137449]), square_terms=array([[ 1.34709040e-04, -1.22646963e-05],
+       [-1.22646963e-05,  2.19649218e-05]]), scale=2.5833101567786023e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13318,14 +3747,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=100, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-2.592689127559629, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([27, 29, 30, 32, 38, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-       71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-       88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=32, candidate_x=array([3.38600796, 0.53634766]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-1.2077668947585871, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1.2916550783893012e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 31, 32]), model=ScalarModel(intercept=0.35081461008194315, linear_terms=array([0.00056518, 0.00293098]), square_terms=array([[1.34789776e-05, 4.51384013e-05],
+       [4.51384013e-05, 2.43643879e-04]]), scale=1.2916550783893012e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13390,15 +3816,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=101, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.3361218252376876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=33, candidate_x=array([3.38600549, 0.53634523]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-1.5245225987179034, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 32, 33]), model=ScalarModel(intercept=0.3508146100819426, linear_terms=array([ 0.00320584, -0.00329279]), square_terms=array([[ 0.00052489, -0.00068924],
+       [-0.00068924,  0.0009381 ]]), scale=1e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13463,15 +3885,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=102, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-1.1831528616194087, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=34, candidate_x=array([3.3860047 , 0.53634681]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-1.0126181506829695, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 32, 33, 34]), model=ScalarModel(intercept=0.35351697295537676, linear_terms=array([ 0.00040882, -0.00043482]), square_terms=array([[ 0.0001282 , -0.00018871],
+       [-0.00018871,  0.00032446]]), scale=1e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13536,16 +3954,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=103, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.563761038428965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=35, candidate_x=array([3.38600469, 0.53634678]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-21.699928254425426, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 32, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 32, 33, 34, 35]), model=ScalarModel(intercept=0.3547069738766576, linear_terms=array([-0.000849  ,  0.00083522]), square_terms=array([[ 3.35030756e-05, -6.43313855e-05],
+       [-6.43313855e-05,  1.68760514e-04]]), scale=1e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13610,16 +4023,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=104, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.495867735639261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=36, candidate_x=array([3.38600654, 0.53634605]), index=27, x=array([3.38600565, 0.53634651]), fval=0.3508146100819424, rho=-10.167680231171396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600565, 0.53634651]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.35582939166219735, linear_terms=array([ 0.0003044 , -0.00093553]), square_terms=array([[ 2.31762194e-05, -5.67035544e-05],
+       [-5.67035544e-05,  1.69567043e-04]]), scale=1e-06, shift=array([3.38600565, 0.53634651])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13684,16 +4092,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=105, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.141138042469495, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=37, candidate_x=array([3.38600552, 0.5363475 ]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=1.0741940176088194, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([27, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=1.0000000000211614e-06, relative_step_length=1.0000000000211615, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=2e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.35432470165000785, linear_terms=array([-0.00038681, -0.00212125]), square_terms=array([[1.10897762e-05, 2.18827976e-05],
+       [2.18827976e-05, 1.65621770e-04]]), scale=2e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13758,16 +4161,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=106, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.26253085023461, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=38, candidate_x=array([3.38600577, 0.53634949]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.65686536819325, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3559542253636117, linear_terms=array([0.00039523, 0.00060171]), square_terms=array([[4.28417698e-06, 5.51782611e-06],
+       [5.51782611e-06, 2.94451212e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13832,16 +4230,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=107, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.702060952107752, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=39, candidate_x=array([3.38600503, 0.53634663]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-10.105831161054212, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.3563161807520077, linear_terms=array([-3.85797672e-06,  7.09334311e-04]), square_terms=array([[ 1.26794611e-05, -1.79077499e-05],
+       [-1.79077499e-05,  4.75810218e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13906,16 +4299,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=108, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-4.873480812672209, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=40, candidate_x=array([3.38600547, 0.5363465 ]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-13.953379367493792, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.35713340812130084, linear_terms=array([0.00185691, 0.00073047]), square_terms=array([[ 8.50206619e-05, -7.69485870e-06],
+       [-7.69485870e-06,  4.77894267e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -13980,16 +4368,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=109, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.5308204847869208, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=41, candidate_x=array([3.3860046 , 0.53634713]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.72521333856114, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([31, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.3572684801697082, linear_terms=array([0.00151078, 0.00061331]), square_terms=array([[6.08222978e-05, 1.88234054e-05],
+       [1.88234054e-05, 2.37951149e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14054,16 +4437,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=110, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-5.088136886367128, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=42, candidate_x=array([3.38600458, 0.53634717]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.692309202321796, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 34, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([31, 32, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 34, 35, 36, 37, 39, 40, 41, 42]), model=ScalarModel(intercept=0.350121396427241, linear_terms=array([-0.00211701, -0.00725435]), square_terms=array([[2.98532683e-05, 4.94795217e-05],
+       [4.94795217e-05, 2.07164315e-04]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14128,16 +4506,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=111, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-0.798183107117362, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.43302075, 0.53650846]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), model=ScalarModel(intercept=0.3531657117852775, linear_terms=array([ 0.00225688, -0.00127711]), square_terms=array([[4.95505487e-05, 2.42934576e-05],
-       [2.42934576e-05, 1.53009058e-04]]), scale=1e-06, shift=array([3.43302075, 0.53650846])), vector_model=VectorModel(intercepts=array([ 0.12451946,  0.23207452,  0.23805405,  0.23807362,  0.21128862,
-        0.16970915,  0.1231641 , -0.01285155, -0.09051547,  0.01558075,
-       -0.20581191, -0.08394371, -0.10460402,  0.00914357,  0.056382  ,
-        0.05456027,  0.05463096]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=43, candidate_x=array([3.38600578, 0.53634847]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.7445853603012595, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 34, 35, 36, 37, 39, 40, 41, 42]), old_indices_discarded=array([31, 32, 33, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 34, 35, 37, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.35677301824184543, linear_terms=array([-0.00138885,  0.00260811]), square_terms=array([[ 1.23964568e-04, -3.81313178e-05],
+       [-3.81313178e-05,  5.68397306e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14202,16 +4575,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.3433042268375101, shift=array([3.43304227, 0.53651214])), candidate_index=112, candidate_x=array([3.43301986, 0.53650895]), index=37, x=array([3.43302075, 0.53650846]), fval=0.34750803159614335, rho=-3.1626583231376575, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 33, 34, 35, 36, 37, 39, 40, 41]), old_indices_discarded=array([ 27,  29,  30,  32,  38,  42,  43,  44,  45,  46,  47,  48,  49,
-        50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,
-        63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,
-        76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,
-        89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101,
-       102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 3.4330422683751007, 'WealthShare': 0.5365121417593192}, {'CRRA': 3.128796818929958, 'WealthShare': 0.23785601955279018}, {'CRRA': 3.7372877178202435, 'WealthShare': 0.3864525813009805}, {'CRRA': 3.128796818929958, 'WealthShare': 0.4871163326122656}, {'CRRA': 3.7372877178202435, 'WealthShare': 0.6786304372717356}, {'CRRA': 3.7372877178202435, 'WealthShare': 0.26374827044227833}, {'CRRA': 3.457621223970444, 'WealthShare': 0.2322666923141764}, {'CRRA': 3.128796818929958, 'WealthShare': 0.5834694461670893}, {'CRRA': 3.68806502711324, 'WealthShare': 0.7}, {'CRRA': 3.589484736331002, 'WealthShare': 0.7}, {'CRRA': 3.1840351927348642, 'WealthShare': 0.7}, {'CRRA': 3.2668451322523016, 'WealthShare': 0.2322666923141764}, {'CRRA': 3.2697531234692105, 'WealthShare': 0.7}, {'CRRA': 3.4293414496677572, 'WealthShare': 0.6483360176482627}, {'CRRA': 3.4600487718987565, 'WealthShare': 0.6556571776542939}, {'CRRA': 3.458651460588676, 'WealthShare': 0.6184284483438415}, {'CRRA': 3.3912268517018753, 'WealthShare': 0.5461859603274469}, {'CRRA': 3.4539062931419062, 'WealthShare': 0.5418143444257352}, {'CRRA': 3.422305597278105, 'WealthShare': 0.5353578530855936}, {'CRRA': 3.4371066018801386, 'WealthShare': 0.532685374299636}, {'CRRA': 3.435026851269979, 'WealthShare': 0.5383242398772852}, {'CRRA': 3.4316862210047865, 'WealthShare': 0.5363450981441692}, {'CRRA': 3.4332581627234156, 'WealthShare': 0.5358773335773711}, {'CRRA': 3.4331353252532404, 'WealthShare': 0.5368342261687626}, {'CRRA': 3.4328746410882607, 'WealthShare': 0.536512903369854}, {'CRRA': 3.433083261008811, 'WealthShare': 0.5364390358374147}, {'CRRA': 3.4330459041799086, 'WealthShare': 0.5365538909974247}, {'CRRA': 3.43302170566752, 'WealthShare': 0.5365081131681623}, {'CRRA': 3.43302172353403, 'WealthShare': 0.5364976363698334}, {'CRRA': 3.4330207095598717, 'WealthShare': 0.5365132559957078}, {'CRRA': 3.4330243242499647, 'WealthShare': 0.5365080561385017}, {'CRRA': 3.433020512070885, 'WealthShare': 0.5365075742955672}, {'CRRA': 3.4330216059208185, 'WealthShare': 0.5365091081810243}, {'CRRA': 3.433020789043295, 'WealthShare': 0.5365085201489797}, {'CRRA': 3.433020730144012, 'WealthShare': 0.5365083789212375}, {'CRRA': 3.4330207306582685, 'WealthShare': 0.5365083752939652}, {'CRRA': 3.4330207050146595, 