mpec_fo_respy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from auxiliary import (read_results, create_tables, create_plots, create_nfxp_plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## General Formulation of MPEC for economic models\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume an economic model is described by the parameter vector $\\theta$ and a state vector $x$ as well as some endogeneous variables $\\sigma$. The data consists of $X = \\{x_i, d_i\\}^M_{i=1}$. $x_i$ is the observed state and $d_i$ is the observed equilibrium outcome of the underlying decision model. $M$ is the number of data points.\n",
    "\n",
    "Generally $\\sigma$ depends on the parameters $\\theta$ through a set of equlibrium conditions. This also includes Bellman Equations. This consistency is expressed by the following condition: \n",
    "\n",
    "\\begin{align}\n",
    "h(\\theta, \\sigma) = 0.\n",
    "\\end{align}\n",
    "\n",
    "For a given $\\theta$, let $\\Sigma(\\theta)$ denote the set of $\\sigma(\\theta)$ for which the equilibrium conditions hold, i.e. $h(\\theta,\\sigma)=0$. \n",
    "\n",
    "\\begin{align}\n",
    "\\Sigma(\\theta) := \\{\\sigma:h(\\theta, \\sigma)=0\\}.\n",
    "\\end{align}\n",
    "\n",
    "Let $\\sigma\\hat(\\theta)$ denote an element of the above set. In the case of the a dynamic discrete-choice model, this represents the expected value function. This set does not have to be a singleton for a given $\\theta$ (imagine e.g. a game with multiple equilibria).\n",
    "\n",
    "For the case of multiple $\\sigma\\hat(\\theta)$ the solution to the maximation of the log likehood function is:\n",
    "\n",
    "\\begin{align}\n",
    "\\hat\\theta = argmax_\\theta \\{max_{\\hat\\sigma(\\theta)\\in\\Sigma(\\theta)} L(\\theta,\\hat\\sigma(\\theta); X)\\}.\n",
    "\\end{align}\n",
    "\n",
    "**NFXP**\n",
    "\n",
    "The nested fixed point algorithm now attempts to evaluate the above log likelihood function $L(.)$ by searching for a $\\theta$ that maximizes it in an outer loop and then, for a given $\\theta$, caluclates all $\\hat\\sigma(\\theta)\\in\\Sigma(\\theta)$ while choosing the one that gives the highest log likelihood (in case $\\Sigma(\\theta)$ is not a singleton).\n",
    "\n",
    "**MPEC** \n",
    "\n",
    "MPEC now goes a different way of ensuring that a guess of $\\theta$ is consistent with the equilibrium condition $h(\\theta, \\sigma)=0$. It models $\\sigma$ explicitly as another parameter vector and makes sure that the equilibrium condition holds by imposing it as a constraint to the new log likelihood function. More formally the log likehood function now becomes $L(\\theta, \\sigma; X)$ and the optimization problem now becomes a constrained optimization looking like the following:\n",
    "\n",
    "\\begin{align}\n",
    "max_{(\\theta, \\sigma)} L(\\theta, \\sigma; X) \n",
    "\\end{align}\n",
    "\\begin{align}\n",
    "subject\\: to\\: h(\\theta, \\sigma) = 0.\n",
    "\\end{align}\n",
    "\n",
    "Su & Judd (2012) provide a proof that those two formulations are actually equivalent in the sense that they yield the same solutions for $\\hat\\theta$. Further they state that MPEC also works for GMM (see Dubé, Fox and Su (2012)) as well as simulated maximum likelihood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Practical Consequences following Rust's Engine Replacement Problem\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of Rust's Engine Replacement problem where we discretize the mileage into a grid of 90 (i.e. $\\sigma$ consists of 90 elements) and assume the cost function to be linear (i.e. $\\theta$ consists of two elements), the MPEC problem consists of 90 nonlinear constraints and 92 parameters. Depending on the complexity of the model the amount of non-linear constraints will quickly become quite large. Su & Judd (2012) report, though, that they successfully solved a structural model with 100,042 constrains and 100,052 parameters within an hour on a 12 GB RAM work station. The constraint Jacobian as well as the Hessian of the Lagrangian were sparse, though, and they provided analytical first and second order derivatives (by using automatic differentiation) as well as their sparsity patterns. This was possible by using KNITRO and won't be with nlopt. In the case of ipopt this is generally possible but python yet seems to lack an efficient tool to perform automatic differentiation applicable to structural models. \n",
    "\n",
    "The big advantage of MPEC is that it is comparably easy to code up as the whole code regarding the solving of the economic model is not needed to be set up. Furthermore at each guess MPEC makes sure that the guess of the parameters is consistent with the economic model and hence it is not necessary to explicitly solve the model at each single guess. Above that constrained algorithms such as ipopt do not enforce the constraints to hold exactly until the algorithm reaches the last iteration. Those two features make it possible that MPEC potentially becomes faster than the NFXP when estimating structural models. Dubé, Fox and Su (2012) report, though, that this possible speed advantage might only be strong if the constraint Jacobian as well as the Hessian of the Lagragian are sparse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Applicability to respy\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please read this section as the starting point for discussion.\n",
    "\n",
    "- Theoretically it seems possible to use MPEC for the estimation. In that case it seems necessary to impose as many nonlinear conditions as there are possible $EMAX$. All those are also modelled explicitly in the log likelihood function as parameters to be estimated. Hence, their dependence on the parameter vector $\\theta$ does not enter the log likelihood function. In the language from before $EMAX$ would be our $\\sigma$ and the condition that $\\sigma$ and $\\theta$ are consistent with each other is cast to the constraints.\n",
    "\n",
    "- In the case of ruspy, this seriously simplified the calculation as the whole calculation for the fixed point (contraction mapping and N-K iterations) was omitted. So the increase in dimensionality of the problem for MPEC was offset by the fact that all the fixed point iterations were not needed anymore. As far as I can see it (although I cannot claim to have fully grasped what is going on in respy as well as Keane & Wolpin (1994, 1997)), this advantage won't be as prominent for respy as this problem has a finite horizon and is solved by backward induction. Hence, no fixed point has to be calculated and therefore no iteration is necessary. With MPEC via ipopt, though, the backward induction does not have to be conducted all the way through such that it holds exactly.\n",
    "\n",
    "- Unforunately, the set up from Su and Judd (2012) cannot be easily applied to respy. First of all, a closed-form first derivative does not exist and hence cannot be found by automatic differentiation (neither does the Hessian, of course, as well as the sparsity patterns). For this reason, I have included a simulation below that is supposed to investigate the sensitivity of MPEC to the use of numerical derivatives following Iskhakov et al. (2016)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Showcasing the response of MPEC to numerical gradients\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I will follow the general setup of Iskhakov et al. (2016) as done in my replication notebook of the paper using ruspy (https://github.com/OpenSourceEconomics/ruspy/blob/replication_notebook_and_documentation/promotion/replication/replication_iskhakov_et_al_2016.ipynb).\n",
    "\n",
    "In the following I run the setup of the named notebook on a smaller scale. I run 600 estimations as opposed to 5000. I use the originally simulated data form the paper for six different $\\beta$ (meaning 50 runs per $\\beta$) which I then estimate with five different starting values each. This I perform for MPEC four times. Once I use ipopt as an algorithm tool and compare its performance with analytical first order derivatives to its performance with finite difference (2-point) derivatives. This I repeat using the optimizer \"LD_SLSQP\" from nlopt. Lastly as a comparison I run the same setup with a nested fixed point algorithm. \n",
    "\n",
    "I find that the convergence rate of MPEC with ipopt stays high. For very high $\\beta$ the numerical derivative introduces a slight downward bias, though. Using nlopt as an algorithm for MPEC proves to be very much unable to converge for a very high $\\beta$ of $0.9999$. The number of function evaluations and iterations needed by ipopt and nlopt goes up with higher $\\beta$.  The CPU time is around 2.5 times higher in the case of numerical derivatives as opposed to analytical. This is likely due to the amount of function evaluations necessary to get the numerical derivatives. I tried to approximate the number of function evaluations for numerical derivatives with 2-point finite differences (the amount of evaluations per approximation is roughly equal to the amount of parameters). The column \"# of Func. Eval.\" shows in the case of numerical derivatives for MPEC the total amount of function plus constraint evaluations needed by the algorithm itself plus for the numerical approximation of the derivatives. This is computationally expensive in the case of MPEC in comparison to NFXP as the dimensionality of the problem of MPEC is much higher. For NFXP the number of function evaluations needed is just slightly higher for numerical than for analytical derivatives. This is because here the gradient of the likelihood function of the NFXP is only a 2 dimensional vector while in the case of MPEC the gradient of the criterion function is a 177 dimensional vector and the Jacobian of the constraint is a $175 \\times 177$ matrix. At the same time, though, per single function evaluation the time needed by MPEC is way lower than by NFXP. This is because the MPEC function can just be evaluated without calculating any fixed point as the discretized expected values are parameters of the likelihood function. NFXP on the other has to repeatedly calculate the fixed point of the expected value function whenever the function is called by the approximation procedure. This results in many more contraction and N-K steps when using numerical derivatives and it introduces a strong dependance of the NFXP on an efficient implementation of the model solving procedure (fixed point calculation in our case). In our ruspy implementation of the NFXP the fixed point calculation seems to work quite well as even several thousand iterations are done quickly but at the same time in the very high regions of $\\beta$ our NFXP becomes quite instable which leads to a low convergence rate when using numerical derivatives.\n",
    "\n",
    "Lastly, it should further be noted that when using 3-point finite differences for MPEC (which means that per approximation of each the function gradient and the constraint Jacobian, each time roughly two evaluations are needed while only one when using 2-point), nlopt and ipopt still need only 3 times as much CPU time when using numerical derivatives as opposed to analytical ones. On top of that, both nlopt and ipopt converge almost always with the estimates for nlopt with numerical and analytical derivatives being basically the same. ipopt still has the problem of a slight downward bias for $\\beta$ equal to $0.9999$ but the magnitude is way lower. This shows that the speed of MPEC is not too sensitive to increasing numbers of function evaluations as no fixed point has to be calculated. The numerical NFXP takes 2.5 times as long as the analytical one and when $\\beta$ gets higher around 3 times as long.\n",
    "\n",