'WealthShare': 0.536508284146242}, {'CRRA': 3.4330207468375313, 'WealthShare': 0.5365084618939816}, {'CRRA': 3.4330190366907574, 'WealthShare': 0.5365095811376175}, {'CRRA': 3.433021693466694, 'WealthShare': 0.5365081395692565}, {'CRRA': 3.4330217252920807, 'WealthShare': 0.5365082554316494}, {'CRRA': 3.4330207436486897, 'WealthShare': 0.536507461899066}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 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0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}, {'CRRA': 3.4330198560310574, 'WealthShare': 0.5365089519237469}], 'criterion': [0.34949789005232257, nan, 1496.3985023983303, 4.871701497467648, 1.5451455291817051, nan, nan, 0.8942752462183866, 1.6937814082014246, 1.7237729105469575, 2.067581772772813, nan, 1.956986024685801, 1.406797810458186, 1.4720931693060364, 1.1220783810845913, 0.3930057185900624, 0.3723114560427566, 0.36134470784656686, 0.35627482614460365, 0.35461334482898055, 0.3564445848379852, 0.35637650743348964, 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156.08511727800942, 156.97213167700102, 157.84226922498783, 158.71605881498544, 159.60228490200825, 160.4847603229864, 161.3556172400131, 162.21954086699407, 163.08820058198762, 163.97121485500247, 164.8674144539982, 165.75118009600556, 166.64667544999975, 167.53972384601366, 168.42168744601076, 169.29438552999636, 170.182711905014, 171.07244954499765, 171.9671753179864, 172.83879069201066, 173.7083620270132, 174.57692519499687, 175.44118076600716, 176.3100334960036, 177.18236168599105, 178.0567009630031, 178.9244393359986, 179.81789260500227, 180.71143624398974, 181.61025451199384, 182.5127600590058, 183.40831728899502, 184.28851214901078, 185.17290250799851, 186.03956018399913, 186.920712629013, 187.7980914019863, 188.69627261700225, 189.57046607800294, 190.45212187900324, 191.35305039701052, 192.25286294799298, 193.1136268679984, 193.97498348000227, 194.84890272599296, 195.73752984500607, 196.63017732600565, 197.5236466419883, 198.40543974298635, 199.3019605659938, 200.29882341399207, 201.18762097100262, 202.06565941200824, 202.949712592992], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}}, {'solution_x': array([3.38599629, 0.53632808]), 'solution_criterion': 0.3474700534738468, 'states': [State(trustregion=Region(center=array([3.34280356, 0.57157995]), radius=0.33428035588349636, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.651110630913295, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
-       [0., 0.]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=44, candidate_x=array([3.38600596, 0.5363466 ]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.0309722834855313, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 34, 35, 37, 39, 40, 41, 42, 43]), old_indices_discarded=array([31, 32, 33, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 34, 37, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.3572975957007542, linear_terms=array([0.00093255, 0.00199095]), square_terms=array([[7.85830987e-05, 9.84541832e-06],
+       [9.84541832e-06, 4.78118619e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14276,11 +4644,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=0, candidate_x=array([3.34280356, 0.57157995]), index=0, x=array([3.34280356, 0.57157995]), fval=0.6511106309132949, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.34280356, 0.57157995]), radius=0.33428035588349636, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=337.63536471177343, linear_terms=array([ -69.21450094, -906.9362254 ]), square_terms=array([[  24.14592789,   93.10327793],
-       [  93.10327793, 1218.79531266]]), scale=array([0.29624825, 0.21233415]), shift=array([3.34280356, 0.48766585])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=45, candidate_x=array([3.38600511, 0.53634659]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.0742386921380165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 34, 37, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([31, 32, 33, 35, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14345,11 +4713,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=13, candidate_x=array([3.34165591, 0.64573186]), index=0, x=array([3.34280356, 0.57157995]), fval=0.6511106309132949, rho=-0.01075744423379715, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34280356, 0.57157995]), radius=0.16714017794174818, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  3,  4,  6,  8,  9, 10, 12, 13]), model=ScalarModel(intercept=87.96590421832867, linear_terms=array([  16.94785614, -276.08323243]), square_terms=array([[  1.69109393, -26.83928781],
-       [-26.83928781, 434.8423738 ]]), scale=array([0.14812413, 0.13827209]), shift=array([3.34280356, 0.56172791])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=46, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.051268363361451, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14414,11 +4782,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=14, candidate_x=array([3.49092768, 0.65805185]), index=0, x=array([3.34280356, 0.57157995]), fval=0.6511106309132949, rho=-0.011824267815632958, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  6,  8,  9, 10, 12, 13]), old_indices_discarded=array([ 1,  2,  5,  7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34280356, 0.57157995]), radius=0.08357008897087409, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  3,  4,  6,  8,  9, 12, 13, 14]), model=ScalarModel(intercept=54.28950936753732, linear_terms=array([  18.26981028, -138.50294916]), square_terms=array([[  3.11348945, -23.48341396],
-       [-23.48341396, 177.65489671]]), scale=0.08357008897087409, shift=array([3.34280356, 0.57157995])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=47, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-6.930044691302346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14483,11 +4851,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=15, candidate_x=array([3.39184475, 0.64319633]), index=0, x=array([3.34280356, 0.57157995]), fval=0.6511106309132949, rho=-0.013660549592994049, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  6,  8,  9, 12, 13, 14]), old_indices_discarded=array([ 1,  2,  5,  7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.34280356, 0.57157995]), radius=0.041785044485437045, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  4,  9, 12, 13, 14, 15]), model=ScalarModel(intercept=0.725764834434642, linear_terms=array([-0.01504025,  0.24109631]), square_terms=array([[ 0.0032466 , -0.00493084],
-       [-0.00493084,  0.1053344 ]]), scale=0.041785044485437045, shift=array([3.34280356, 0.57157995])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=48, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.9881317723568115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14552,11 +4920,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=16, candidate_x=array([3.33580058, 0.53038592]), index=16, x=array([3.33580058, 0.53038592]), fval=0.3727103252170964, rho=1.5069292886768861, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  4,  9, 12, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.04178504448543702, relative_step_length=0.9999999999999993, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.33580058, 0.53038592]), radius=0.08357008897087409, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  4,  6,  9, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=107.94560993644438, linear_terms=array([  55.85251912, -189.10876542]), square_terms=array([[ 14.51703944, -49.07519171],
-       [-49.07519171, 166.08531832]]), scale=0.08357008897087409, shift=array([3.33580058, 0.53038592])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=49, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.641272324518779, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14621,11 +4989,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=17, candidate_x=array([3.35319573, 0.61212555]), index=16, x=array([3.33580058, 0.53038592]), fval=0.3727103252170964, rho=-0.007023276002626893, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  4,  6,  9, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1,  2,  3,  5,  7,  8, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.33580058, 0.53038592]), radius=0.041785044485437045, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3776781551040974, linear_terms=array([-0.00334584,  0.0237077 ]), square_terms=array([[0.00406171, 0.01230161],
-       [0.01230161, 0.22488074]]), scale=0.041785044485437045, shift=array([3.33580058, 0.53038592])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=50, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-0.8357164471649183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14690,11 +5058,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=18, candidate_x=array([3.37709831, 0.52375992]), index=16, x=array([3.33580058, 0.53038592]), fval=0.3727103252170964, rho=-12.029174803763228, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.33580058, 0.53038592]), radius=0.020892522242718523, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=0.3698732885368718, linear_terms=array([-0.00451711, -0.0406949 ]), square_terms=array([[ 0.01033758, -0.0374601 ],
-       [-0.0374601 ,  0.14432839]]), scale=0.020892522242718523, shift=array([3.33580058, 0.53038592])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=51, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.698474952292186, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14759,11 +5127,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=19, candidate_x=array([3.35481698, 0.54025144]), index=19, x=array([3.35481698, 0.54025144]), fval=0.3664567254392625, rho=0.3281859665661826, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.021423165856161042, relative_step_length=1.0253987339239263, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.35481698, 0.54025144]), radius=0.041785044485437045, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 12, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.3587725662500538, linear_terms=array([-0.00526767,  0.02082611]), square_terms=array([[0.00340207, 0.00833318],
-       [0.00833318, 0.28167295]]), scale=0.041785044485437045, shift=array([3.35481698, 0.54025144])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=52, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.454115128427189, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14828,11 +5196,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=20, candidate_x=array([3.39643101, 0.53597252]), index=20, x=array([3.39643101, 0.53597252]), fval=0.3554561155551655, rho=2.1720550331595736, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([9]), step_length=0.04183344041451278, relative_step_length=1.001158211739911, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39643101, 0.53597252]), radius=0.08357008897087409, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 13, 14, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.3531129780646663, linear_terms=array([ 0.00043157, -0.15546156]), square_terms=array([[ 0.0376359 , -0.21292252],
-       [-0.21292252,  1.55595875]]), scale=0.08357008897087409, shift=array([3.39643101, 0.53597252])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=53, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.176151474721439, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14897,11 +5265,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=21, candidate_x=array([3.47798475, 0.55532748]), index=20, x=array([3.39643101, 0.53597252]), fval=0.3554561155551655, rho=-4.769227729206466, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39643101, 0.53597252]), radius=0.041785044485437045, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 13, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.35772736837084507, linear_terms=array([ 0.00198946, -0.0105233 ]), square_terms=array([[0.00359762, 0.01732426],
-       [0.01732426, 0.38502112]]), scale=0.041785044485437045, shift=array([3.39643101, 0.53597252])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=54, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-11.287772421670637, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -14966,11 +5334,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=22, candidate_x=array([3.35475767, 0.5390258 ]), index=20, x=array([3.39643101, 0.53597252]), fval=0.3554561155551655, rho=-5.322265261648299, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 4,  9, 12, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39643101, 0.53597252]), radius=0.020892522242718523, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.3566625775139682, linear_terms=array([ 0.00073692, -0.00697934]), square_terms=array([[0.00092304, 0.00613359],
-       [0.00613359, 0.14666375]]), scale=0.020892522242718523, shift=array([3.39643101, 0.53597252])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=55, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.269683306069262, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15035,11 +5403,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=23, candidate_x=array([3.37561482, 0.53783266]), index=23, x=array([3.37561482, 0.53783266]), fval=0.35283075025011806, rho=3.0517905306543422, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.020899134283580235, relative_step_length=1.0003164788236143, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37561482, 0.53783266]), radius=0.041785044485437045, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.35536222988170385, linear_terms=array([0.00086008, 0.0026171 ]), square_terms=array([[0.00369217, 0.024453  ],
-       [0.024453  , 0.58370449]]), scale=0.041785044485437045, shift=array([3.37561482, 0.53783266])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=56, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.2768573841796, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15104,11 +5472,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=24, candidate_x=array([3.36386076, 0.53813773]), index=23, x=array([3.37561482, 0.53783266]), fval=0.35283075025011806, rho=-112.20288765237434, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 4,  9, 12, 13, 14, 15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37561482, 0.53783266]), radius=0.020892522242718523, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 22, 23, 24]), model=ScalarModel(intercept=0.35290808386122385, linear_terms=array([-0.00488911, -0.00907737]), square_terms=array([[ 0.0012435 , -0.00945396],
-       [-0.00945396,  0.13712743]]), scale=0.020892522242718523, shift=array([3.37561482, 0.53783266])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=57, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.043895246621858, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15173,11 +5542,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=25, candidate_x=array([3.39636795, 0.54054908]), index=23, x=array([3.37561482, 0.53783266]), fval=0.35283075025011806, rho=-3.066567462023386, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 22, 23, 24]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.37561482, 0.53783266]), radius=0.010446261121359261, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 16, 18, 19, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=0.35651907980698994, linear_terms=array([-0.00026749,  0.00767238]), square_terms=array([[0.00021296, 0.00111306],
-       [0.00111306, 0.06071561]]), scale=0.010446261121359261, shift=array([3.37561482, 0.53783266])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=58, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.122090237012355, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15242,11 +5612,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=26, candidate_x=array([3.3859973 , 0.53632767]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=6.290131796458907, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 20, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.010490990502880989, relative_step_length=1.0042818555847002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.020892522242718523, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([16, 18, 19, 20, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.35426785385973486, linear_terms=array([-0.00248014,  0.01452573]), square_terms=array([[ 0.0006866 , -0.00166885],
-       [-0.00166885,  0.40084598]]), scale=0.020892522242718523, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=59, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.246905723544951, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15311,11 +5682,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=27, candidate_x=array([3.40689015, 0.53566045]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-4.851577496687222, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 18, 19, 20, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 17, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.010446261121359261, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([18, 19, 20, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.3556399810582215, linear_terms=array([-5.35096730e-05,  1.02011073e-02]), square_terms=array([[0.00017383, 0.00160022],
-       [0.00160022, 0.10522455]]), scale=0.010446261121359261, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=60, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.3215667644322755, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15380,11 +5752,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=28, candidate_x=array([3.39638762, 0.5351576 ]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-21.96457819688039, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 19, 20, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.005223130560679631, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([18, 20, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.356295096610289, linear_terms=array([0.0003174 , 0.00526687]), square_terms=array([[4.59532066e-05, 4.83123103e-04],
-       [4.83123103e-04, 2.62038670e-02]]), scale=0.005223130560679631, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=61, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-10.73959585601585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15449,11 +5822,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=29, candidate_x=array([3.38076375, 0.53538096]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-4.888107995816659, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 20, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.0026115652803398153, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([20, 23, 25, 26, 28, 29]), model=ScalarModel(intercept=0.35309811771803185, linear_terms=array([0.00119772, 0.00201317]), square_terms=array([[1.73272987e-05, 1.67080328e-04],
-       [1.67080328e-04, 5.52625014e-03]]), scale=0.0026115652803398153, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=62, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.931733886559184, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15518,11 +5892,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=30, candidate_x=array([3.38336265, 0.53560328]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-4.459789472195381, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 25, 26, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.0013057826401699077, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 29, 30]), model=ScalarModel(intercept=0.34782822109051886, linear_terms=array([ 0.00421943, -0.02722425]), square_terms=array([[ 0.00090159, -0.00353984],
-       [-0.00353984,  0.01443324]]), scale=0.0013057826401699077, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=63, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-0.36556430139474044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15587,11 +5962,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=31, candidate_x=array([3.38626972, 0.53760472]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.3042940396349309, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.0006528913200849538, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 30, 31]), model=ScalarModel(intercept=0.3478282210905187, linear_terms=array([-0.00250355,  0.00316664]), square_terms=array([[ 5.62044140e-05, -1.46847835e-04],
-       [-1.46847835e-04,  4.77310476e-04]]), scale=0.0006528913200849538, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=64, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-6.588037690416145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15656,11 +6032,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=32, candidate_x=array([3.38625963, 0.5357298 ]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-1.4677389077824103, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.0003264456600424769, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 31, 32]), model=ScalarModel(intercept=0.34782822109051886, linear_terms=array([ 6.36949550e-03, -4.24325797e-05]), square_terms=array([[4.73621725e-04, 6.17363895e-05],
-       [6.17363895e-05, 5.64135345e-05]]), scale=0.0003264456600424769, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=65, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-9.495416212627726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15725,11 +6102,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=33, candidate_x=array([3.3856713 , 0.53634463]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-2.112241662632396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=0.00016322283002123846, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 32, 33]), model=ScalarModel(intercept=0.34782822109051875, linear_terms=array([-0.00632797, -0.00419512]), square_terms=array([[0.00039221, 0.00018229],
-       [0.00018229, 0.00010782]]), scale=0.00016322283002123846, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=66, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.212295177528413, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15794,11 +6172,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=34, candidate_x=array([3.38614387, 0.53639951]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.8089182626828136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=8.161141501061923e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 33, 34]), model=ScalarModel(intercept=0.347828221090519, linear_terms=array([-0.00245149,  0.01160207]), square_terms=array([[ 6.83322955e-05, -2.19271941e-04],
-       [-2.19271941e-04,  8.29977837e-04]]), scale=8.161141501061923e-05, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=67, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.714838720034903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15863,11 +6242,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=35, candidate_x=array([3.38600875, 0.53624687]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.4194818217670447, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=4.0805707505309614e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35]), model=ScalarModel(intercept=0.34782822109051886, linear_terms=array([ 0.00254228, -0.0018864 ]), square_terms=array([[ 6.59748775e-05, -8.86654215e-05],
-       [-8.86654215e-05,  1.42100833e-04]]), scale=4.0805707505309614e-05, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=68, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.976867088146723, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -15932,11 +6312,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=36, candidate_x=array([3.38596164, 0.5363475 ]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-3.481470988573249, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=2.0402853752654807e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 35, 36]), model=ScalarModel(intercept=0.34782822109051903, linear_terms=array([-0.00719742, -0.00214315]), square_terms=array([[1.52001012e-04, 6.31676136e-05],
-       [6.31676136e-05, 5.69873117e-05]]), scale=2.0402853752654807e-05, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=69, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.277917783663618, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16001,11 +6382,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=37, candidate_x=array([3.386017  , 0.53633301]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-1.4560750643311462, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=1.0201426876327404e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 36, 37]), model=ScalarModel(intercept=0.3478282210905188, linear_terms=array([0.0027188 , 0.01029047]), square_terms=array([[2.72360453e-05, 8.16772900e-05],