    "This is different for the NFXP. With 3-point approximation, the amount of contraction iteration and N-K iterations increases to around 1.5 times of the amount needed by the 2-point approximation. The numerical NFXP then takes around four times as long as the NFXP with analytical derivative. But the NFXP then always converges even for very high $\\beta$'s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the results of the estimation strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the results of the six estimation strategies\n",
    "results_ipopt, results_nlopt, results_nfxp = read_results()\n",
    "results = [results_ipopt, results_nlopt, results_nfxp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0.9999</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">9</th>\n",
       "      <th>2</th>\n",
       "      <th>MPEC (numerical)</th>\n",
       "      <td>10.0957</td>\n",
       "      <td>1.96682</td>\n",
       "      <td>6.022</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>13986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>MPEC</th>\n",
       "      <td>10.6258</td>\n",
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       "      <td>2.619</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>MPEC (numerical)</th>\n",
       "      <td>10.096</td>\n",
       "      <td>1.96692</td>\n",
       "      <td>8.379</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>18018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>MPEC</th>\n",
       "      <td>10.6258</td>\n",
       "      <td>2.14888</td>\n",
       "      <td>2.372</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>MPEC (numerical)</th>\n",
       "      <td>10.0962</td>\n",
       "      <td>1.967</td>\n",
       "      <td>5.401</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>12568</td>\n",
       "      <td>NaN</td>\n",
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       "                                                 RC theta_11 CPU Time  \\\n",
       "Discount Factor Run Start Approach                                      \n",
       "0.9750          0   0     MPEC              8.89422  1.55117    1.484   \n",
       "                          MPEC (numerical)  8.89423  1.55117    4.369   \n",
       "                    1     MPEC              8.89422  1.55117    1.117   \n",
       "                          MPEC (numerical)  8.89421  1.55117    3.442   \n",
       "                    2     MPEC              8.89422  1.55117    1.344   \n",
       "...                                             ...      ...      ...   \n",
       "0.9999          9   2     MPEC (numerical)  10.0957  1.96682    6.022   \n",
       "                    3     MPEC              10.6258  2.14888    2.619   \n",
       "                          MPEC (numerical)   10.096  1.96692    8.379   \n",
       "                    4     MPEC              10.6258  2.14888    2.372   \n",
       "                          MPEC (numerical)  10.0962    1.967    5.401   \n",
       "\n",
       "                                           Converged # of Major Iter.  \\\n",
       "Discount Factor Run Start Approach                                      \n",
       "0.9750          0   0     MPEC                     1               23   \n",
       "                          MPEC (numerical)         1               21   \n",
       "                    1     MPEC                     1               15   \n",
       "                          MPEC (numerical)         1               17   \n",
       "                    2     MPEC                     1               19   \n",
       "...                                              ...              ...   \n",
       "0.9999          9   2     MPEC (numerical)         1               37   \n",
       "                    3     MPEC                     1               31   \n",
       "                          MPEC (numerical)         1               48   \n",
       "                    4     MPEC                     1               30   \n",
       "                          MPEC (numerical)         1               33   \n",
       "\n",
       "                                           # of Func. Eval. # of Bellm. Iter.  \\\n",
       "Discount Factor Run Start Approach                                              \n",
       "0.9750          0   0     MPEC                           29               NaN   \n",
       "                          MPEC (numerical)             8240               NaN   \n",
       "                    1     MPEC                           17               NaN   \n",
       "                          MPEC (numerical)             6802               NaN   \n",
       "                    2     MPEC                           23               NaN   \n",
       "...                                                     ...               ...   \n",
       "0.9999          9   2     MPEC (numerical)            13986               NaN   \n",
       "                    3     MPEC                           65               NaN   \n",
       "                          MPEC (numerical)            18018               NaN   \n",
       "                    4     MPEC                           61               NaN   \n",
       "                          MPEC (numerical)            12568               NaN   \n",
       "\n",
       "                                           # of N-K Iter.  \n",
       "Discount Factor Run Start Approach                         \n",
       "0.9750          0   0     MPEC                        NaN  \n",
       "                          MPEC (numerical)            NaN  \n",
       "                    1     MPEC                        NaN  \n",
       "                          MPEC (numerical)            NaN  \n",
       "                    2     MPEC                        NaN  \n",
       "...                                                   ...  \n",
       "0.9999          9   2     MPEC (numerical)            NaN  \n",
       "                    3     MPEC                        NaN  \n",
       "                          MPEC (numerical)            NaN  \n",
       "                    4     MPEC                        NaN  \n",
       "                          MPEC (numerical)            NaN  \n",
       "\n",
       "[600 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at one dataframe to get an idea\n",
    "results_ipopt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create tables showing the means and standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create tables with means and standard deviations\n",
    "tables = create_tables(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9750</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4181</td>\n",
       "      <td>1.4681</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>20.1400</td>\n",
       "      <td>26.6800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2122</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>0.1727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.4909</td>\n",
       "      <td>5.4863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4180</td>\n",
       "      <td>3.4981</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>20.5400</td>\n",
       "      <td>8078.6400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2122</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>0.5094</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.9912</td>\n",
       "      <td>1072.6316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9850</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0694</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>1.4709</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>19.8600</td>\n",
       "      <td>28.3200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.2007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.9898</td>\n",
       "      <td>5.0606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0693</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>3.3806</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>20.0800</td>\n",
       "      <td>7917.7200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.5591</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.1548</td>\n",
       "      <td>1130.3441</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9950</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8233</td>\n",
       "      <td>2.4418</td>\n",
       "      <td>1.6392</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>21.6400</td>\n",
       "      <td>34.4800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3411</td>\n",
       "      <td>0.3849</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.5442</td>\n",
       "      <td>6.8935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8229</td>\n",
       "      <td>2.4417</td>\n",
       "      <td>3.6688</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>21.6800</td>\n",
       "      <td>8499.4400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3409</td>\n",
       "      <td>0.3848</td>\n",
       "      <td>0.6290</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0780</td>\n",
       "      <td>1463.7719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9990</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9685</td>\n",
       "      <td>2.5288</td>\n",
       "      <td>2.0247</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>25.3800</td>\n",
       "      <td>41.6400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3180</td>\n",
       "      <td>0.4386</td>\n",
       "      <td>0.2942</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.4339</td>\n",
       "      <td>10.3070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9572</td>\n",
       "      <td>2.5252</td>\n",
       "      <td>4.8234</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>10744.3600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3125</td>\n",
       "      <td>0.4368</td>\n",
       "      <td>1.2610</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.8513</td>\n",
       "      <td>2893.3622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9995</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0823</td>\n",
       "      <td>2.5746</td>\n",
       "      <td>2.0288</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>25.8200</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.4332</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.3060</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.8950</td>\n",
       "      <td>9.6664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0595</td>\n",
       "      <td>2.5683</td>\n",
       "      <td>4.7960</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>28.1429</td>\n",
       "      <td>10838.7347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.4110</td>\n",
       "      <td>0.4606</td>\n",
       "      <td>0.7086</td>\n",
       "      <td>0.1414</td>\n",
       "      <td>4.2426</td>\n",
       "      <td>1555.0340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9999</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9333</td>\n",
       "      <td>2.5222</td>\n",
       "      <td>2.2742</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>28.3200</td>\n",
       "      <td>51.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.5556</td>\n",
       "      <td>0.5083</td>\n",
       "      <td>0.3304</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.9406</td>\n",
       "      <td>9.5212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>10.9791</td>\n",
       "      <td>2.2093</td>\n",
       "      <td>7.2583</td>\n",
       "      <td>0.9400</td>\n",
       "      <td>44.1064</td>\n",
       "      <td>16605.4468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.0548</td>\n",
       "      <td>0.3441</td>\n",
       "      <td>2.1827</td>\n",
       "      <td>0.2399</td>\n",