-       [8.16772900e-05, 2.86054270e-04]]), scale=1.0201426876327404e-05, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=70, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-9.08705999829023, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16070,11 +6452,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=38, candidate_x=array([3.38599499, 0.53631774]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.9391391060892705, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=5.100713438163702e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 37, 38]), model=ScalarModel(intercept=0.34782822109051886, linear_terms=array([ 0.00434701, -0.00588312]), square_terms=array([[ 7.33198627e-05, -1.25803949e-04],
-       [-1.25803949e-04,  2.44751878e-04]]), scale=5.100713438163702e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=71, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.0135581598392425, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16139,11 +6522,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=39, candidate_x=array([3.38599476, 0.53633209]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-1.4086744245691452, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=2.550356719081851e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 38, 39]), model=ScalarModel(intercept=0.34782822109051875, linear_terms=array([-0.00997823, -0.00010956]), square_terms=array([[5.25037035e-04, 1.72257328e-05],
-       [1.72257328e-05, 1.09781848e-05]]), scale=2.550356719081851e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=72, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.127397176581123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16208,11 +6592,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=40, candidate_x=array([3.38599985, 0.53632763]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-1.2269600170883905, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=1.2751783595409254e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 39, 40]), model=ScalarModel(intercept=0.3478282210905191, linear_terms=array([0.00591662, 0.00622658]), square_terms=array([[0.00015587, 0.00015738],
-       [0.00015738, 0.00017091]]), scale=1.2751783595409254e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=73, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-10.20824458525985, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16277,11 +6662,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=41, candidate_x=array([3.38599659, 0.53632661]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.8800524488658921, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 40, 41]), model=ScalarModel(intercept=0.34782822109051875, linear_terms=array([ 0.00438978, -0.00958299]), square_terms=array([[ 8.39858244e-05, -2.40461850e-04],
-       [-2.40461850e-04,  8.10083961e-04]]), scale=1e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=74, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.635276948917016, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16346,11 +6733,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=42, candidate_x=array([3.38599698, 0.53632862]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-0.9402211255490968, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 40, 41, 42]), model=ScalarModel(intercept=0.3543138580494716, linear_terms=array([0.00168456, 0.00057868]), square_terms=array([[ 1.77628192e-05, -1.73887243e-05],
-       [-1.73887243e-05,  6.75722131e-05]]), scale=1e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=75, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.736360260094163, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16415,11 +6804,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=43, candidate_x=array([3.38599634, 0.53632734]), index=26, x=array([3.3859973 , 0.53632767]), fval=0.3478282210905188, rho=-8.597056397450775, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.3859973 , 0.53632767]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 40, 41, 42, 43]), model=ScalarModel(intercept=0.3565085088053355, linear_terms=array([ 0.00033861, -0.00017302]), square_terms=array([[4.50390802e-06, 1.47379286e-06],
-       [1.47379286e-06, 9.11569341e-05]]), scale=1e-06, shift=array([3.3859973 , 0.53632767])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=76, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.989316181195964, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16484,11 +6875,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=44, candidate_x=array([3.38599629, 0.53632808]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=0.8835482979169098, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=1.0949958457595914e-06, relative_step_length=1.0949958457595914, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=2e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.3547619398785043, linear_terms=array([0.00170732, 0.00150219]), square_terms=array([[2.39123483e-05, 9.76143461e-06],
-       [9.76143461e-06, 2.61272894e-05]]), scale=2e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=77, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.052251141805401, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16553,11 +6946,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=45, candidate_x=array([3.38599473, 0.53632684]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-1.8077981665596943, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.35458480052971836, linear_terms=array([0.00099619, 0.00086022]), square_terms=array([[7.43762910e-06, 8.51782300e-07],
-       [8.51782300e-07, 3.36827506e-06]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=78, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-0.7762310109173324, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16622,11 +7017,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=46, candidate_x=array([3.38599553, 0.53632744]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-7.488202293152167, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.35506736815028733, linear_terms=array([0.00074824, 0.00068147]), square_terms=array([[ 4.27491856e-06, -2.92140881e-07],
-       [-2.92140881e-07,  3.34333221e-06]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=79, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-11.102909318169958, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16691,11 +7088,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=47, candidate_x=array([3.38599555, 0.53632741]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-15.638554944624026, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.35485092156577025, linear_terms=array([ 0.00058089, -0.00130674]), square_terms=array([[ 7.87372013e-06, -2.01027032e-05],
-       [-2.01027032e-05,  7.56563316e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=80, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-11.214187626188565, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16760,11 +7159,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=48, candidate_x=array([3.38599595, 0.53632902]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-0.41036516933313666, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 40, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 41, 42, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.35338367715599445, linear_terms=array([-0.00040749, -0.00247353]), square_terms=array([[ 1.15568063e-05, -1.10113652e-05],
-       [-1.10113652e-05,  5.46146075e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=81, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-9.354736639325525, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16829,11 +7230,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=49, candidate_x=array([3.38599646, 0.53632907]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.083490495219852, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([39, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 41, 42, 43, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=0.35495601546707, linear_terms=array([-0.00333829, -0.0020202 ]), square_terms=array([[7.23517568e-05, 5.29815506e-05],
-       [5.29815506e-05, 8.36081946e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=82, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-9.447565740182592, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16898,11 +7301,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=50, candidate_x=array([3.38599717, 0.53632856]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-2.275396704412755, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 41, 42, 43, 44, 46, 47, 48, 49]), old_indices_discarded=array([39, 40, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 42, 43, 44, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.355601446438899, linear_terms=array([-0.00192779, -0.00262014]), square_terms=array([[1.61268922e-05, 1.14679067e-05],
-       [1.14679067e-05, 7.73571146e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=83, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.2435662619718784, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -16967,11 +7372,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=51, candidate_x=array([3.38599685, 0.53632891]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-2.7384215911337484, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 42, 43, 44, 46, 47, 48, 49, 50]), old_indices_discarded=array([39, 40, 41, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.3570563299138845, linear_terms=array([ 0.00311745, -0.00546304]), square_terms=array([[ 8.78374362e-05, -1.33743778e-04],
-       [-1.33743778e-04,  2.19905244e-04]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=84, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.209202624123364, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17036,11 +7443,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=52, candidate_x=array([3.38599587, 0.53632899]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-1.970030601076053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([26, 39, 40, 41, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 46, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.3565335127146101, linear_terms=array([ 0.00185895, -0.00252328]), square_terms=array([[ 2.78678646e-05, -4.25171451e-05],
-       [-4.25171451e-05,  1.07765597e-04]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=85, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.267346935301817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17105,11 +7514,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=53, candidate_x=array([3.38599578, 0.53632894]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.242181924356419, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 46, 48, 49, 50, 51, 52]), old_indices_discarded=array([26, 39, 40, 41, 45, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=86, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.0656746132869785, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17174,11 +7585,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=54, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.666681292399065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=87, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.385972620478264, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17243,11 +7656,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=55, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-0.7195588140999996, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=88, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.225794274723245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17312,11 +7727,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=56, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.190957477959568, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=89, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.69987003500903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17381,11 +7798,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=57, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.358146772360783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=90, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.23028663828148, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17450,11 +7869,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=58, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.187177470837372, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=91, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.8302220973936767, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17519,11 +7941,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=59, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.205798299923983, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=92, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-12.606929004357943, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17588,11 +8013,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=60, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.1074548858611415, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=93, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.349436510446207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17657,11 +8085,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=61, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.19847844195024, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=94, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.823732377259821, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17726,11 +8157,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=62, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.20971784052341, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=95, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.252874029202012, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17795,11 +8229,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=63, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.333320886133252, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=96, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.324702940630041, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17864,11 +8301,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=64, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-1.9105010613011533, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=97, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-4.694020443606421, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -17933,12 +8373,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=65, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.3852585783625253, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=98, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.8569023058667105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18003,12 +8445,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=66, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.338756191160101, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=99, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-3.6701549079973064, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18073,12 +8517,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=67, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.557965126034485, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=100, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-5.1488722816755566, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([31, 32, 33, 34, 35, 36, 38, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+       56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+       90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18143,12 +8589,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=68, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.838794432985282, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=101, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-0.5824334083504613, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18213,12 +8662,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=69, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.7345815315485154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=102, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-2.409066546296075, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18283,12 +8735,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=70, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.068516091705679, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=103, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.001156288376406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18353,12 +8808,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=71, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.2763407078125466, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=104, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.810476075682494, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18423,12 +8881,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=72, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.711722491556433, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=105, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-8.140963298981708, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18493,12 +8955,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=73, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.4323184470832064, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=106, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-10.45678486670816, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18563,12 +9029,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=74, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-10.527662747089332, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=107, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-7.398886852992587, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18633,12 +9103,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=75, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-2.6489578525446875, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=108, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-9.631289401642254, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18703,12 +9177,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=76, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.4093643359253445, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=109, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.0552914370512378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18773,12 +9251,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=77, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.7418333669368975, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=110, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-10.070151065488597, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18843,12 +9325,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=78, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.474440020143085, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=111, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-1.5725831782454014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38600552, 0.5363475 ]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3575242011521331, linear_terms=array([0.00019912, 0.00128937]), square_terms=array([[ 1.09085236e-04, -9.07900373e-06],
+       [-9.07900373e-06,  3.01776521e-05]]), scale=1e-06, shift=array([3.38600552, 0.5363475 ])), vector_model=VectorModel(intercepts=array([ 0.1245057 ,  0.23230623,  0.23873604,  0.23911848,  0.21260374,
+        0.17152706,  0.1253866 , -0.00852589, -0.08570856,  0.02093649,
+       -0.20010393, -0.07800846, -0.08731675,  0.02630673,  0.07335506,
+        0.07141013,  0.07139164]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18913,12 +9399,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=79, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.372219741456405, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33859962886928496, shift=array([3.38599629, 0.53632808])), candidate_index=112, candidate_x=array([3.38600537, 0.53634648]), index=37, x=array([3.38600552, 0.5363475 ]), fval=0.3498739082027467, rho=-6.250831135549411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 37, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([ 31,  32,  33,  34,  35,  36,  38,  46,  47,  48,  49,  50,  51,
+        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
+        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
+        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
+        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
+       104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.085920180640578, 'WealthShare': 0.2512539629679427}, {'CRRA': 3.6860723967451205, 'WealthShare': 0.434347454103874}, {'CRRA': 3.085920180640578, 'WealthShare': 0.529072925803897}, {'CRRA': 3.6860723967451205, 'WealthShare': 0.6844247702773922}, {'CRRA': 3.675106450606796, 'WealthShare': 0.23625197655732288}, {'CRRA': 3.423012553569043, 'WealthShare': 0.23625197655732288}, {'CRRA': 3.1032507693565923, 'WealthShare': 0.7}, {'CRRA': 3.6860723967451205, 'WealthShare': 0.6762011172533888}, {'CRRA': 3.623093738952932, 'WealthShare': 0.7}, {'CRRA': 3.085920180640578, 'WealthShare': 0.6972882414786035}, {'CRRA': 3.252172404544021, 'WealthShare': 0.23625197655732288}, 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217.1405652720132, 218.15240250900388, 219.17187067400664, 220.14476079001906], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}}, {'solution_x': array([3.39433057, 0.53754381]), 'solution_criterion': 0.3476591866783385, 'states': [State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.33095587758238637, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.6607127140516494, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
+       [0., 0.]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -18983,12 +9473,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=80, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.344908467794465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=0, candidate_x=array([3.30955878, 0.57146613]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.33095587758238637, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=469100.56003831374, linear_terms=array([ 1029983.98164179, -1223830.90282946]), square_terms=array([[ 1132738.22199273, -1343499.04166053],
+       [-1343499.04166053,  1596421.69293623]]), scale=array([0.29330201, 0.21091794]), shift=array([3.30955878, 0.48908206])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19053,12 +9542,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=81, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.4864171465068585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=13, candidate_x=array([3.3031337 , 0.64688524]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=-7.348577092374725e-06, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.16547793879119319, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  4,  8,  9, 11, 12, 13]), model=ScalarModel(intercept=54.73615878023596, linear_terms=array([  -1.98581212, -208.37733252]), square_terms=array([[8.02353181e-02, 3.56619407e+00],
+       [3.56619407e+00, 3.99052236e+02]]), scale=array([0.146651  , 0.13759244]), shift=array([3.30955878, 0.56240756])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19123,13 +9611,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=82, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.350267914992089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=14, candidate_x=array([3.45620978, 0.63302605]), index=0, x=array([3.30955878, 0.57146613]), fval=0.6607127140516494, rho=-0.01443461348866765, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  4,  8,  9, 11, 12, 13]), old_indices_discarded=array([ 1,  5,  6,  7, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.08273896939559659, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  3,  4,  8,  9, 12, 13, 14]), model=ScalarModel(intercept=0.5004665658183998, linear_terms=array([-0.07613957,  0.50653673]), square_terms=array([[ 0.01791485, -0.08989396],