       "      <td>13.7860</td>\n",
       "      <td>5009.3518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          RC  theta_11  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          MPEC             Mean                11.4877    2.4181   \n",
       "                                 Standard Deviation   1.2122    0.3990   \n",
       "                MPEC (numerical) Mean                11.4877    2.4180   \n",
       "                                 Standard Deviation   1.2122    0.3990   \n",
       "0.9850          MPEC             Mean                12.0694    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "                MPEC (numerical) Mean                12.0693    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "0.9950          MPEC             Mean                11.8233    2.4418   \n",
       "                                 Standard Deviation   1.3411    0.3849   \n",
       "                MPEC (numerical) Mean                11.8229    2.4417   \n",
       "                                 Standard Deviation   1.3409    0.3848   \n",
       "0.9990          MPEC             Mean                11.9685    2.5288   \n",
       "                                 Standard Deviation   1.3180    0.4386   \n",
       "                MPEC (numerical) Mean                11.9572    2.5252   \n",
       "                                 Standard Deviation   1.3125    0.4368   \n",
       "0.9995          MPEC             Mean                12.0823    2.5746   \n",
       "                                 Standard Deviation   1.4332    0.4690   \n",
       "                MPEC (numerical) Mean                12.0595    2.5683   \n",
       "                                 Standard Deviation   1.4110    0.4606   \n",
       "0.9999          MPEC             Mean                11.9333    2.5222   \n",
       "                                 Standard Deviation   1.5556    0.5083   \n",
       "                MPEC (numerical) Mean                10.9791    2.2093   \n",
       "                                 Standard Deviation   1.0548    0.3441   \n",
       "\n",
       "                                                     CPU Time  Converged  \\\n",
       "Discount Factor Approach         Statistic                                 \n",
       "0.9750          MPEC             Mean                  1.4681     1.0000   \n",
       "                                 Standard Deviation    0.1727     0.0000   \n",
       "                MPEC (numerical) Mean                  3.4981     1.0000   \n",
       "                                 Standard Deviation    0.5094     0.0000   \n",
       "0.9850          MPEC             Mean                  1.4709     1.0000   \n",
       "                                 Standard Deviation    0.2007     0.0000   \n",
       "                MPEC (numerical) Mean                  3.3806     1.0000   \n",
       "                                 Standard Deviation    0.5591     0.0000   \n",
       "0.9950          MPEC             Mean                  1.6392     1.0000   \n",
       "                                 Standard Deviation    0.2576     0.0000   \n",
       "                MPEC (numerical) Mean                  3.6688     1.0000   \n",
       "                                 Standard Deviation    0.6290     0.0000   \n",
       "0.9990          MPEC             Mean                  2.0247     1.0000   \n",
       "                                 Standard Deviation    0.2942     0.0000   \n",
       "                MPEC (numerical) Mean                  4.8234     1.0000   \n",
       "                                 Standard Deviation    1.2610     0.0000   \n",
       "0.9995          MPEC             Mean                  2.0288     1.0000   \n",
       "                                 Standard Deviation    0.3060     0.0000   \n",
       "                MPEC (numerical) Mean                  4.7960     0.9800   \n",
       "                                 Standard Deviation    0.7086     0.1414   \n",
       "0.9999          MPEC             Mean                  2.2742     1.0000   \n",
       "                                 Standard Deviation    0.3304     0.0000   \n",
       "                MPEC (numerical) Mean                  7.2583     0.9400   \n",
       "                                 Standard Deviation    2.1827     0.2399   \n",
       "\n",
       "                                                     # of Major Iter.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          MPEC             Mean                         20.1400   \n",
       "                                 Standard Deviation            2.4909   \n",
       "                MPEC (numerical) Mean                         20.5400   \n",
       "                                 Standard Deviation            2.9912   \n",
       "0.9850          MPEC             Mean                         19.8600   \n",
       "                                 Standard Deviation            2.9898   \n",
       "                MPEC (numerical) Mean                         20.0800   \n",
       "                                 Standard Deviation            3.1548   \n",
       "0.9950          MPEC             Mean                         21.6400   \n",
       "                                 Standard Deviation            3.5442   \n",
       "                MPEC (numerical) Mean                         21.6800   \n",
       "                                 Standard Deviation            4.0780   \n",
       "0.9990          MPEC             Mean                         25.3800   \n",
       "                                 Standard Deviation            3.4339   \n",
       "                MPEC (numerical) Mean                         27.9000   \n",
       "                                 Standard Deviation            7.8513   \n",
       "0.9995          MPEC             Mean                         25.8200   \n",
       "                                 Standard Deviation            3.8950   \n",
       "                MPEC (numerical) Mean                         28.1429   \n",
       "                                 Standard Deviation            4.2426   \n",
       "0.9999          MPEC             Mean                         28.3200   \n",
       "                                 Standard Deviation            3.9406   \n",
       "                MPEC (numerical) Mean                         44.1064   \n",
       "                                 Standard Deviation           13.7860   \n",
       "\n",
       "                                                     # of Func. Eval.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          MPEC             Mean                         26.6800   \n",
       "                                 Standard Deviation            5.4863   \n",
       "                MPEC (numerical) Mean                       8078.6400   \n",
       "                                 Standard Deviation         1072.6316   \n",
       "0.9850          MPEC             Mean                         28.3200   \n",
       "                                 Standard Deviation            5.0606   \n",
       "                MPEC (numerical) Mean                       7917.7200   \n",
       "                                 Standard Deviation         1130.3441   \n",
       "0.9950          MPEC             Mean                         34.4800   \n",
       "                                 Standard Deviation            6.8935   \n",
       "                MPEC (numerical) Mean                       8499.4400   \n",
       "                                 Standard Deviation         1463.7719   \n",
       "0.9990          MPEC             Mean                         41.6400   \n",
       "                                 Standard Deviation           10.3070   \n",
       "                MPEC (numerical) Mean                      10744.3600   \n",
       "                                 Standard Deviation         2893.3622   \n",
       "0.9995          MPEC             Mean                         42.5000   \n",
       "                                 Standard Deviation            9.6664   \n",
       "                MPEC (numerical) Mean                      10838.7347   \n",
       "                                 Standard Deviation         1555.0340   \n",
       "0.9999          MPEC             Mean                         51.6000   \n",
       "                                 Standard Deviation            9.5212   \n",
       "                MPEC (numerical) Mean                      16605.4468   \n",
       "                                 Standard Deviation         5009.3518   \n",
       "\n",
       "                                                     # of Bellm. Iter.  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9850          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9950          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9990          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9995          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9999          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "\n",
       "                                                     # of N-K Iter.  \n",
       "Discount Factor Approach         Statistic                           \n",
       "0.9750          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9850          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9950          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9990          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9995          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9999          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MPEC with ipopt\n",
    "tables[\"ipopt\"].astype(float).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>RC</th>\n",
       "      <th>theta_11</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Converged</th>\n",
       "      <th># of Major Iter.</th>\n",
       "      <th># of Func. Eval.</th>\n",
       "      <th># of Bellm. Iter.</th>\n",
       "      <th># of N-K Iter.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount Factor</th>\n",
       "      <th>Approach</th>\n",
       "      <th>Statistic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9750</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4181</td>\n",
       "      <td>1.3647</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>20.8400</td>\n",
       "      <td>20.8400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2125</td>\n",
       "      <td>0.3991</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.6386</td>\n",
       "      <td>3.6386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4181</td>\n",
       "      <td>3.2674</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>26.4800</td>\n",
       "      <td>9373.9200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2125</td>\n",
       "      <td>0.3991</td>\n",
       "      <td>0.4003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.1287</td>\n",
       "      <td>2169.5555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9850</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0694</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>1.5283</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>22.8600</td>\n",
       "      <td>22.8600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.2169</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.6590</td>\n",
       "      <td>3.6590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0694</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>3.5557</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>29.4600</td>\n",
       "      <td>10428.8400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.5208</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.8561</td>\n",