+       [-0.08989396,  0.99787974]]), scale=0.08273896939559659, shift=array([3.30955878, 0.57146613])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19194,13 +9680,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=83, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.418726845345919, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=15, candidate_x=array([3.39311852, 0.53771009]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=2.0029050777898285, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  3,  4,  8,  9, 12, 13, 14]), old_indices_discarded=array([ 1,  5,  6,  7, 10, 11]), step_length=0.09012047727108692, relative_step_length=1.0892144044023249, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.16547793879119319, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  4,  8,  9, 12, 13, 14, 15]), model=ScalarModel(intercept=0.34878877603921216, linear_terms=array([-0.03705295,  0.0190055 ]), square_terms=array([[ 0.05682432, -0.30245945],
+       [-0.30245945,  3.1391181 ]]), scale=array([0.146651, 0.146651]), shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19265,13 +9749,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=84, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.15308696103769, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=16, candidate_x=array([3.53976952, 0.55095228]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-3.581626565424316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  4,  8,  9, 12, 13, 14, 15]), old_indices_discarded=array([ 1,  3,  5,  6,  7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.08273896939559659, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  2,  8,  9, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.3569052397831979, linear_terms=array([-0.02064809,  0.04404888]), square_terms=array([[ 0.01408808, -0.07135611],
+       [-0.07135611,  0.97836934]]), scale=0.08273896939559659, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19336,13 +9818,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=85, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.443122477292719, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=17, candidate_x=array([3.47586747, 0.54000017]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-0.606420050178191, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  8,  9, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 3,  4,  5,  6,  7, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.041369484697798296, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0,  9, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3925932650868588, linear_terms=array([-0.01003772,  0.07129721]), square_terms=array([[ 0.00325831, -0.01173561],
+       [-0.01173561,  0.18224875]]), scale=0.041369484697798296, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19407,13 +9887,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=86, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.4511848874831035, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=18, candidate_x=array([3.43406426, 0.52438668]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-3.0969880233882745, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  9, 12, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.020684742348899148, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([ 0, 15, 17, 18]), model=ScalarModel(intercept=0.3485820297714826, linear_terms=array([-0.00074319, -0.00146985]), square_terms=array([[0.00101407, 0.00939171],
+       [0.00939171, 0.28345108]]), scale=0.020684742348899148, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19478,13 +9956,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=87, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.059206533556658, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=19, candidate_x=array([3.41379517, 0.53713169]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-9.757146948690874, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.010342371174449574, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 18, 19]), model=ScalarModel(intercept=0.3516051875331796, linear_terms=array([0.00219875, 0.02465401]), square_terms=array([[0.00023781, 0.00248109],
+       [0.00248109, 0.10837521]]), scale=0.010342371174449574, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19549,13 +10025,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=88, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-7.81181923555584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=20, candidate_x=array([3.3827347 , 0.53562304]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-1.1807624978148843, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.005171185587224787, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 19, 20]), model=ScalarModel(intercept=0.35160518753318, linear_terms=array([ 0.00046497, -0.0103518 ]), square_terms=array([[4.56360182e-05, 2.67054733e-04],
+       [2.67054733e-04, 1.94955566e-02]]), scale=0.005171185587224787, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19620,13 +10094,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=89, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.6936165323397105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=21, candidate_x=array([3.38868682, 0.54042464]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-7.710929850876411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.0025855927936123935, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 20, 21]), model=ScalarModel(intercept=0.3516051875331794, linear_terms=array([-0.00349048,  0.01334719]), square_terms=array([[ 7.31586941e-05, -3.77051863e-04],
+       [-3.77051863e-04,  7.76160919e-03]]), scale=0.0025855927936123935, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19691,13 +10163,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=90, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.460042792359161, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=22, candidate_x=array([3.39298772, 0.53512781]), index=15, x=array([3.39311852, 0.53771009]), fval=0.35160518753317954, rho=-0.34754006801086396, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39311852, 0.53771009]), radius=0.0012927963968061968, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 21, 22]), model=ScalarModel(intercept=0.3516051875331797, linear_terms=array([-0.00568513,  0.00024143]), square_terms=array([[ 1.55033113e-04, -9.21536859e-05],
+       [-9.21536859e-05,  1.58282752e-03]]), scale=0.0012927963968061968, shift=array([3.39311852, 0.53771009])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19762,13 +10232,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=91, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.698713834249947, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=23, candidate_x=array([3.39441104, 0.53768295]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=0.09656327619380714, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([15, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0012928078477874524, relative_step_length=1.0000088575287525, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0006463981984030984, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 22, 23]), model=ScalarModel(intercept=0.3510635416029081, linear_terms=array([-2.40997084e-04,  8.69427787e-05]), square_terms=array([[3.02309191e-05, 5.56820834e-05],
+       [5.56820834e-05, 3.87753918e-04]]), scale=0.0006463981984030984, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19833,13 +10301,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=92, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-1.839330909630113, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=24, candidate_x=array([3.39504226, 0.53753499]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=-42.68165354816944, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0003231990992015492, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 23, 24]), model=ScalarModel(intercept=0.351063541602908, linear_terms=array([-0.00064703, -0.02503445]), square_terms=array([[9.71662717e-06, 8.83261117e-05],
+       [8.83261117e-05, 2.28719466e-03]]), scale=0.0003231990992015492, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19904,13 +10370,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=93, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.6533152001136133, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=25, candidate_x=array([3.39440305, 0.53800605]), index=23, x=array([3.39441104, 0.53768295]), fval=0.35106354160290787, rho=-0.5180215287252007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39441104, 0.53768295]), radius=0.0001615995496007746, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 24, 25]), model=ScalarModel(intercept=0.35106354160290787, linear_terms=array([0.00404705, 0.00612794]), square_terms=array([[5.08648953e-05, 7.71881874e-05],
+       [7.71881874e-05, 1.60475065e-04]]), scale=0.0001615995496007746, shift=array([3.39441104, 0.53768295])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -19975,13 +10439,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=94, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-1.6073768568944988, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=26, candidate_x=array([3.39433057, 0.53754281]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=0.4386967610903063, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([23, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.00016159954960085255, relative_step_length=1.0000000000004823, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=0.0003231990992015492, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([15, 23, 24, 25, 26]), model=ScalarModel(intercept=0.3519952953954319, linear_terms=array([0.00185825, 0.00707496]), square_terms=array([[1.77486787e-05, 6.80886070e-05],
+       [6.80886070e-05, 4.34934176e-04]]), scale=0.0003231990992015492, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20046,13 +10508,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=95, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.981420746202243, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=27, candidate_x=array([3.39426412, 0.53722652]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.8921007111780193, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=0.0001615995496007746, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.35566758027340173, linear_terms=array([0.00116083, 0.00029621]), square_terms=array([[ 6.02224251e-06, -1.84823048e-06],
+       [-1.84823048e-06,  1.31048561e-05]]), scale=0.0001615995496007746, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20117,13 +10577,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=96, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.2448061093056473, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=28, candidate_x=array([3.39417348, 0.53750274]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-13.34379427948951, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 24, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=8.07997748003873e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 26, 27, 28]), model=ScalarModel(intercept=0.35346633961662144, linear_terms=array([-0.00450256, -0.00069175]), square_terms=array([[ 9.11343323e-05, -9.14334018e-06],
+       [-9.14334018e-06,  1.04148138e-05]]), scale=8.07997748003873e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20188,13 +10646,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=97, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-2.9245121939174075, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=29, candidate_x=array([3.3944103 , 0.53755595]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.6064474452786758, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=4.039988740019365e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([23, 26, 28, 29]), model=ScalarModel(intercept=0.35254587409801086, linear_terms=array([-0.00267665,  0.00103318]), square_terms=array([[ 7.31999705e-05, -6.40560604e-05],