       "      <td>2073.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9950</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8233</td>\n",
       "      <td>2.4418</td>\n",
       "      <td>1.6966</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>25.2400</td>\n",
       "      <td>25.2400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3411</td>\n",
       "      <td>0.3849</td>\n",
       "      <td>0.2043</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.9360</td>\n",
       "      <td>3.9360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8231</td>\n",
       "      <td>2.4417</td>\n",
       "      <td>3.8561</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>32.1000</td>\n",
       "      <td>11363.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3409</td>\n",
       "      <td>0.3848</td>\n",
       "      <td>0.5665</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.6645</td>\n",
       "      <td>2713.2408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9990</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9684</td>\n",
       "      <td>2.5288</td>\n",
       "      <td>2.0694</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3179</td>\n",
       "      <td>0.4385</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.2762</td>\n",
       "      <td>4.2762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9578</td>\n",
       "      <td>2.5254</td>\n",
       "      <td>4.5124</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>38.3400</td>\n",
       "      <td>13572.3600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3125</td>\n",
       "      <td>0.4368</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.6120</td>\n",
       "      <td>1986.6399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9995</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0824</td>\n",
       "      <td>2.5746</td>\n",
       "      <td>2.2135</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>31.7000</td>\n",
       "      <td>31.7000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.4332</td>\n",
       "      <td>0.4691</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.1955</td>\n",
       "      <td>4.1955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0228</td>\n",
       "      <td>2.5554</td>\n",
       "      <td>4.9376</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>40.2449</td>\n",
       "      <td>14246.6939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.4209</td>\n",
       "      <td>0.4651</td>\n",
       "      <td>0.9218</td>\n",
       "      <td>0.1414</td>\n",
       "      <td>5.4334</td>\n",
       "      <td>1923.4325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9999</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9336</td>\n",
       "      <td>2.5223</td>\n",
       "      <td>2.5056</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>34.6400</td>\n",
       "      <td>34.6400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.5555</td>\n",
       "      <td>0.5082</td>\n",
       "      <td>0.3118</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.3481</td>\n",
       "      <td>5.3481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MPEC (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>10.3608</td>\n",
       "      <td>2.0198</td>\n",
       "      <td>4.7476</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>36.3333</td>\n",
       "      <td>12862.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>0.6817</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.4467</td>\n",
       "      <td>0.2399</td>\n",
       "      <td>3.0551</td>\n",
       "      <td>1081.4879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          RC  theta_11  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          MPEC             Mean                11.4877    2.4181   \n",
       "                                 Standard Deviation   1.2125    0.3991   \n",
       "                MPEC (numerical) Mean                11.4877    2.4181   \n",
       "                                 Standard Deviation   1.2125    0.3991   \n",
       "0.9850          MPEC             Mean                12.0694    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "                MPEC (numerical) Mean                12.0694    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "0.9950          MPEC             Mean                11.8233    2.4418   \n",
       "                                 Standard Deviation   1.3411    0.3849   \n",
       "                MPEC (numerical) Mean                11.8231    2.4417   \n",
       "                                 Standard Deviation   1.3409    0.3848   \n",
       "0.9990          MPEC             Mean                11.9684    2.5288   \n",
       "                                 Standard Deviation   1.3179    0.4385   \n",
       "                MPEC (numerical) Mean                11.9578    2.5254   \n",
       "                                 Standard Deviation   1.3125    0.4368   \n",
       "0.9995          MPEC             Mean                12.0824    2.5746   \n",
       "                                 Standard Deviation   1.4332    0.4691   \n",
       "                MPEC (numerical) Mean                12.0228    2.5554   \n",
       "                                 Standard Deviation   1.4209    0.4651   \n",
       "0.9999          MPEC             Mean                11.9336    2.5223   \n",
       "                                 Standard Deviation   1.5555    0.5082   \n",
       "                MPEC (numerical) Mean                10.3608    2.0198   \n",
       "                                 Standard Deviation   0.6817    0.2419   \n",
       "\n",
       "                                                     CPU Time  Converged  \\\n",
       "Discount Factor Approach         Statistic                                 \n",
       "0.9750          MPEC             Mean                  1.3647     1.0000   \n",
       "                                 Standard Deviation    0.1945     0.0000   \n",
       "                MPEC (numerical) Mean                  3.2674     1.0000   \n",
       "                                 Standard Deviation    0.4003     0.0000   \n",
       "0.9850          MPEC             Mean                  1.5283     1.0000   \n",
       "                                 Standard Deviation    0.2169     0.0000   \n",
       "                MPEC (numerical) Mean                  3.5557     1.0000   \n",
       "                                 Standard Deviation    0.5208     0.0000   \n",
       "0.9950          MPEC             Mean                  1.6966     1.0000   \n",
       "                                 Standard Deviation    0.2043     0.0000   \n",
       "                MPEC (numerical) Mean                  3.8561     1.0000   \n",
       "                                 Standard Deviation    0.5665     0.0000   \n",
       "0.9990          MPEC             Mean                  2.0694     1.0000   \n",
       "                                 Standard Deviation    0.2431     0.0000   \n",
       "                MPEC (numerical) Mean                  4.5124     1.0000   \n",
       "                                 Standard Deviation    0.7128     0.0000   \n",
       "0.9995          MPEC             Mean                  2.2135     1.0000   \n",
       "                                 Standard Deviation    0.2500     0.0000   \n",
       "                MPEC (numerical) Mean                  4.9376     0.9800   \n",
       "                                 Standard Deviation    0.9218     0.1414   \n",
       "0.9999          MPEC             Mean                  2.5056     1.0000   \n",
       "                                 Standard Deviation    0.3118     0.0000   \n",
       "                MPEC (numerical) Mean                  4.7476     0.0600   \n",
       "                                 Standard Deviation    0.4467     0.2399   \n",
       "\n",
       "                                                     # of Major Iter.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          MPEC             Mean                         20.8400   \n",
       "                                 Standard Deviation            3.6386   \n",
       "                MPEC (numerical) Mean                         26.4800   \n",
       "                                 Standard Deviation            6.1287   \n",
       "0.9850          MPEC             Mean                         22.8600   \n",
       "                                 Standard Deviation            3.6590   \n",
       "                MPEC (numerical) Mean                         29.4600   \n",
       "                                 Standard Deviation            5.8561   \n",
       "0.9950          MPEC             Mean                         25.2400   \n",
       "                                 Standard Deviation            3.9360   \n",
       "                MPEC (numerical) Mean                         32.1000   \n",
       "                                 Standard Deviation            7.6645   \n",
       "0.9990          MPEC             Mean                         29.6000   \n",
       "                                 Standard Deviation            4.2762   \n",
       "                MPEC (numerical) Mean                         38.3400   \n",
       "                                 Standard Deviation            5.6120   \n",
       "0.9995          MPEC             Mean                         31.7000   \n",
       "                                 Standard Deviation            4.1955   \n",
       "                MPEC (numerical) Mean                         40.2449   \n",
       "                                 Standard Deviation            5.4334   \n",
       "0.9999          MPEC             Mean                         34.6400   \n",
       "                                 Standard Deviation            5.3481   \n",
       "                MPEC (numerical) Mean                         36.3333   \n",
       "                                 Standard Deviation            3.0551   \n",
       "\n",
       "                                                     # of Func. Eval.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          MPEC             Mean                         20.8400   \n",
       "                                 Standard Deviation            3.6386   \n",
       "                MPEC (numerical) Mean                       9373.9200   \n",
       "                                 Standard Deviation         2169.5555   \n",
       "0.9850          MPEC             Mean                         22.8600   \n",
       "                                 Standard Deviation            3.6590   \n",
       "                MPEC (numerical) Mean                      10428.8400   \n",
       "                                 Standard Deviation         2073.0708   \n",
       "0.9950          MPEC             Mean                         25.2400   \n",
       "                                 Standard Deviation            3.9360   \n",
       "                MPEC (numerical) Mean                      11363.4000   \n",
       "                                 Standard Deviation         2713.2408   \n",
       "0.9990          MPEC             Mean                         29.6000   \n",
       "                                 Standard Deviation            4.2762   \n",
       "                MPEC (numerical) Mean                      13572.3600   \n",
       "                                 Standard Deviation         1986.6399   \n",
       "0.9995          MPEC             Mean                         31.7000   \n",
       "                                 Standard Deviation            4.1955   \n",
       "                MPEC (numerical) Mean                      14246.6939   \n",
       "                                 Standard Deviation         1923.4325   \n",
       "0.9999          MPEC             Mean                         34.6400   \n",
       "                                 Standard Deviation            5.3481   \n",
       "                MPEC (numerical) Mean                      12862.0000   \n",
       "                                 Standard Deviation         1081.4879   \n",
       "\n",
       "                                                     # of Bellm. Iter.  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9850          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9950          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9990          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9995          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "0.9999          MPEC             Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "                MPEC (numerical) Mean                              NaN   \n",
       "                                 Standard Deviation                NaN   \n",
       "\n",
       "                                                     # of N-K Iter.  \n",
       "Discount Factor Approach         Statistic                           \n",
       "0.9750          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9850          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9950          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9990          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9995          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "0.9999          MPEC             Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  \n",
       "                MPEC (numerical) Mean                           NaN  \n",
       "                                 Standard Deviation             NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MPEC with nlopt\n",
    "tables[\"nlopt\"].astype(float).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RC</th>\n",
       "      <th>theta_11</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Converged</th>\n",
       "      <th># of Major Iter.</th>\n",
       "      <th># of Func. Eval.</th>\n",
       "      <th># of Bellm. Iter.</th>\n",
       "      <th># of N-K Iter.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount Factor</th>\n",
       "      <th>Approach</th>\n",
       "      <th>Statistic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9750</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4181</td>\n",
       "      <td>1.0800</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>9.9800</td>\n",
       "      <td>12.1800</td>\n",
       "      <td>262.0800</td>\n",
       "      <td>88.2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2122</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>0.2655</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.6146</td>\n",
       "      <td>2.8692</td>\n",
       "      <td>57.1764</td>\n",
       "      <td>18.9481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.4877</td>\n",
       "      <td>2.4181</td>\n",
       "      <td>2.5510</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>10.1200</td>\n",
       "      <td>24.7600</td>\n",
       "      <td>1671.5200</td>\n",
       "      <td>578.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2122</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>0.6952</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.0549</td>\n",
       "      <td>6.9387</td>\n",
       "      <td>458.9746</td>\n",
       "      <td>153.7218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9850</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0694</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>1.2406</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11.2400</td>\n",
       "      <td>13.5400</td>\n",
       "      <td>290.4200</td>\n",
       "      <td>103.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.3305</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.1594</td>\n",
       "      <td>3.4712</td>\n",
       "      <td>69.4251</td>\n",
       "      <td>25.6962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0694</td>\n",
       "      <td>2.5473</td>\n",
       "      <td>2.9140</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11.3200</td>\n",
       "      <td>27.2400</td>\n",
       "      <td>1847.2800</td>\n",
       "      <td>679.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.6792</td>\n",
       "      <td>0.5293</td>\n",
       "      <td>0.7646</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.1780</td>\n",
       "      <td>6.9622</td>\n",
       "      <td>467.3697</td>\n",
       "      <td>175.3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9950</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8233</td>\n",
       "      <td>2.4418</td>\n",
       "      <td>1.2612</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>10.7400</td>\n",
       "      <td>12.8600</td>\n",
       "      <td>275.6800</td>\n",
       "      <td>101.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3411</td>\n",
       "      <td>0.3849</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0849</td>\n",
       "      <td>4.2379</td>\n",
       "      <td>84.7747</td>\n",
       "      <td>35.2554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8233</td>\n",
       "      <td>2.4418</td>\n",
       "      <td>2.9585</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>10.8200</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>1759.9200</td>\n",
       "      <td>660.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3411</td>\n",
       "      <td>0.3849</td>\n",
       "      <td>1.1684</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.1092</td>\n",
       "      <td>8.5520</td>\n",
       "      <td>587.0407</td>\n",
       "      <td>244.5231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9990</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9684</td>\n",
       "      <td>2.5288</td>\n",
       "      <td>1.8817</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11.8400</td>\n",
       "      <td>13.8800</td>\n",
       "      <td>297.2200</td>\n",
       "      <td>168.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3179</td>\n",
       "      <td>0.4385</td>\n",
       "      <td>1.0475</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.2929</td>\n",
       "      <td>5.4046</td>\n",
       "      <td>107.6857</td>\n",
       "      <td>98.8503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8309</td>\n",
       "      <td>2.4730</td>\n",
       "      <td>4.9606</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>12.1364</td>\n",
       "      <td>31.7273</td>\n",
       "      <td>2075.5455</td>\n",
       "      <td>1178.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.2936</td>\n",
       "      <td>0.4239</td>\n",
       "      <td>2.9592</td>\n",
       "      <td>0.3283</td>\n",
       "      <td>5.6716</td>\n",
       "      <td>14.3419</td>\n",
       "      <td>895.8024</td>\n",
       "      <td>758.1117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9995</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>12.0824</td>\n",
       "      <td>2.5746</td>\n",
       "      <td>3.0172</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11.0800</td>\n",
       "      <td>13.1400</td>\n",
       "      <td>282.0400</td>\n",
       "      <td>255.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.4332</td>\n",
       "      <td>0.4691</td>\n",
       "      <td>1.1295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.2129</td>\n",
       "      <td>4.3660</td>\n",
       "      <td>87.6908</td>\n",
       "      <td>93.0855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.8405</td>\n",
       "      <td>2.5110</td>\n",
       "      <td>9.4765</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>10.9487</td>\n",
       "      <td>35.9487</td>\n",
       "      <td>2155.5897</td>\n",
       "      <td>2037.0769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.3493</td>\n",
       "      <td>0.4563</td>\n",
       "      <td>5.0752</td>\n",
       "      <td>0.4185</td>\n",
       "      <td>3.6052</td>\n",
       "      <td>29.7162</td>\n",
       "      <td>1229.2076</td>\n",
       "      <td>1246.5464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0.9999</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP</th>\n",
       "      <th>Mean</th>\n",
       "      <td>11.9336</td>\n",
       "      <td>2.5223</td>\n",
       "      <td>2.7524</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11.1400</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>279.2400</td>\n",
       "      <td>273.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>1.5555</td>\n",
       "      <td>0.5082</td>\n",
       "      <td>0.8761</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.9693</td>\n",
       "      <td>3.8861</td>\n",
       "      <td>78.0127</td>\n",
       "      <td>79.6949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">NFXP (numerical)</th>\n",
       "      <th>Mean</th>\n",
       "      <td>10.7840</td>\n",
       "      <td>2.1038</td>\n",
       "      <td>10.6143</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>10.6250</td>\n",
       "      <td>47.5000</td>\n",
       "      <td>2553.5000</td>\n",
       "      <td>2511.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td>0.4486</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>4.5645</td>\n",
       "      <td>0.3703</td>\n",
       "      <td>2.0659</td>\n",
       "      <td>24.5590</td>\n",
       "      <td>1038.6099</td>\n",
       "      <td>1066.8185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          RC  theta_11  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          NFXP             Mean                11.4877    2.4181   \n",
       "                                 Standard Deviation   1.2122    0.3990   \n",
       "                NFXP (numerical) Mean                11.4877    2.4181   \n",
       "                                 Standard Deviation   1.2122    0.3990   \n",
       "0.9850          NFXP             Mean                12.0694    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "                NFXP (numerical) Mean                12.0694    2.5473   \n",
       "                                 Standard Deviation   1.6792    0.5293   \n",
       "0.9950          NFXP             Mean                11.8233    2.4418   \n",
       "                                 Standard Deviation   1.3411    0.3849   \n",
       "                NFXP (numerical) Mean                11.8233    2.4418   \n",
       "                                 Standard Deviation   1.3411    0.3849   \n",
       "0.9990          NFXP             Mean                11.9684    2.5288   \n",
       "                                 Standard Deviation   1.3179    0.4385   \n",
       "                NFXP (numerical) Mean                11.8309    2.4730   \n",
       "                                 Standard Deviation   1.2936    0.4239   \n",
       "0.9995          NFXP             Mean                12.0824    2.5746   \n",
       "                                 Standard Deviation   1.4332    0.4691   \n",
       "                NFXP (numerical) Mean                11.8405    2.5110   \n",
       "                                 Standard Deviation   1.3493    0.4563   \n",
       "0.9999          NFXP             Mean                11.9336    2.5223   \n",
       "                                 Standard Deviation   1.5555    0.5082   \n",
       "                NFXP (numerical) Mean                10.7840    2.1038   \n",