+       [-6.40560604e-05,  6.40002230e-05]]), scale=4.039988740019365e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20259,13 +10715,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=98, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-2.2674369307802267, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=30, candidate_x=array([3.39436893, 0.53753014]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-2.5703145265315235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([23, 26, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=2.0199943700096824e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 29, 30]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([ 0.00165623, -0.00605254]), square_terms=array([[ 1.99272995e-05, -7.34672931e-05],
+       [-7.34672931e-05,  8.04360412e-04]]), scale=2.0199943700096824e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20330,14 +10784,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=99, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.228560115147837, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
-       98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=31, candidate_x=array([3.394327  , 0.53756269]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.8852747038198089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1.0099971850048412e-05, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 30, 31]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([0.00284406, 0.00307702]), square_terms=array([[6.07999388e-05, 4.61180689e-05],
+       [4.61180689e-05, 7.99271139e-05]]), scale=1.0099971850048412e-05, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20402,14 +10853,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=100, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.6653413471866, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([26, 39, 40, 41, 45, 46, 47, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-       64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
-       98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=32, candidate_x=array([3.39432465, 0.53753463]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.5242284841117368, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=5.049985925024206e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 31, 32]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([-0.00552597,  0.00028898]), square_terms=array([[2.90175622e-04, 1.48921274e-05],
+       [1.48921274e-05, 1.22979131e-05]]), scale=5.049985925024206e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20474,15 +10922,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=101, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.971700714024643, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=33, candidate_x=array([3.3943356 , 0.53754241]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-2.5783615029704325, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=2.524992962512103e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 32, 33]), model=ScalarModel(intercept=0.3478920125068538, linear_terms=array([ 0.00611459, -0.00627501]), square_terms=array([[ 0.00029739, -0.00029901],
+       [-0.00029901,  0.00032332]]), scale=2.524992962512103e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20547,15 +10991,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=102, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.92203882526202, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=34, candidate_x=array([3.3943293, 0.537545 ]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.7543682741070091, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1.2624964812560515e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 33, 34]), model=ScalarModel(intercept=0.34789201250685414, linear_terms=array([0.00377478, 0.00572821]), square_terms=array([[0.00010336, 0.00010671],
+       [0.00010671, 0.00013639]]), scale=1.2624964812560515e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20620,15 +11060,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=103, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.6344423342285825, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=35, candidate_x=array([3.39432995, 0.53754171]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-0.8632597138234605, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35]), model=ScalarModel(intercept=0.3478920125068536, linear_terms=array([-0.00677101, -0.0011257 ]), square_terms=array([[0.00058792, 0.00026872],
+       [0.00026872, 0.00014363]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20693,15 +11129,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=104, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-5.892779452037306, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=36, candidate_x=array([3.39433157, 0.53754288]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-1.633231931013853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36]), model=ScalarModel(intercept=0.35366192634166027, linear_terms=array([0.00210695, 0.00093369]), square_terms=array([[1.50096793e-04, 1.03796987e-04],
+       [1.03796987e-04, 9.01207269e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20766,15 +11198,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=105, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.3909353934584265, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=37, candidate_x=array([3.39432962, 0.53754252]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-5.068479687698034, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37]), model=ScalarModel(intercept=0.3546765935712899, linear_terms=array([ 2.93648505e-04, -8.21538214e-05]), square_terms=array([[6.70773601e-05, 4.72517413e-05],
+       [4.72517413e-05, 5.27614345e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20839,15 +11267,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=106, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-4.324910675139608, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=38, candidate_x=array([3.39432965, 0.5375432 ]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-35.37352019968308, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3550492056644534, linear_terms=array([-0.00018027, -0.00010127]), square_terms=array([[6.40799410e-05, 5.32720617e-05],
+       [5.32720617e-05, 5.31281796e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20912,15 +11336,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=107, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.77927860522737, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=39, candidate_x=array([3.39433157, 0.53754292]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-67.43828414236042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.3556865640928767, linear_terms=array([6.27777937e-04, 6.15492284e-05]), square_terms=array([[2.96767876e-05, 3.02354652e-05],
+       [3.02354652e-05, 4.47668081e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -20985,15 +11405,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=108, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-7.135652454999523, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=40, candidate_x=array([3.39432957, 0.53754276]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-20.185564466637064, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.3561559441108768, linear_terms=array([-4.87400437e-05, -2.89196899e-04]), square_terms=array([[3.09430241e-05, 3.31085919e-05],
+       [3.31085919e-05, 4.73955318e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -21058,15 +11474,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=109, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-3.1094839060266346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=41, candidate_x=array([3.39433058, 0.53754381]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-29.628047642446706, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.3561366959098984, linear_terms=array([-6.93364006e-05, -3.19511637e-04]), square_terms=array([[2.21115355e-05, 2.09212529e-05],
+       [2.09212529e-05, 3.09525060e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -21131,15 +11543,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=110, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-6.81114916569235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=42, candidate_x=array([3.39433068, 0.53754381]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-31.62404440213817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 34, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.3562075049579002, linear_terms=array([-0.00068234,  0.00129522]), square_terms=array([[2.86604859e-05, 1.03948890e-05],
+       [1.03948890e-05, 4.95585714e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -21204,15 +11612,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=111, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.907040305781573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.38599629, 0.53632808]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.35761223484260696, linear_terms=array([-0.00174563, -0.00114058]), square_terms=array([[2.78584246e-05, 3.06222156e-05],
-       [3.06222156e-05, 7.80352905e-05]]), scale=1e-06, shift=array([3.38599629, 0.53632808])), vector_model=VectorModel(intercepts=array([ 0.11517484,  0.20165273,  0.18620886,  0.16695763,  0.12060953,
-        0.06310542,  0.00097825, -0.21028578, -0.30206284, -0.20149905,
-       -0.43202641, -0.3117366 , -0.03807877,  0.07878577,  0.12680524,
-        0.12716808,  0.12768758]), linear_terms=array([[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=43, candidate_x=array([3.39433105, 0.53754193]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-11.19745237584309, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.3580325302964799, linear_terms=array([-4.05258899e-05, -2.76046390e-03]), square_terms=array([[1.11011536e-05, 1.00347631e-05],
+       [1.00347631e-05, 5.07447680e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
        [0., 0.],
        [0., 0.],
        [0., 0.],
@@ -21277,11 +11681,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
         [0., 0.]],
 
        [[0., 0.],
-        [0., 0.]]]), scale=0.33428035588349636, shift=array([3.34280356, 0.57157995])), candidate_index=112, candidate_x=array([3.38599716, 0.53632858]), index=44, x=array([3.38599629, 0.53632808]), fval=0.3474700534738468, rho=-8.98251062208686, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 43, 44, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 26,  39,  40,  41,  45,  46,  47,  54,  55,  56,  57,  58,  59,
-        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,
-        73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,  85,
-        86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