       "                                 Standard Deviation   0.4486    0.1200   \n",
       "\n",
       "                                                     CPU Time  Converged  \\\n",
       "Discount Factor Approach         Statistic                                 \n",
       "0.9750          NFXP             Mean                  1.0800     1.0000   \n",
       "                                 Standard Deviation    0.2655     0.0000   \n",
       "                NFXP (numerical) Mean                  2.5510     1.0000   \n",
       "                                 Standard Deviation    0.6952     0.0000   \n",
       "0.9850          NFXP             Mean                  1.2406     1.0000   \n",
       "                                 Standard Deviation    0.3305     0.0000   \n",
       "                NFXP (numerical) Mean                  2.9140     1.0000   \n",
       "                                 Standard Deviation    0.7646     0.0000   \n",
       "0.9950          NFXP             Mean                  1.2612     1.0000   \n",
       "                                 Standard Deviation    0.5100     0.0000   \n",
       "                NFXP (numerical) Mean                  2.9585     1.0000   \n",
       "                                 Standard Deviation    1.1684     0.0000   \n",
       "0.9990          NFXP             Mean                  1.8817     1.0000   \n",
       "                                 Standard Deviation    1.0475     0.0000   \n",
       "                NFXP (numerical) Mean                  4.9606     0.8800   \n",
       "                                 Standard Deviation    2.9592     0.3283   \n",
       "0.9995          NFXP             Mean                  3.0172     1.0000   \n",
       "                                 Standard Deviation    1.1295     0.0000   \n",
       "                NFXP (numerical) Mean                  9.4765     0.7800   \n",
       "                                 Standard Deviation    5.0752     0.4185   \n",
       "0.9999          NFXP             Mean                  2.7524     1.0000   \n",
       "                                 Standard Deviation    0.8761     0.0000   \n",
       "                NFXP (numerical) Mean                 10.6143     0.1600   \n",
       "                                 Standard Deviation    4.5645     0.3703   \n",
       "\n",
       "                                                     # of Major Iter.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          NFXP             Mean                          9.9800   \n",
       "                                 Standard Deviation            2.6146   \n",
       "                NFXP (numerical) Mean                         10.1200   \n",
       "                                 Standard Deviation            3.0549   \n",
       "0.9850          NFXP             Mean                         11.2400   \n",
       "                                 Standard Deviation            3.1594   \n",
       "                NFXP (numerical) Mean                         11.3200   \n",
       "                                 Standard Deviation            3.1780   \n",
       "0.9950          NFXP             Mean                         10.7400   \n",
       "                                 Standard Deviation            4.0849   \n",
       "                NFXP (numerical) Mean                         10.8200   \n",
       "                                 Standard Deviation            4.1092   \n",
       "0.9990          NFXP             Mean                         11.8400   \n",
       "                                 Standard Deviation            5.2929   \n",
       "                NFXP (numerical) Mean                         12.1364   \n",
       "                                 Standard Deviation            5.6716   \n",
       "0.9995          NFXP             Mean                         11.0800   \n",
       "                                 Standard Deviation            4.2129   \n",
       "                NFXP (numerical) Mean                         10.9487   \n",
       "                                 Standard Deviation            3.6052   \n",
       "0.9999          NFXP             Mean                         11.1400   \n",
       "                                 Standard Deviation            3.9693   \n",
       "                NFXP (numerical) Mean                         10.6250   \n",
       "                                 Standard Deviation            2.0659   \n",
       "\n",
       "                                                     # of Func. Eval.  \\\n",
       "Discount Factor Approach         Statistic                              \n",
       "0.9750          NFXP             Mean                         12.1800   \n",
       "                                 Standard Deviation            2.8692   \n",
       "                NFXP (numerical) Mean                         24.7600   \n",
       "                                 Standard Deviation            6.9387   \n",
       "0.9850          NFXP             Mean                         13.5400   \n",
       "                                 Standard Deviation            3.4712   \n",
       "                NFXP (numerical) Mean                         27.2400   \n",
       "                                 Standard Deviation            6.9622   \n",
       "0.9950          NFXP             Mean                         12.8600   \n",
       "                                 Standard Deviation            4.2379   \n",
       "                NFXP (numerical) Mean                         25.9200   \n",
       "                                 Standard Deviation            8.5520   \n",
       "0.9990          NFXP             Mean                         13.8800   \n",
       "                                 Standard Deviation            5.4046   \n",
       "                NFXP (numerical) Mean                         31.7273   \n",
       "                                 Standard Deviation           14.3419   \n",
       "0.9995          NFXP             Mean                         13.1400   \n",
       "                                 Standard Deviation            4.3660   \n",
       "                NFXP (numerical) Mean                         35.9487   \n",
       "                                 Standard Deviation           29.7162   \n",
       "0.9999          NFXP             Mean                         13.0000   \n",
       "                                 Standard Deviation            3.8861   \n",
       "                NFXP (numerical) Mean                         47.5000   \n",
       "                                 Standard Deviation           24.5590   \n",
       "\n",
       "                                                     # of Bellm. Iter.  \\\n",
       "Discount Factor Approach         Statistic                               \n",
       "0.9750          NFXP             Mean                         262.0800   \n",
       "                                 Standard Deviation            57.1764   \n",
       "                NFXP (numerical) Mean                        1671.5200   \n",
       "                                 Standard Deviation           458.9746   \n",
       "0.9850          NFXP             Mean                         290.4200   \n",
       "                                 Standard Deviation            69.4251   \n",
       "                NFXP (numerical) Mean                        1847.2800   \n",
       "                                 Standard Deviation           467.3697   \n",
       "0.9950          NFXP             Mean                         275.6800   \n",
       "                                 Standard Deviation            84.7747   \n",
       "                NFXP (numerical) Mean                        1759.9200   \n",
       "                                 Standard Deviation           587.0407   \n",
       "0.9990          NFXP             Mean                         297.2200   \n",
       "                                 Standard Deviation           107.6857   \n",
       "                NFXP (numerical) Mean                        2075.5455   \n",
       "                                 Standard Deviation           895.8024   \n",
       "0.9995          NFXP             Mean                         282.0400   \n",
       "                                 Standard Deviation            87.6908   \n",
       "                NFXP (numerical) Mean                        2155.5897   \n",
       "                                 Standard Deviation          1229.2076   \n",
       "0.9999          NFXP             Mean                         279.2400   \n",
       "                                 Standard Deviation            78.0127   \n",
       "                NFXP (numerical) Mean                        2553.5000   \n",
       "                                 Standard Deviation          1038.6099   \n",
       "\n",
       "                                                     # of N-K Iter.  \n",
       "Discount Factor Approach         Statistic                           \n",
       "0.9750          NFXP             Mean                       88.2200  \n",
       "                                 Standard Deviation         18.9481  \n",
       "                NFXP (numerical) Mean                      578.1800  \n",
       "                                 Standard Deviation        153.7218  \n",
       "0.9850          NFXP             Mean                      103.7000  \n",
       "                                 Standard Deviation         25.6962  \n",
       "                NFXP (numerical) Mean                      679.1000  \n",
       "                                 Standard Deviation        175.3460  \n",
       "0.9950          NFXP             Mean                      101.4400  \n",
       "                                 Standard Deviation         35.2554  \n",
       "                NFXP (numerical) Mean                      660.8400  \n",
       "                                 Standard Deviation        244.5231  \n",
       "0.9990          NFXP             Mean                      168.8000  \n",
       "                                 Standard Deviation         98.8503  \n",
       "                NFXP (numerical) Mean                     1178.5000  \n",
       "                                 Standard Deviation        758.1117  \n",
       "0.9995          NFXP             Mean                      255.4400  \n",
       "                                 Standard Deviation         93.0855  \n",
       "                NFXP (numerical) Mean                     2037.0769  \n",
       "                                 Standard Deviation       1246.5464  \n",
       "0.9999          NFXP             Mean                      273.0600  \n",
       "                                 Standard Deviation         79.6949  \n",
       "                NFXP (numerical) Mean                     2511.0000  \n",
       "                                 Standard Deviation       1066.8185  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NFXP\n",