-        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 3.342803558834963, 'WealthShare': 0.571579946415527}, {'CRRA': 3.056793450976187, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.639051810868848, 'WealthShare': 0.47451590578279923}, {'CRRA': 3.0465553068010784, 'WealthShare': 0.5659057617222264}, {'CRRA': 3.499963089386706, 'WealthShare': 0.7}, {'CRRA': 3.639051810868848, 'WealthShare': 0.30018655956366036}, {'CRRA': 3.389650999018406, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.0465553068010784, 'WealthShare': 0.6955569808291606}, {'CRRA': 3.639051810868848, 'WealthShare': 0.6269704836311257}, {'CRRA': 3.458253701473133, 'WealthShare': 0.7}, {'CRRA': 3.065655877321031, 'WealthShare': 0.7}, {'CRRA': 3.216095887232247, 'WealthShare': 0.2753316943816423}, {'CRRA': 3.2804577967265995, 'WealthShare': 0.7}, {'CRRA': 3.341655905914133, 'WealthShare': 0.6457318631541257}, {'CRRA': 3.4909276848519055, 'WealthShare': 0.65805184835626}, {'CRRA': 3.3918447454600544, 'WealthShare': 0.6431963336353185}, {'CRRA': 3.3358005784674627, 'WealthShare': 0.5303859151898463}, {'CRRA': 3.353195731808456, 'WealthShare': 0.6121255531315644}, {'CRRA': 3.37709830857061, 'WealthShare': 0.5237599163349254}, {'CRRA': 3.354816979554147, 'WealthShare': 0.5402514372242311}, {'CRRA': 3.3964310109161833, 'WealthShare': 0.5359725202217566}, {'CRRA': 3.477984745267262, 'WealthShare': 0.555327475149672}, {'CRRA': 3.3547576687619083, 'WealthShare': 0.5390257965509951}, {'CRRA': 3.3756148229595264, 'WealthShare': 0.53783266343029}, {'CRRA': 3.3638607590913794, 'WealthShare': 0.5381377261906201}, {'CRRA': 3.396367948582899, 'WealthShare': 0.540549077411684}, {'CRRA': 3.3859973023212477, 'WealthShare': 0.5363276703954324}, {'CRRA': 3.4068901490316383, 'WealthShare': 0.535660453630372}, {'CRRA': 3.3963876242032662, 'WealthShare': 0.5351576026260113}, {'CRRA': 3.380763751111572, 'WealthShare': 0.5353809598457648}, {'CRRA': 3.3833626506414505, 'WealthShare': 0.5356032815238324}, {'CRRA': 3.386269719812858, 'WealthShare': 0.5376047205172498}, {'CRRA': 3.386259630989039, 'WealthShare': 0.5357297985480461}, {'CRRA': 3.3856712972621, 'WealthShare': 0.5363446253466494}, {'CRRA': 3.3861438659153213, 'WealthShare': 0.536399509139383}, {'CRRA': 3.386008751855222, 'WealthShare': 0.5362468661181211}, {'CRRA': 3.385961638738713, 'WealthShare': 0.5363475000360188}, {'CRRA': 3.3860169953501873, 'WealthShare': 0.5363330052855385}, {'CRRA': 3.3859949868925048, 'WealthShare': 0.5363177352104794}, {'CRRA': 3.385994756546634, 'WealthShare': 0.5363320903851603}, {'CRRA': 3.385999852297034, 'WealthShare': 0.5363276263172663}, {'CRRA': 3.38599659113487, 'WealthShare': 0.5363266119561167}, {'CRRA': 3.3859969836220563, 'WealthShare': 0.536328618251342}, {'CRRA': 3.385996342664318, 'WealthShare': 0.5363273416728321}, {'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.3859947256072442, 'WealthShare': 0.5363268369063479}, {'CRRA': 3.38599552529143, 'WealthShare': 0.5363274386852382}, {'CRRA': 3.385995549515321, 'WealthShare': 0.5363274107349373}, {'CRRA': 3.3859959462312483, 'WealthShare': 0.5363290241414203}, {'CRRA': 3.3859964637925275, 'WealthShare': 0.5363290691603053}, {'CRRA': 3.3859971685633172, 'WealthShare': 0.5363285598231875}, {'CRRA': 3.3859968504644633, 'WealthShare': 0.5363289119019571}, {'CRRA': 3.385995871531211, 'WealthShare': 0.5363289934419042}, {'CRRA': 3.3859957782594887, 'WealthShare': 0.5363289445269069}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 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'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}, {'CRRA': 3.385997157441434, 'WealthShare': 0.5363285798629607}], 'criterion': [0.651110630913295, nan, 5.675714763756907, 0.7485221854034401, 1.7759867886740137, 2392.5294259214493, nan, 2.2752882162041703, 1.1599773152967352, 1.801353776963429, 2.279586147642942, nan, 1.9569316087463757, 1.4492511072714638, 1.4687707033848807, 1.3889244096740674, 0.37271032521709646, 1.1001324223603026, 0.42302895817465713, 0.3664567254392625, 0.3554561155551655, 0.4701858988979148, 0.3618349216318794, 0.35283075025011806, 0.365331885296856, 0.3696513922923923, 0.34782822109051886, 0.35919571256583305, 0.36162328526663284, 0.35140349314595337, 0.35451147259732896, 0.353775659120616, 0.3531810760996626, 0.3607763818510574, 0.35372353344630125, 0.35260750460413165, 0.3584779787553001, 0.35863042305651543, 0.3576746964513782, 0.3578469196590983, 0.35974532243799, 0.3551432851384375, 0.35726946113872765, 0.36330703543067466, 0.34747005347384685, 0.3515454466500686, 0.3573008342047012, 0.3632731671072725, 0.34803895895852505, 0.35760494795389397, 0.35620231830246213, 0.35627750006335007, 0.3595193517369406, 0.36654959548197297, 0.35907617696308386, 0.34894380645611095, 0.360149964924345, 0.3584442568752082, 0.35399782050360196, 0.35813222676565004, 0.35793080626058343, 0.36426163717359616, 0.35609212035759674, 0.35634527597672744, 0.35138301594687704, 0.3544035171849921, 0.3645489448226616, 0.3568053775167482, 0.3573805536114828, 0.3551189774558458, 0.3598991885394153, 0.35418043877444455, 0.35916842738137106, 0.35449990210935556, 0.36903211909762773, 0.35289547452155184, 0.35650102316336, 0.3592300984936151, 0.3586824410477566, 0.35642494605082814, 0.3584171430514978, 0.36075510596233645, 0.3563799857653255, 0.3606164671447005, 0.35802426687222566, 0.36066643267167525, 0.3586348114163227, 0.3578319871624018, 0.36346970724740046, 0.3591313439479261, 0.36479735605288094, 0.3611899180590759, 0.351237249928564, 0.3549525331069434, 0.35076217691122497, 0.35767267143470166, 0.3541158516862408, 0.35345984677913, 0.3521140684915947, 0.35613071186377554, 0.3590734325755311, 0.3617490318108686, 0.3616473176003903, 0.3590101472517972, 0.35953925632577377, 0.36055954652292777, 0.35632805073635726, 0.36545119382408314, 0.36208482697100614, 0.3538386936600433, 0.361420200701409, 0.36571286692643595, 0.3658674402588], 'runtime': [0.0, 0.9554516019998118, 0.996106911014067, 1.036342482024338, 1.0798351330158766, 1.1201098020246718, 1.16300321102608, 1.210370981018059, 1.2489452590234578, 1.3021201550145634, 1.3466687720210757, 1.3880491830059327, 1.4394283470173832, 2.6155129830003716, 3.4893452640098985, 4.358042316016508, 5.247744849009905, 6.12996861099964, 7.001164315006463, 7.874720443011029, 8.733121040015249, 9.598033275018679, 10.481550532014808, 11.376358810026431, 12.271743425022578, 13.18051275901962, 14.072031520016026, 14.94154458300909, 15.840225860010833, 16.726047791016754, 17.615626429003896, 18.493162312021013, 19.365763407025952, 20.251163862005342, 21.118008126009954, 21.99244954402093, 22.8522281810001, 23.711960279004415, 24.578673943004105, 25.44114976702258, 26.307103115017526, 27.20354122002027, 28.105684484005906, 29.004972889000783, 29.903897141019115, 30.787709708005423, 31.683450970012927, 32.56882043700898, 33.44234831799986, 34.331606551015284, 35.205107461020816, 36.075197826023214, 36.95240029602428, 37.82981198700145, 38.69868560699979, 39.56684692201088, 40.44901608701912, 41.320922701008385, 42.1816082970181, 43.056845399027225, 43.9474028290133, 44.84501080002519, 45.72826013300801, 46.615623682009755, 47.51120820501819, 48.384543431020575, 49.271859422005946, 50.16096321301302, 51.057399341021664, 52.0606782840041, 52.92731157701928, 53.93236671600607, 54.86679268901935, 55.77035476602032, 56.642290818010224, 57.532932465022895, 58.41875544501818, 59.306816562020686, 60.1900245960278, 61.08496604501852, 61.98472625002614, 62.88077890101704, 63.78344465201371, 64.66446894002729, 65.55193799600238, 66.43045875101234, 67.31071650900412, 68.19697613801691, 69.10976293601561, 69.9991971940035, 70.87388519602246, 71.74095917501836, 72.61796937300824, 73.48706868602312, 74.35230953502469, 75.21640051601571, 76.09414605502388, 76.98622749300557, 77.88104783202289, 78.76867255702382, 79.6506672250107, 80.53489927301416, 81.4150972510106, 82.29322036300437, 83.16349801199976, 84.0472115650191, 84.91190045201802, 85.78980948100798, 86.66668209902127, 87.5501131270139, 88.42678522600909, 89.2887227400206, 90.15453295601765], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 3.43304227,  0.53651214],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=44, candidate_x=array([3.39433057, 0.53754381]), index=44, x=array([3.39433057, 0.53754381]), fval=0.3476591866783385, rho=0.08512327562783835, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([34, 35]), step_length=9.999999999835112e-07, relative_step_length=0.9999999999835112, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 45 entries., 'history': {'params': [{'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}, {'CRRA': 3.016256765973506, 'WealthShare': 0.29061885915302493}, {'CRRA': 3.602860785674221, 'WealthShare': 0.5097704838031792}, {'CRRA': 3.016256765973506, 'WealthShare': 0.5760370098839758}, {'CRRA': 3.5760478459618126, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.2878392037263486}, {'CRRA': 3.4478588052515033, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.0207215987193226, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.6327324745145311}, {'CRRA': 3.5030790241126377, 'WealthShare': 0.7}, {'CRRA': 3.016256765973506, 'WealthShare': 0.6807913272054849}, {'CRRA': 3.234215209095579, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.292914640909409, 'WealthShare': 0.7}, {'CRRA': 3.303133696480183, 'WealthShare': 0.6468852446280728}, {'CRRA': 3.456209780749042, 'WealthShare': 0.6330260460920982}, {'CRRA': 3.393118516188777, 'WealthShare': 0.5377100904840499}, {'CRRA': 3.5397695211139557, 'WealthShare': 0.5509522833458697}, {'CRRA': 3.4758674679770443, 'WealthShare': 0.5400001711234805}, {'CRRA': 3.434064260747328, 'WealthShare': 0.5243866818724292}, {'CRRA': 3.4137951701197853, 'WealthShare': 0.5371316890165021}, {'CRRA': 3.3827347013962545, 'WealthShare': 0.5356230431833575}, {'CRRA': 3.3886868172194666, 'WealthShare': 0.5404246379718759}, {'CRRA': 3.3929877161471165, 'WealthShare': 0.5351278082679701}, {'CRRA': 3.394411039178595, 'WealthShare': 0.5376829528332149}, {'CRRA': 3.3950422594077354, 'WealthShare': 0.5375349901544691}, {'CRRA': 3.394403048866773, 'WealthShare': 0.5380060531468045}, {'CRRA': 3.394330571104913, 'WealthShare': 0.5375428125336963}, {'CRRA': 3.3942641227083596, 'WealthShare': 0.5372265179426579}, {'CRRA': 3.394173480699256, 'WealthShare': 0.5375027375064382}, {'CRRA': 3.394410295274285, 'WealthShare': 0.537555952565116}, {'CRRA': 3.394368932528548, 'WealthShare': 0.5375301416190236}, {'CRRA': 3.394326995715963, 'WealthShare': 0.5375626935376785}, {'CRRA': 3.3943246528733915, 'WealthShare': 0.5375346281603528}, {'CRRA': 3.3943356046345925, 'WealthShare': 0.5375424051802747}, {'CRRA': 3.394329304009661, 'WealthShare': 0.5375449965801891}, {'CRRA': 3.3943299520790617, 'WealthShare': 0.5375417122135792}, {'CRRA': 3.3943315684886763, 'WealthShare': 0.5375428848222087}, {'CRRA': 3.3943296151296067, 'WealthShare': 0.5375425190865739}, {'CRRA': 3.394329649205157, 'WealthShare': 0.5375432004343061}, {'CRRA': 3.3943315656125383, 'WealthShare': 0.537542917197843}, {'CRRA': 3.3943295696848708, 'WealthShare': 0.5375427637114514}, {'CRRA': 3.3943305792741665, 'WealthShare': 0.5375438125003273}, {'CRRA': 3.394330680756775, 'WealthShare': 0.5375438065037508}, {'CRRA': 3.394331046758134, 'WealthShare': 0.5375419308777375}, {'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}], 'criterion': [0.6607127140516494, nan, 0.7133179969678111, 0.9122250510532794, 1.738814245006418, 50904625.67032898, 116246.71705175268, 2.371483432043779, 1.21707149810191, 1.7742326776217476, 2.1844788091340064, nan, 1.9431589702823127, 1.4900363321437669, 1.2619256393554017, 0.35160518753317954, 0.42838937661127785, 0.36008666150771423, 0.4076867472422282, 0.35498950666354673, 0.3567456338142511, 0.37666426015405113, 0.354837869983808, 0.35106354160290787, 0.36144004176498795, 0.363428759218245, 0.3478920125068537, 0.36129474761431013, 0.36389252870011624, 0.3506283566979166, 0.35511565098469156, 0.35306860499987824, 0.3541434194955959, 0.3617849686943767, 0.3540838274551238, 0.35370468558579427, 0.3585447569605501, 0.35897473002769464, 0.3580444096251574, 0.35816686951261745, 0.3603114273224131, 0.3557617475147986, 0.35761591726779984, 0.3641098629626268, 0.3476591866783385], 'runtime': [0.0, 1.0335186279844493, 1.075547086977167, 1.1165464899968356, 1.158358821994625, 1.2079391409934033, 1.2510499019990675, 1.304747892980231, 1.3741786059981678, 1.4210400559823029, 1.4693867799942382, 1.5261096809990704, 1.604769954981748, 2.7414454839890823, 3.706038322998211, 4.649230179988081, 5.615710240992485, 6.626046998979291, 7.616200939984992, 8.57367010199232, 9.614224569988437, 10.586447503999807, 11.524956123990705, 12.481786197982728, 13.458031254005618, 14.387363051995635, 15.386936976981815, 16.391233448986895, 17.398493871995015, 18.405060699005844, 19.37944886999321, 20.31278933599242, 21.267557780985953, 22.213215073978063, 23.17634274897864, 24.143351596983848, 25.07568769500358, 26.016631281003356, 26.956457163003506, 27.89484377898043, 28.803292337979656, 29.715133089979645, 30.6246158569993, 31.623351027985336, 32.615992790990276], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 3.38599629,  0.53632808],
        [ 3.125     ,  0.65625   ],
        [ 6.5       ,  0.525     ],
        [ 8.1875    ,  0.503125  ],
@@ -21294,7 +11694,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([3.34280356,
        [11.        ,  0.35      ],
        [12.125     ,  0.30625   ],
        [ 3.6875    ,  0.328125  ],
-       [ 8.75      ,  0.2625    ]]), 'exploration_results': array([3.62115019e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
+       [ 8.75      ,  0.2625    ]]), 'exploration_results': array([3.64521041e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
        4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
        3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
        2.18671524e+03, 2.20657404e+03])}}"