    "tables[\"nfxp\"].astype(float).round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show comparison plots between analytical and numerical strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AM2hbQsSzrJ4RRPxWKv84Y4ARfoPBYADPAurlevxe4S/T4zcRv8FgMBxAFEzTLLINlBfxb3sJnrsx0zE4+4zHiF8I8cHRbojBYDCMKeV4/F6xt8oQ/q3Pw9M/yLzvWD1jnP5ZUviF4tACb51cofYYDAbD2FBWVk+xdM4iQi5E9n72OPD4pZQS+EuB179TsRYZDIbi9LbCk98Zuma8Yfh48/hX/hHW/y3z3pqHYfUDw0/nFP7sY5dzV7EfKMfqWSqEMBG+wXAgsOFJePGn0L9rrFty8GF7snr+fhMs/23mvaW/hpd+VWLmbpHBXZ8j/DlWzxhH/OWswHUW8GkhxFZgEBCom4HjK9kwg8FQADcqNRH/qOOt1WOlMiINYCXBSuRE/GXU6hG+7G1dqyddePv9RDnC/4GRHlwI0Qj8BjgWdWX+BVgH/BGYBWwFLpNSdo/0HAbDhMIuw4c2jAyvDWOnsz18OwXpxPDz+EVOxO+mcx7Ag7sAUsptwKHA+/TzaDn7aX4GPC6lPBKYC6wBrgeeklLOAZ7SvxsMhnIop4KkYWR4fXg7nZ21YznCn/Oau28x4fdlv2+Nk3ROIcR3gK8BX9cvBYE7y9ivHngP8FsAKWVSStkDXAws0pstAhYOv9kGwwTFtXrGVxngAxYr7Zm45ZnAZVvZEb+VUnbPcPP4HY8/1+oZB4O7HwIuQvn7SCl3AXVl7PcOoAO4TQixQgjxGyFEDTBVSrlbH2s3MGVELTcYJiIHSFbIQcOtC+DF/1XPbc/4iV3A48+zesqo1ZMX8R8Yg7vlCH9Sp3VKAC3e5RAATgRullLOQ3UcZds6QohrhBDLhRDLOzo6yt3NYDi4MYO7o0v3dti7RT33dqZWMj9100qOwON3hH/8pXPeK4T4P6BRCHE1sBg1YDsUrUCrlHKZ/v1+VEfQLoSYBqAf9xTaWUp5i5TyJCnlSS0tLWWczmCYAJQzychQPra2cCB7bkQ6WSDij5fI4y8znXM8TOACkFLeiBLtPwFHAP8upbypjP3agB1CiCP0S2cDbwEPAVfq164EHhxBuw2GiUmuH23YN+y0EnTIjsLT8cIe/7AncOWkcx4gJRuGTOcUQvxISvk14MkCrw3FtcAfhBAhYDPwSVRnc68Q4lPAduDSEbXcYJiIGKtndHGydSDHs7cKZ/A4nQSMcObugZHVU04e/7morB4vHyjwWh5SyteBkwq8dXYZ5zUYDLkcIB7xQYFtAdIT8eeItzed04n+k4Oe14Zj9eRU5xzj76+o8Ash/g34DPAOIcQqz1t1wIuVbpjBYCiAm3Jo0jn3GUeEnYg/V4ztHI8fIBXN3x+Gb/UcwBH/XcBjwA/Jzsbpl1LurWirDAZDYcqpGW8oD9e+KWD1eN+3rYywexdJH1ZWT+7M3QNX+P1AH/DZ3DeEEM1G/A2GMcA2Hv+o8NT3YfqJ6nnRiF+LtDe7p5jVM1StnnHk8b9K5tOInPckaoKWwWDYn5iSDaPD338BR1+snjsef26pazfi9wi/N+IvZyGW3Jm7B3pWj5Ry9v5siMFgKANvWQHDyLFTGb9+KI/fG/Gnig3uDrNI24Gex28wGA4gjNWz79i2un5O9F4sq0faatssq2eEg7uu1TN+avUYDIYDBTO4u+84gu0Ivztzt8A19c7sheFn9eSmc46X6pwGg+EAwo34TTrnkET3Fl6i0om6XaunwMxdB6uU8HsnfBUb3M1dc/fAyOoZarF1nxDizf3VGIPBMARmcLc8EgPwv8fCWw/kv2flCL+VVB1EsYjfG9kXtXqKCX/uzN0DI49/qMXWbWClEOJt+6k9BoOhFN4FwQ3FSQ6ogdi+3fnv5Vo9oJZVLNSZWumciN+bx1/GQix5a+4eGCtwlVOyYRqwWgjxMromP4CU8qKKtcpgONjpbYVYNxxy3PD2M0XaysOJ6r2infue17ZJx4tE/Ol9zOoptuZukY7btmHjYphzbsYmqgDlCP/3KnZ2g2GisuSH0LocPrts6G29mMHd8iiUipn7njd6TxeJ+HMXZEkW8/iHu+Zuke9v+9/hrkvh6mdgxomFtxkFhhR+KeWzQoipwMn6pZellAVr6BsMhjJJDmaLSLkYj7883AlT5Ub8ieKDu1kTuPYhnVPKofP4nZnB3hnCFaCcNXcvA15GlU++DFgmhPjHirbKYDjYsa2R+fQmj788XKsnkf9eVqkFTe6yit5ti2X1ZN1NDFGd07ZzOopiVk+JDmsUKcfq+SZwshPlCyFaUKtw3V/JhhkMBzWySBbJkPsZ4S+LUlZPodfS8RLpnOVk9ZRRpC1rEZci2+fW9KkQ5eTx+3Ksna4y9zMYDMUwEX9lcQusFYic7ULCnyie8+89Rnqk1Tmt7A5nHET8jwshngDu1r9fDjxauSYZDBMAaY0w4ncW9DCDuyWxSgi/VcjqKRbxpwt3FDDMmbu5EX+R789pQ6G7klGknMHdrwohPgycgarSeYuUssCsCIPBUDYjjfiN1VMe5WT1eCmWx5+b1ZP1XjkTuDzpnGV5/E6+/xgLP4CU8k+oxdYNBsNoIK3iPm8pzNKL5ZG7yEqh97wUG9y1Sgn/cNI5c6yeoSL+YncZo0RRr14I8YJ+7BdC9BX42SKE+Eypgwshtgoh3hBCvC6EWK5faxZCPCmE2KAfm0b3IxkM44ARR/wmnbMscuvfZ703jMFdr8cvcuRyuGvu2geOx19U+KWUZ+rHOillfe4PahH1L5RxjrOklCdIKZ1F168HnpJSzgGeIntZR4NhYpB7618uxuopD9fqKTS4W0B0i0b86cz3FKrNOc4wZ+56jz9UVk+hcYhRpOzsHCHEFCHE25wfKWUXsGAE57wYWKSfLwIWjuAYBsP4ZqSDu+7Uf2P1lKSckg1e0vESHr8+Rq7wZ2Xp5OzbuQE2PZMpu3CAZfWUM4HrIiHEBmAL8CywFbUIO1LKAhWQspDA34QQrwohrtGvTXX2049Tipz3GiHEciHE8o6OjrI+jMEwbthnq8eUZS7JsNM5k0PX6gnnRvx6e+HPF/4Vd8Jdl2WXbLDHgcfv4QfAqcB6vRzj2cCLZR7/DCnlicAHgM8KId5TbsOklLdIKU+SUp7U0tJS7m4Gw/jA/QcfpmVjll4sj1LCXzSds1h1Ti3CoZrC5/AF8vdN9EG4PnvN3QMoq6cc4U9pW8cnhPBJKZ8BTijn4FLKXfpxD/AA8C6gXQgxDUA/mro/honHSLNzzASu8ihl9RSbwFVscNfZ3rF6nCi+lPDHeyHSkB3xWwdOHn85wt8jhKgFngP+IIT4GTDkyIMQokYIUec8B84D3gQeAq7Um10JPDiShhsM4xq3WuMwhd8M7paHG/GXU7JBFC/L7KzA5QsqIQfwh7LP4QuQV6sn3geR+uyZu+Mhq8fDxUAM+BLwOLAJuLCM/aYCLwghVqKKvD0ipXwcuAE4V48bnKt/NxgmFvsa8ZvB3dI4Ilsojz834g9Wl474rRT4gzDlaPVabmlsXwGP34n4vUXasvL4h8rqGfuZu976oIuKbpi/32ZgboHXu1DjBAbDxGWkdfVNHn95WKUifo9h4QtAMKIi/kCk8LaO8B9yrH7NWZxdH9vnzwy2b1+maurHe6F+WvEibUNF/PFe+OuX4ZzvqjuHUaao8Ash+sm+fxH6dwFInctvMBhGQu7i2+ViBnfLo9ysHl8Q/GFVssGxcHK3tVNqu6nHFj6H1+P/3XnqsXaqGtzNsnqG4fHvWApdG+HIC+Dw0Y+TS0X8TwGHAH8G7pFSbh/1sxsMExV3JaZhRu6uRWTSOUtSagKX9y7AF1DRvJUqUZY5qTqFptk55ygxuDvQnm31yByrZ6isnnhffltHkVIzdxcC7wc6gFuFEM8KIT4jhGiuSEsMhomEGdytLKWsHm/k7Q9AIKzEvWgef1pt58st2VDA4w/VZd6PNBQv0jZUxJ/o1+2vzCBvycFdKWWvlPI2VB7+r4HvA5+oSEsMhonESAd3R9phTDRcq2eIIm2+oIrmnYjfSb/0butE/AD/8gRcfmf2OYTH46/2lB7LSuf0CL8/VLzjdr5Xp+5/hYS/5OCuEOJ04Arg3cALwIeklM9XpCUGw0RipIO7Jo+/PByrx04r+8QbrdsFrB6nVo8/lBFdXzDj8TvC/7ZTM4u022lAqKje+T6qmqBHu+LFrJ5AZOiI32F/C78QYivQA9wDXIPO3RdCnAggpXytIi0yGCYCI474TVnmsshKnUyBL1z4PX9AR/xJJc6O8Hu9fyulc/U1ThRvpZToZwm/xwn3Du5619z1h4bO6nHbuv8j/q2oLJ73oyZfCc97EnhfRVpkMEwERhzxm3TOsvD66VZS+fiF3suyemwl9qCE3hdQ34+Vys74ccszy3zh95Z18Hr83lo9JSP+nO+1QoO7RYVfSrmgImc0GAwjF3CTx18eXnFPJ8HR/bY3INadec+vhT/Vm7F6wCP8jscfzOzjrcufK/ze80bqdXVOoatz6vcC4QM64jcYDJXCjfiHm8c/wuJuEw1vpOyIp5Tw2/MgFc285/NaPZayfkB5867Vk3PHIDzmhyv8zlrIXuFvyBzL9pRsCITL9/gLzTweBcqux28wGEYRRyDM4G5lsAsIfyqWLfrg8fKTBSL+oPqeEgPKr3dwonjQwu/MbSW7w3H2cco2OyIerB46q8dt+37O4xdCnKEfw8W2MRgMI8QM7lYWr4A64ulk43hxrB4n4vd5PH5/QO2b7M9fhMWxexzhz7N6hEf4fdrq0R1QsCq7fbtXwiNfgf62/ZbVUyriv0k/vlSRMxsMExlTq6eyFLJ6cqN9UAIfCKvtbVvZMsLnifhTajJV7iIsTppmrsfvnPfICzIppE4tn3QiM3bgFfjubfDKrTDYeUB4/CkhxG3ADCHETblvSik/X5EWGQwHO1KOXMBNVk95ZFk92mLxCr8T5XutHmlrIfdne/yJAQjXZR/fjfhF/uDu4efAR/6Qva1tgUyojB7H83fbou8ynDZ4GQPh/yBwDipt89WKnN1gmIh4/7lHWrJhvMzcXfsItL0JC762f89b0OrxCH+wKpOt4w9lyjL7tOg7kXlyUHUiJa0er/CnMnaRd1unHr8/pD3/QsKfGvuIX0rZCdwjhFgjpVxZkbMbDBMR7z/3wb4C15qHYfOS/S/8hayepFf4q1XpYzerR4uuG/EHlNj3t6nti0b8OVk9Tl0fLz7P4G4grCN+z/fnDCg74wxZn2MMavVouoQQDwgh9ggh2oUQfxJCzKxIawyGiYD3n/tg9/i9i5Xv1/MWyepxCFarR1/O4K7wRPze8gvFhN/nR+Xpe6weXyB/W9vK1Pxx7gAc3BW9CkX8+zmrx8NtqOUSpwMzgIf1awaDYSR4/7lHmsc/XoTfKXkwiuzsiXHGDU+zY2+BwVoHO63q7IOawAWQ8qwp5Qi/U7LBEV13cNevCq45++RZPd50zqGsnkIRfxGr5wDK458ipbxNSpnWP7cDLRVpjcEwEZAjtHr2ZVB4rLDT+Usd7iPbOgfZ2RNjS+dg8Y2sNIS0uBeM+KvUozO4C2oVrqyI31t3J1f4c7N6vFZPEY/fmQiW5/F7rJ4DKOLvEEJ8TAjh1z8fA7oq0hqDYSKQFfGXEHArBQMdmd/3ZVB4rHAsjlEkZSuRTVklrp2dgqCum1MondOJ2B2rByAVVymYwq9er/KUWA7nLDhYKo/fSfV0cDz9dFzdheRF/F6Pf/9k9ZQj/P8CXAa0AbuBf9SvlYXuLFYIIf6qf28WQjwphNigH5uGOobBcFBR7uDua3fAL+Znarx4RWFcRfzpUV0xLK0Fv7TweyN+HTV7B3ed78CxekBV5XRsHl8Aqj0Rf9Gsntx0zmJZPbaynApF/M6YwH7M6hlS+KWU26WUF0kpW6SUU6SUC6WU24Zxji8Aazy/Xw88JaWcg1re8frhNdlgGOeUO7jbv1tlniQH8rcdLzN33SUQR8+ySOuIP2mV6EysdMbOcfP4PVaPO5kqCAFPxC88Hn9Jqycn4qccq0ev6ztOsnpGjM7+uQD4jefli4FF+vkiYGEl22AwHHCUK+COUCUH87cdjYh/09OZtV0rhbuo/CgKvxb8VLpcq8fJ4/eMCaTjWoRzItaUtMgAACAASURBVH6fP5PO6Y34h0znLJHVk5vOWSyr50CK+PeRnwLXAd5vaKqUcjeAfpxSaEchxDVCiOVCiOUdHR2FNjEYxiflRvxORocj/Fkdxj5aJ7Ee+P0lsOqP+3acoXCXQBw9AUvraDk91PiII9aOt58V8cdhylEweU62xy/8yud30jkdilo9/gJWT4l0zuFm9Yw34RdCfBDYI6Uc0axfKeUtUsqTpJQntbSYJCLDQYRdZuTuLAHoWD2jObibHABkZlHvSuEK/zDTVkvgRPwlrR47DVWNSoSd+vvJnIj/6qfhlH/NCG8qmh3xO1ZPsLrAgK2WzmBVfq2ePKtHe/rO4G7JrJ6cv4f0GAm/EOJbnufDqdR5BnCRXsLxHuB9Qog7gXYhxDR9vGnAnmG12GAY72QJeAlBTOfUmBnNwV0n+q1EnvjmJfDG/eq5Y7NUIOIvbfWkM5k5jvCnYrjllL2f28n3Ryq/3inb4Fg9uTYPZCL+UA098TTdgwkt2jJ/cNct0pZU4wkls3rGOOIXQlwnhDgNlcXjUHalTinl16WUM6WUs4CPAE9LKT+Gmgx2pd7sSuDBYbfaYBjP2GVaPbke/2gO7rrCH9+34xRi2f/BM/+lnlfA409ZZaRzWimVsVPVDNG9escYVE/KPHfIWlbRD8dcAu88X9kywZp8mwcywh+sorU7wZ7eWOYz5t4dCKGtnoQn4vcO7paq1bOfl14E1gGXAu8QQjyPysyZJIQ4Qkq5bh/OeQNwrxDiU8B2fQ6DYeLgjfJLCbjr8Q/kb7uvEb8j+JWIKJMDnkwkx+oZPQGzysrj1xF/dbMn4h+E2qkQ7czuiLzWjM8PZ30983tVU35GD3iEv5q0nSCAnfmMBa0ebzqnL1vghVDWUm7E77xWAUoJfzfwDWCB/jkKtfD69Vr8Ty/3JFLKJcAS/bwLOHtErTUYDgbKHtzV4pyshNUTzT7HaJIcVKWMoSLC7wj+kB6/49N3b9U7xqBGR/yBqsy2uRG/l+omCJWweoLVpGU3QsrMZy1WndNKZJZdzO3wnXpB3r+HcH0mFXWUKSX85wPfAQ4DfgKsBAallJ+sSEsMholCuZaNK/wFrJ59HdxN6WNXwuNPDqro2rvO7Kh6/MOxeppg1wrdrqjqCC74CbzdE7d6hT9Uk32cs76Z/b6D00GEqklLNXNXWik1glAondNO6yJtYSCR//359TKP3r+HSD0MVGYItFRZ5m8ACCFWAncC84AWIcQLQLeU8sKKtMhgONjJivhLiJcjzgWtnn1M50xX0ON37lCSAx6Pf/SyehyrJ12W1dMEMcfjj6rZvCd/KnvbgEfYvbn7AEd8oPDxs6we8CFJJBNEIL8ss/BnvstAKF/gwRPx22qxlnRcRfy9rcU/4z5QTjrnE1LKV6SUtwCtUsozARP1GwwjxSv25Ub8ax+Fn80tb79yqGRWj+6o3vsfD1Ukjz/llmwo0vlJmcmnr2pW1zEZVcIfrMrf3hvRVzXnv18Ip9ZPsIqULfBhk0w6g7sFrB6no3Vq9Ug7u/P2lIZOh1RdoGiwSZe8GP3yHOWUbLjO8+sn9Gudo94Sg2GiMFyPPxWFro05xxitdM4KefxA0Ip60jlHf+ZusljE79bh8RRai3Vr4a/J3947GFtVZukwp0MLVpO2JQJJKqk70UJWj3O9nVo9kJ/Zo7N6Opvn84nkV9nVeKJ6rwIDvMOawGVW4jIYRoFy6/GnPBO4cv/5RyurZ7QjfivtDkjWEq/I4K7r8RfL43cHWT1lF2J71fUcKuLPtXqKNkJfv2A1KQkCSSKhr2VeVo/IFn5n8pf378AXdCP+lPSxxJ5HzHaKt42x8BsMhlGg3Hr83pINuf/8+zy462T1jLLwe+rh1IpYhWr1DFGd082n98y+3bNWXcOqxvzt/Z55qeVaPc51C1WTtgQ+JKlUMnNeL8Kfud5+b8SfM4lLR/wpqWykqKW3q0AuvxF+g2F/U249frdkQ3T0I343q2eUrR5PWYQaYhWq1eNk9RTx+L359E4E//It6vGIC/K3H4nVo6+bHagiJdXgbjJZRPizrJ5QZoJXbr0eXY8/ZTvCr+XZRPwGwz5iW/DqorFZB9bbBodiEb+VzohmcjC/Zss+Du5u3KULH452xO8R/jrhmc06qlaPk8c/hMfvLbTW+jJMPxFa3pm//YisHnXdUr4IthQIIUmnMlbPzp4YT77Vrn4XvozwlxHxJ/XHGkg7wj/6A/BG+A0Ti9bl8PDnYesLY9eGcgZ3vZF4ciD7n1/49ymds3MgwdJ1rfnnGQ2yIv4KefzWEOmcToTsD0JNCzS/Q4nvKf9aeHuv8EcKWEGF0EKeEBHS+JFSkExlLKY7XtrKZ/7wKlJKLfT6+3Kqc0J+vR5dnTOpI/5BV/hHP0gpNYHLYDj4cDzoSmSzlEs5Eb83Es/1+P3BffL4BxNpImL0J1YBWcJfj6caZkVq9RTp/JzvNlClrtXnV5Q+oLe2Tm4OflHUueOE+Hr6agD+5PH4e6MpUpYkZUlCwhNfl8rqSapJb0lLCX9/ylg9BsPo4FgmlchfB1j+O1jzcOltyor4nSJiQg0Meq0ef2ifPP5o0iKC/vyj2QG++WdY+iv310bhEf5yxatrEzzylZIdWyA1wA2BWwikiiwioz/TvSv3sLljYOhzOjn5wKK/b+WpNe0lN39k1W73eVxE3Ofu4K4/SF9cdXTxtJXJ4gGdx+9k9XgyuvyZrJ6E/mr707pdRvgNhn2kksXJAP76Jfjjx0pvM5yIv6ox3+rxBfZJ+GMpiwgV6ADv/ySs/av7a6PwiG659fjXPw6v3FpyxuqM2Ft8JLCEOfE3Cm+gB64fW9vLVYuWl3dezW9e2Mz9r5aeLfvZu15zn8dkJiPIciP+IP1x9XnjSSu7/k8glKkTlFsh1EqBbZPQEX+fE/FXoCa/EX7DxMIR/EoJfzmUE/E7olA9SVs9OdUk92FwN5b0Cn98VBdCd7CkoAGv8Jd5vZ0SysnikbpPd95V6dIRf4JM5F0u8ZRNPFX82jrlIhyiMpMRlHY9fj99sZR7PEROxB9pUM8TnvZ7I359+j7nkpmI32DYR5wIt1JWTzl4UziLWj26fdWTlCXgXSnLFySZSnP+T5+jJzp8UYglLaqE3k/ao1pHx6GTBhrECDx+p4Ry7spgXZvgV6fDQAd+W12baquY8KtOMy5DRJPD6yDjKUuJdRG2dA5m/T7ojfjTGavHjfjTVtYYwr/98U23JAPx3syBPNU5nYi/J2k8foNhdHAskwMl4i9q9eiI30lHjPdk3vMHSKZSrG3rZ8X2nvx9hyDL6oGKDHS3yyYavIO75WamOAXVEjkRf9sbsGc1dK7Hr9tbW1T41XecIFi+8P/TvfBvLxFPWcRKRPxr27LPOWhnMoLS6YzV49xpxJJWVsT/2s4Y/bJa/RLPifh18ba4Pv2WRD2c9jlomFneZxgGRvgNE4v0AWD1eCPsIhG/nVTiFg8UiA59QWx917CmrYj4lSDL6oGhPeTOjXrma/m2yV5ZnzO4W+a+rtWTE/E72ULJAQK2Fn7p+exeEdU2WZyMKA8m0th2YUsrmkyTOuxcrJajSFmypNWzZneO8FuZaN5KZ9I5+2I64k9le/xJAsQDemEXz3eaJOBG/K7wpycRf9/3oeWIou0ZKUb4DRMLt0ZNhYW/lNCVMbj79JvbALhvtY58s2yBINIR/t3DXyw9lrKIiGFE/HdcBL86BZ74Runtqie7TweIjMzjdyP+IsKf6Mfv1AKy9TZ9u+DHs2HL8+p3N+JXwp+ybM780dPcu3xHwVMu/OWL/HTxelfwEyXW8l3fnn0nEvNsa2vhT0ifO7kslsrO6kkQIubLFv6lm7u4a3kbdjqpI35BXUSllTqW0WhjhN8wsXAHdyvg8XsHSXOFK2s7LfbCX7Rkw8CgEro9accWyBF+fYzcCLQcojrijwl97KGE/4L/gebDlM9eCk/pgzhhwsJ7Z1OmgMW0dZVr9aQywh/UEX+dV/jtNHSsVb9rmyyhB17742m6oyk25/jzAIm0xfr2Ada3D7gWT6yEPZQ7puJs6xMo4Qb6PX2+d3DXwk+UMDGfvu56cHdzxyBJ6Xe/h7QtmN5QpdtemRnmRvgNE4t0BT1+r7jFS3jvbtngUOGIP7qXaT2vAtCDjg496ZsDKeF2GJs7BkpaE4WIpSyqSDLoRJ5DDXQf8QGYPAcGh1gNynOchMypULmvWT1ZVo86Tz392e85q1XpdjhWz2BCfS/dg/lt2N2jxLZzIOFex3i6+PXsj6eZXJuxkF7fob7nhqoglk5ZHfCcJpHOWD2D/jpAELcEhOqwoj0s29xFdzRJCj8+HYxY+JjWqOYH9I23iF8IERFCvCyEWCmEWC2E+J5+vVkI8aQQYoN+LLMqksEwCjgCVAmrxyug3gg9F0fE/aHCHv/DX+CUrr8A0CvzF/p+a08M204TCfqwJezYGx1WM+PJNBGSDAhH+MsY3K1pGXoZQH1tB0QNSZGzXGE5Hn864Ynsc+5kkvkRf70cyH5vQE+8csopoDqfPf3qe+mO5rehtVtt29GfEf5SEX9fLMWMxipetw8D4K96MldNOIDUf1N9nj+tWDKT1dMn1HhNIm1BpIGd7e1cfstS3mjtJeUpomDjY/ZktW7Apj1lTEAbAZWM+BPA+6SUc4ETgPOFEKcC1wNPSSnnAE/p3w2G/YM7gasCVo83qo2XsGC8C4UUskD6MzNDe8hfOCQt/fiQvK1ZWQaDw0xZTCRi+ISkX9Tkt7sYtVNgsHOIaqIJOP3zXFJ7JzIQyX6vHOF3on3It3qcO4DEAEGp2tuYG/EPZgrPSYQaMAVau1XH2BvL/5w7e9R7KuJXny2RtlWNnQL0xdPMaKriQ8nv8Y74ne7r4YAPqSP+viyrJ5PV04tatD2RstV6utrWWtPWR1JmhN/Cxymzm5nZVMVfXt9ZsB37SsWEXyqcby+ofyRwMbBIv74IWFipNhgOPrZ0DnJfkUG6sihk9UgJL96UySEfKVnCXyri91g9u19XpQ40G/cM0JbI5Ib3FIj402QLf6kI1cW24KkfwMNf5ILtNwLQyzAi/tqpqt0xjzhvWJwZULVtlasfqmEwhVo3Nuv8Wg33rIEVd1IQ7/XPG9x11vHtd62eGpHgV4tXk4jpTtaJ+NMxLF8Y1NLnblRfKOLfqd+Lp2w6BzxWVYEBXsuWDCTSzGisQuLD9shnJOjH1p1bXyLTacTTtmv1OJ24E/H7k6rd2/dGsyJ+Cx+14SCXzJvBixs7ae8b/XTbinr8Qgi/EOJ1YA/wpJRyGTBVSrkbQD9OKbLvNUKI5UKI5R0dHZVspmEccc8r27n+z0Wm6pdDIaunayM8+W1Y9/i+Nc5r9eRaFV6ciD8Qgr2b4bHM6qZ3LdtOV7vq2F6Uc+mnOm/3JAF82Mxs0sKfKsMH3rUCnr8RVj/AEf0vsUs2s0ocld/uYtS0qMcBTx2bP3wYFn1QPXfuoPwh4ikLXzA34tfX+8Wb4KFrC98BeDuVYh5/op+QzAjhbYtXsMUpMT2Qifgtz+IqjvAXmuzW2pMpm7CjO/O8UGc6oP32qfURfLqMTnXIzxXvOlRH/Ooz9XoupzePf6+tOtp4yoZIA4GU+oxSkmf11EYCfOjEmdSGA6xrG37m1lBUVPillJaU8gRgJvAuIcSxw9j3FinlSVLKk1paWirXSMO4Ipa0sGxZvCTvULgRv+e/0ymPkMrP+hgWXjErN+KHrOi2O5pksujlEf/ZfKvu+8RkKG/3NIGciL+Ma9GmO8t/fZYvzryX0xO/YCnH6QOWGfFDxufPtUKc6xoIE01a+EI5Sxw6tXra31BjHH278s/hWD3h+pJWT0hmBLxJDCCdbQf3aBWNkfZlrptj9fREU3kWTmt3zBXxVs9YSaEBXmdSVkNVkPoqNX7wXx86jh9ecjyRoN/9/rMj/kw6Z6flifjD9YTSme89P+L3M3tyDcu/dS7veefo699+yeqRUvYAS4DzgXYhxDQA/TjEiJHBkMGJxIouwjEUbjqnR6TdRc1j+duX4IbH1mYW24DszqQMj7/XLcIVd9vTMxhnEn1sT9UxuTaUNQnJbS4+/NgZ4S8nq6f9TQjVQePb3dmsA87ko9yI/9Gvwsansl66baXqFP/28ir+/Fprvi2mr6v0h4ilLPy5wr/uEVXArmMdAKvXrObruXdu0S712Pg2dcfUtQnu/id2tbezebeO5pMDhGSmvX8OfYfq3g36c8Thjouh7Q3SvvyIP62tGi87u2PMmaK89x3dHuEvULbBEf66SNDNs5/ZpD5nOOBD2mk1eSuRxu9TufjxpOV+t3u08DsRfyid6dxSeCaCaasHIBSojERXMqunRQjRqJ9XAecAa4GHgCv1ZlcCD1aqDYaDj6gWuVR6hIXFCi0y7ryWHF52zN0vb+dvq9s8xykzq0cLf088P+8/PbiXgLBpt+qZXBsmRjhvd58vQMAnOW6mKvYVS5Zh9bS9CVOPASHc7JX+dCC/3emkWqZwfbbtdftKdW3WbdqsyhL3bM8+vrMilc6kCYS9FpUOqZf/zu0gNm1cw90vb89ORd30lLKUJh2uIvzXFsG6R9j75E9Ix/rd6xSWCVbYh/O6fRi1Is6UzqWZY2x5Fna/TlrkR/ygon6HwUSaXb0xTp6tEgt37C1t9TizceurAtRH1OecoYXfjfh9Adr7EkyqCVEd8iuR138LPTLb449Y/Th1/VMy2+qpCXsqelaASkb804BnhBCrgFdQHv9fgRuAc4UQG4Bz9e8GQ1nE9T9kwhphdcpCJRuc9WdTwxP+ZNp2OyJ1zOFYPYKk9Pz7aeEPRNUNcIdspKUu7KYkeqmPCPxIasNKLIaM+G0b2lfDIcdmbd/vRvweq0dnxljRzDwEKSWt8SBJglSnulTU3JszwK6F30njDIY9EX8wJ/oHqqLK6nGFOLoX1j8Bx12qSlEnBiCoOo9Jbc9RLXTnlOgnRJJu0cjVyS+rY6Xy50wkRRi/T9BYHcxasKXb4/Ova+9HSjjz8Mn4RHYHEUtZJNJWljXkTKaq1xF/wCeYUqfGMsIBn8rQ8gXZ2R1jZlMVkaBfWT06e6dbqjuLuM7q8WNTjdNhZls9NaHKrpFVyayeVVLKeVLK46WUx0opv69f75JSni2lnKMf9w51rIOaX5wMf7pqrFuxX3nlpx9h6c1FlsEbgpN6Hueh0DdJ6qyLxW+1874bl7i/D0mhIm3pIsJvpeEX74K3Hip4qKRlE/VaB1aBiP+vX4KHv5C9o61yu51FtdXB1G1/MNYJQKdsoLEqiN/nIyVyon6fmvgVCfo5WazlY8+9Tw0SazZ1DDDr+kdYuWYt3PhOFb0n+1XEDxmrJ50v/Kl+ZV09vXIjv3xmI6BSGC0bukUjjXY313V+I2vNgatuW+Z+9oQWsFDEE/F7hd8fhupJ1MaU8Fur7oOfn6QWr7GSXPDsDFKBGtUR6vGEaQNvMVOo60Kin4hMkPaF6dHpkQASz7UEUiJEyO+jpTb72nkjfmfW8zHTG2iuCWVl/Xzitpc54luPc939qwD48h9f55rfq0l19ZEgzTUhZjZV4dcDBJGgX1k9/gA7e2LMaKomEvCrOwcn4tdZVM+s28MNz6iUXaeQnVf4A34/Pl/25xltzMzdsaZzPbxx31i3Yr8yue8tmrteG3rDAhyaWM/xvi2k9ODb2rY+NncOZkVyJSlUlrmY8Md7oHMd7Hkr/zCWjWXL7Bx6524iUAVRLVStr2RSHh2kms2Z5VYlBrBsSXVK+dwdNFATDlAd8pNy/Opzf8APar5JV/hQAPwCrgwupjrdAyv+4B7q1W3Kf3/hxedVFs66R9QbjW9TH8uZqOTYSJ7lEt9avx6ASf4Ydy7dhm1LerUgtln1tNDL/FT2d/fSuh2Z+jjasghXeYTfqU4572Nwxd3QNJuGhBK+4JbF0LUBWl8mLX2skW8nSrUqu1BoADg5QJgkaV+YFAEGpIq4d1YfyeeTnyUaUGvmJkWIUMDHZC38ziJb3r+Ttbv7qQsHmNlU5W7n4NTIefD1XWzc08+DKzNtqa8K8OVzj+Anl5/gvtZYHeLx5AnY7/4qu3tjzGisIhLyq3ROneHVp6tyvr6jh3VR1QkcIlTcK5wa/UAwmH+XN9oY4T9Q2Ic1VMs6dm9lJoKMhJCMU5cuP2c+lrTYs7cb+tsI6wGxVEL5sa5tUW5Nk0ILsRQb3E3kTBDy4AwuR73+unPMhpmZtMdot1pNyrbp7+mip7ONZCqN9PndfHTnXH19fcwVqh5Op2ygOuSnJhQgqSP+bup4ipMJ+HWkHuvmHN8r6vmqe93JVZGgn5liD+k+1YbkThW1UjNFt9nCJ1TtGBmszsoqemOdivIPq7PY3RtnqS4pALBHNtAi8m2VWmLuZ+/SlzIc8Uw8c7J13n4mHH42NB5KU0q1LdKl6+u0vcle6rHxEffpO4S9m9VC6Zq0LwxWkjqipP1K8J0oek88wEP2GfSHVfZRkiChgI+WOnXtptXr7aMp2nrjxFMWa3b3ceS0OoQQ7nZe3j1nMknL5nN3rchagKU2HODwKbWc+LZM0YGWujDPpI9jwzs+TsqSzGiqIhLwqU5WWz19Oo9fStgpVUG7GfpOprohU+AuGKj8UuhG+A8UhiqAtS+8+Wf4+YmlM032I2GZoEn2uBUmh+KXz2xk18//Af7nCKpsR/iVwji2RW+szJomhZZeTBUZ3HVSCAtk+zjWUla9d8fqaZiZySmP7VWvD3aw9GcfZd1NF7N04x5iaUnY9hw32Q/P/ZhPBp6gV1bTRzXVIRXxO775Dx7fxJ7+BIGAFv7NzxAhxUtNF0Pvdtj+dwCC7a/zQviLHN//HAB2+xq1fe1UpJTEUhaN1eqYdrDG/ZxSSvbsVt59vYhSFfTz5Jp2V/g7ZAOzhWcwW1MrYu51/eETynKqrvYIv5O+2qjuVGiazWRrD030UdOnOhrZsZYOqaLeZbvV9rJ7C/Yhc93DDAQnAVAnYm6evjPBrSulouQuS0XV23olIX8m4p/RVEXAJ1i9q5dTf/gU33t4NRv2DHDEIcouyrWEAN77zhaOPKSOtW3qzsAh4M+XTad+z+s7VEAzs6mKqpBfCf9hC1TbZMaacoR/TngvPgFTpk5z3wuFTMR/cOPNKW5/s3Ln6d+l/jFLTSraj0RkgohI0d9XXtS/qzfGCVLZLfW28kvTSWfG5TAj/nSBCVzFrB4nEi4l/IkCVk/DTDUnILo3c8ye7Rxrr2O2aKMvGidl+6jGM6iaGIDODWy3W7go+R+AoCbspzrsJ6EtmajlJ5q0CDgRoY6kF9d/CEK1sPIeAOo6VgAw31bpkhGRUh549SRdjgAadR66Hax1P2dPNEWTrb4TkehjRlMVbb1xevUygp00EBHqufX+H7H4yB8AUEOclF4/IGqr41ZV5884pkEL/5EXECTNtYG/4JeqwxbpOJ1a+F+LqjsTYSUZDLeQ1oPgvf5J7qHien5Dtxb+KCqi3x5Xj1EZIBz0MblObddQFWTBES3cu1ytp/vK1m56YylaatX2k+uyLSFQUfwdn3oXd111Cg9+7oz8z+PB6Ticom0zG6uIBLTwX/C/PHbuYmJE3DGBQarolrWc2jzIM19ZwMwZM9xjBQNG+A9uPFHn3s0e33T3KjW1vUx298Z4aVNX8Q2cSDY18qnfj7/ZlpfitvKZ++jtyo8ASyFtmyoteL0dpRe1dhj0iPo0W1kElhYaGethgW9FwSqGibTFI6t2Z0/asQpM4Coq/E7EX6icrxL+wYJWjxY4nbMOwO7XmSb20kwfg7EYFoIaPB1Koh9/fyub5HS2yUMAqAoFqA4F3Fx+p/ZM0In4dUfeYdfB0RfDWw9CKkZ9j7JP6kXm8wwGGvnr6j106QqVDdVa+EO17ufsHEgwWfS6x/5nHuGQrqVuVcsO2egeL9Z8BKsHVdGxk3zrSW5bntXGcCQ/k4d6LW4z5rONaXzC/0TW250o4V8em4YtlUC+3BGkW9s53b6MtdJvqfY7Vk9UhplcG6YjrXPlZfbgbk04wIfmZVayatKfv6lGPTrbOWmaoIR/Sl2E0w+fzDtaCnRkHhyryFkRbUZTFZGgjx17Yyxe302nr0WfN5NmulNOZlJ6D2+fVENNbeZuIBjMn7sx2hjhH0s82RRb16/KvP7XL8ET3yz7ML96ZhNXLXqlaGEpV9BGuMTepo4BPn3nqzy8KjPA1dXRxnFLruax3//3sI6VTMbxC9XO/s4Cg3cFiEQznYvjMTsR/4ldj3B76L+J93bm7ffbF7bw2bte49E3vLn2w/D4k8Ujfkf4Y0lPyp/TmWhLw/Z23jovPiBsGuxelbLnifjj0V7CAzvZKScT9CvRqwn5qQ75ibnCr3PknYjQidStEBx1keoIdq2geWB9XnvbrHo+d9cKvqEnTTnlHtKBjNXT0Z9gMpm7wn/uu5Wj+19ys12ciByg39/E8t3qGv578PfUvKT+Dpz00yyP/+iFKpvHr+9UhOBO+1x8QrJTTiKq1611OpbNvZKtUnn1j2626NPZO20iM4N1+mTVCUT9qk1RIrxzaq3bSSQJEA743Ei+Jhzg7KOmcGiz6pC2dKr/iQZ95+PcGTRWe4Q/x/459R3NnDyrcDFhx1Ja397P5NoQ1aEAVSE/sZTFVXcs58k1ewgHfEzxjCXslJNpTLXp82bEPmysnoMcTwQeTHnqcQzuGVbBsO17owwmrbxZiZnz7Jvwb9dT2dt7M/vv3bkRn5DI6PCycePRTPQc6y7vbqEluiHvNVsLfzitIlS7P/9Yjh2zaqdnQNIR53QZefylBnf1sdO2zMwi9lo9wJ5NKzM7bFzsPp0qurHwux0gQOu2LYRTPeyULa4oV4cC1IQCrn3iTPLJRPz9WPjoS/oy/nnfLqbEM6mdDrvSDSjMjAAAIABJREFUKjp/dn0HVUE/5x+j7irSgdrMncNAImvw1ofNplRzxurxCP/iHbB9MH+SkRPxR7xZPR/8X/h2ZoJ+2rK5NXk+R8Rv5z2Jn7JHC34Xqo3RpMVaqTKQOmlg1tvU8zXicPcYkxob2HrDBTROUrbQIGEOn1LrlrEOkVaDu1qQ68IBIkE/z1/3Ps47eqpbkM2JwCcXiPhzM33uueY07vv06XmfGVQHEvQLbAlHHlKf9/5z6zs475hD3Bm/oIS/PtEGUjKjMXOHFAqawd0Dh+QgPP51NUCaTqqIvJDoSQnP/FBlJDz732qt0lyW3QI7Xs4S4rDlqU0S68kvUlWCus4VfMr/KJ0DhVMaBweUgMlhTlBycKa8e6sXDrSrwehJvuGVOYhHMxFlui9Tfpj1T7Dykf/j079/lTuXbsvaZ3oif+C7f+trvLToGwT0Z5IFasU70Zxbr962C6/AVWTmrtTCH4/lfxdJy+Zj/ic5Uaznty9s4aVNXazapgd0tfD3ta4GMimODlNEN5bnX8/CR6pNbbuLya4I1IRVxB/Vi5o4oupm9ST6SfiqVMqgzthh+1JCMumOCzg4NgrA+cce4lodqUC1x+pRdYLsmqnutltSzezujVEV9NOhj5GUfn77yl5EuI5ckrqtYc/g7tKtvfzm+c28snUvtz63mUE9LpIghIXfbZu//hB3nzW2EntZMwV/jfL2B1KZjtIOqGuUCqkIPCGqOLSpml6dORMRiaysnhrP4Kw3qneE39muKpjpzJy/n3Lw+QSTatQxjtQDxm/s7M069iXzZqgZvpqdcjIhKwqxbmY0VhF3rl3IWD0HDqv+CEt/Bc/9WBW8eukXsHlJ/nYD7fDsDfDq7fDMf8DKu7PflxKe/Hd4bRHSYyFUOcJvpVQEllukqghSSj4w8Be+HriLjt7CRcbau1QH1dM3skUddrrCn+lYEp1KnBuGKfxJj4ja/R6xvusy5r5yHY+vbuPW57Mj1qZUvqi3bPoTp235JTUpZfH4CqwO5WTcbOvSgu6Ivj+sZlk6WUVFrJ7BfvWPO9CfPyieTNt8JXAv/xR4mh8/vo47XtrK0g3auqqbDgiaBlWHdad1Ln31c3hUqmjxEPZie2btDoRamJ7YotpcPcP136u01TNgZ1s9oWDG6kn6qlRKa3WzypffpjJ71oRVAbYefzOgovU5U2o564gWPnXmbMJBdf6kP2P1iLY3aBSDiLef5ratVbawYc8Ab59U7Ub8nTSwdW+MEw47NO+6OFZPVaQKW0+q+tHfNvKfj67h0l+/xH8+usateePgHDfcME3NgAUetU/hSWs+6abD4Lz/YG3tKTyTnku6To8T6OqfVkSPO4SqaakLM6jz+qtJEA74mVwbZuEJ0zlzTiZd0uuzO52AE9071wUY9iQqxy46apqK+L/xgaO4+ITp/O9lJ/CBYw/h3XMmu58PYJOcRm/9ERDvpSYcoN/ptCo8axeM8JePU19875ZMNF4oKnfuApz0zNyp7Yl+NTkl3kcirlf/kQ1USy3ajsVTas1WD3sHk7yTbQSEzUBn4Tr1QgtaKj6yiH9nT2aVIhf9uWrk8CpaJqKZa+aP5pfbnh6K5i0lGLSidMrs2+dqvd5qQ0odw5nx6sUZjN7SOYhty0yU70SqbolmR/izP0tUZx350oWzeqpIuAuKd/QnSCcTKpIPRrCrJ9GiJ6X/VF7OL4/6Pd9IfUp9biGzIn6qmmkQ6tx1U2dTr+2AmlCA6nCAAV1Txx3cdayARD9Jf7Xq4Hx+VeemQ40rbK6ZB0DjLPXYIRuY//Ymbvvkuzh2RgNhbRcl/TXu39phux8mRQAx3ymlBa1yMps7BplaH8EO1RIj5HrxUydl7iLc66KFPxjwu8/fbItmJbBt6sj+v3HSOKuap7llKDbJGVyd+n+0NDdC82zuP/KntCUCpBpmq+9Er2plR1TE7wvV0lIXdsdDqkgS8vvw+wQ//ci8rJz7xgLC31Qdwu8TWRH5cHFsJUf4Tz98Mj/7yDzOnDOZmz82n4Dfl3X8JfY8lr3/IWhWnynqU8JvIv5KsHEx3P1P+WVlh8KJwHtbM15wIXHWwi31FPrethy/1VklKNFHMq7+2TtppC5H+GVyoKw27urs5h1CRZrJzq0FtxFauFLDLELm0NPVzr2h79HUu9p9LTygJoSpQlPAvR9XtVYA7rpcTShy+MtnYenNAKTjGXENxfPF+pOHD7iC/cpffsGymz5OyI7RIRuyprXXS3XeqahjhBP5xxpMppkjWvkTX6Vt3cuqNAB4hF93BI7Hb6d5c0fmOLFBFfEHrBgrfvwPrHw6M8M6lUoQFmmahPq72Nw5iE+mSEk/UkoSIWVPWP4wM6dM5o3WXnrsSGYtWl/mXy9Uo5fkkwGmzXw79ZEgQijboSbkL5DV40T8faT9VZn6RZFJIG1iMkRH3dFqG12fp1M2uIIEuJHnn97scauDHt+zmOXBk6Fplmp7sMa1ThqrgzRVh+n1NbkRujMW4SXp+Y6STmVRn9+1P4C8+vIDQXVXUjdpepYlA7i2V10kSDRpEZusPk/YUn9HskrtK8JK+Pt0IbQYoaKVLR2xD/iE29H4fYJJNaEsq2e4tNSFCfgEh08pngEUzmlTtSe6j+k1kPfH4G7l7ykONLY8r6awx3ugahjL/TqLRPS2ehaFKBDxO9tp4Re5VQydGZ3xXhI6Au8PNBOythGPDRLRdwwCqc4TLp1G1rd9lTtIaHdvL7iNP63Ok4oPz5ZxOK57Me/yrWN59CVA1RWqS6jB1LA1qOyptx5UaYyHvktlsKx/HI6/DDo3wut3qmj05KtJJdS1G5QRqlP5YySHWVuJpZTHLDY9zTv7lvEms4gSoVvWMkUPPjbqSHsqqqOsSuYfK5qwuC5wD0f5thP92xcyi4W7wq8tB89Yy8vrWzn2UGULJPV4RI3Vy7zoi7y09lB436V6F3VNm/Tyf3sHk4QDKZIEGRhM0l17LHN61iH8YWY0Rljb1g8IYoF6wlYXmwOH8YXoZ3ngihmEVyk78DX7nRw1vZGjptUza3INPp/guJmNvKkFtKWhjm09EAp6Bnf9DURTKrOoNVnHYcAuOYnOSfPhHd+EU/4VO1THUYlzuPiE6e7ndCL+QfSg4p63aLL2sr7xFE7T9oldPxP6ld0x79BG3n/MIexp+xa/elp1jk5lSi9pT3nhpAhh24LDpzbww0uO45FVu7j1+S2u8N946VxiyTSvrUrzne1VnD51OrXh7Lsu5xz1VUqqNh59LS+80slRb7tQfceTjuW/U5fR2nQah7fUcva5H+Qva9r4wY65nFVE+J3xjcbqEMKTuP+dC49hemOEC46fRnPN8KPuK0+fxbtmTypZStmJ+Burg/REU1R7qnAmArVgQa2vzDkp+8BBHfH3x1Ps7s1N0dNiXWTh6GSiSOaLY+FEO6F7q944E7m4RcIc4dZiUp/uyq4L45w33udaL1EdHQ70dmWvQpRzR2FbFnbak7kjJb4dy9xfA/2F8+L9lmqLlRy+8CfSFuemngHgMHura8O0WEr4q+0BtyOUiX5VBRKUjw6wSk0qYrADNj3tRvzt/kOoT+vP6rk+02IbSVmSlGUTSA9SQ4xaEWdQRtyytoA7kcjp9GpTXXT0xemPZr6/wWSa6ULNb4iFmjMfyhF+t25PZp/OvZlsKlvPdPajvlsR71HlL6TE0p+5UWQ6/xBpEgTY2jnIE4GzAPAl+6iPBNnTp87VaKn2LKs7j63hI/Addwk+ne//Z/tMjppWz2EttVzxLjW4ecZhk9zJSrOmKkEOeaweK1iNZUva+xKs6Vci2SpbqKmugvdeB5EGfO/9KtecNy/L4nDEqV8Lf2Lzi+r3hiPUQiiAv/nt7vYXzp3OPxw3jSPP+hivyiOAwhE/nmJpKREihZ+ZTVWccGgj/+88td+6dvV3feyMev75tFlEWmazyHo/M5uq3Ag8pGfHOudwsm029djcZF2CL6T+viKRCL+0FlJd14DPJ/js++awac6/0EUDwQIzbCFj9TRVZ0fWFxw/jXlva+L9xxzCybOaC+1akmOmN/CP82eW3MaJ+J21FLxVOFNB9Xfp2w8TLQ9q4f/hY2u58OcvZL/oiGkB4V/xxCKs/zqUrvZ8Ae3v9iy4sfPVrGN1DyZ557ce43cvbMkWbo3V4zmeK/y9JBM6Eq9S+cnRvr2kBjyWRc4Ywsu//lfe+u9zMi8881+ctuFGBmQVPaKByGDhejxBXRrAGoHVs2fHJub5NpLGz5FiOx39CbraW2lgkKT0UyMHlS0FbN3Vruq+A9Tr6HL1AzDr3RBphDUPYumIvzcyg2anbIMnO+qQAdVxxFIWIWuQkLBopJ+Uv4pu8rNIHKpTe/nqDTfCj2bR26PFPmkxTRfBCg94xj/yrJ5Mh7i3x1NOOeeOLpDoVqUvlt6Mpb+7RgYQumMIkib1/9s78/jIjuref+t239731q7RjGbRbB6PZ8Zje7zbjDG2A9gmgCHsEMgDh0CAJOSR8DFkJSSQTx7k8chCCOElvAAhhgQePIfVGIzHHnvMyNizbxqN1m5J3eq13h9Vt2+3tlk8skZSfT8ffdS6fft2VV/1r06dc+oUXl75mUf482fc2WQ04K2lfBb0ADTUej1pvcyfiMrI+YH3OrrTjZurez0WsVQLZWmxoatduYAcH/BkRpVcAHb9yUOcKKnZ4UnZ1JCWOOPnpS1NJxj6ve88SFUKqs2bVb59MImVWls7P6391/XWbH0K4kyUhJ8Kntp5AdtDU8RXs/gd0VuVDuOxBCuSoVodesddskoLZEp/Vs7mLV7LXesAEA+6g5rT99l2aUvULP75d6lMxbH41+kFYfWZQ+WWrQB4w9NjJxebJe3qCfs8tdSxGs6XuX7vUI338c8RFEUOHXyKdGvjyJ3LDGJLW1majvtGX8vZueej39jPW3dPz7/vP/YsHU36SzThCn9ZFxqrhlthGPLZYXIZN/Gums82jMyR0WfoLh1Qvn8hqAw8iwf4Xys/xj39nyZWqEuPrMPWxcAqF7ByN3Oily7geGwHq7M/48nhQU499kXSwJ7Q9Vyb/wH5sSGCQGEio7bWA7Wt4GRW7Wd7xWtVadqJIap+9VkVol34cg+TyQzhnxgkAJyMX0lnZg+bxFEmixWV6gY0iwynQjFGx2av7dMkMmwQJ4iKPEdPHyWeSJMvFEgJJTKhXN2gqC1a19VTQCIQSDIZV/idPVEdkqV+Nds7uQcZ71HdFJIoebKE8YlSLZ3x1Tu7OL71+3Slo8T2uLO0R3b/K7d0h/hAYE0tP577vshI/1H+NrChtqS/ntf92u/Qu+9W3rBzM1vXdBApParbX0R6Xav7hm2b4elvcEI2sTow91c7FrD557fvIvP0KDwB22Qv2VAXr79Jxwbe8G8Q7+Lrl9u0xqbXsQGdIvnux/n0Nx/j/gPvAOCb77mxZmlPYlPGqu1SBWqwcLLDHOv+9btWsXNVknjQJqJF+7XXrGRtU5juJjWwXb+2iU/edwW/+SW1NsKplxPUwl9vvTu58jNtmK7OdRZrzX8QdSoBnTX0K9es5FU7u2iLu3sTX/26B9j38A623nD3vLdjSVv8IZ+XfKmiMjocHPdM5rjrlgDOnDzMZZN7AcidOVw7LqXkyeOjkB/hmNS50mNaYIvuUneAy8QRcsPTV6MOnKjLQ3cGnGoJqQO5Xp2/XJwYpZB1Lf7ceGMlxEhpiAh5yhPqdaNDp3msup7t19/BmL+dVFlfu5hrWD/gqyrBr85g8UspeerE9IqLz+39IbJaJT9wRLVl9e0AFE49TfLAVznoWU2u7WoA8kNKVK3SOPnj6otZzQ3XPt+xxEYKVoBSfoyqtviFDiCOnjlJdli1++ja11IVXu71/Ih8qUKgqtobEZPE4vFaXZaZaCLD5oQS0vy4+nySeXc9gCVdA2A0q8T9FycH1f9GOU/eo2YBmWwGKSVSytrA47CiqgePzHFkndvMcff4RbkW3PztOzbStX4bpNc2LNoRyVXQupm2eKBWIIxoK8l1V9d21JpKKJLg8mtvJ2B7uGZNujZDACh6lKjevL6ZLevVYHRSNs0qevVcuzbNmg4VT2kVo8RXb3d92x3bIdzE5SvitMQCs18kvZbYmp21Pze0RmvW+kTF22DxQ2NcwAnkRvxedmrXSkRb/E1hH9etc1MwfV6Le7evqA1Czmpp5xr1KZpRPXhMzQ5zcDN5XniL34mtNEX8XLs23fCc5fFw+U33Iqz5l+UlLvzqQ27Yochx9fzgL+Czt9Ss9kMP/T2W9heX64KkPz44xN2ffpjKxBBH9TLy2t6g+lqDY0XaGeLrvg8R6nUzP07KJsrSIt/vrjwt1+WuW3oQ8CdVZb7ixAilMVf4J8ZcQZ4sVUhKXd61XwWOC9khxq0IN/Y0MxnuoKU6qGYDP/0MfPZm5cOWEr/e5UeWpuytqvv38k89zP5Trl/x8P6f0fO1l/Lzh79OeeQYFSlIXH4HAJWjP2F9+VkGuu6q1RAfH1AC6y1PIAdUuQCZG65t8P2p/UGeOlOmb3Cwtogs0KJmQBNDJxkf1SmZzT0MtVzLLdZecsUKQekKbzqZ4rBsp09O970OiRReUeXqmBL84pj63VI4Ou1cgC8eVOLxO196jG/s64NygXHL8fvnyeRLHBvOESTvBj8BP9pCHz3eMIi2eNXjsKdK1bJZmQo1rPqsd7s8n6yRGmFX+EPaLfBrN62BpvVIYfGMXDlnZkk9Xe3utUTb1nNuglP6AGBjh+vWqs99H694KeNpiAWsb1Wfczo8c9aN4/6JzuKq+t07N+nX63LLcVX4bHWz6yIL+vSMY5bBz+9Vg5Ezm3gh6UwGCfs8tVISC8WSdvWEtDUwUSy7aWKOq8ex/Mf7kXaItiNf4xl7M02lU3iyrj/4kM45jlSynJRbqUjhLrXXwj8wXmCLdbg2cDhkrCQlESIw7Frf2YGTONLl5LFH0mpRSnlilGpuhIK08YsSk+Ou2+H00CjduujWeP9hUmt34i+NUvKtxPZYyGCKgCiRz40THHxOBSwnBiDcXAtOzlRz5pTO0T8+kmNzh3KBZPuPADA50oc3e4JBkSLapNpo6fx9T6ITodPPCkM6p780QkgUGJERkmKcyUM/ZlKG2T8eZkfJpwKtxRwVKYi1qdzl/GgfUlvWsVQrpUgHCbGPE6UKIZmvxQqDkRh/W7mJf6rcxl7/O7BF3WCeWgNDw6RzakAs5pTwR/XirkESNDHKI5XN/LfSe7nMOsL9PIhNWblbSnkytNLCSUIUODGS5//19vNO8uSD7ZCfki011oenLgC3JVnmsTMQ9lToaUnz7bfc1HB6rM6P6xgjz4uwW7NmZXszT7z0xSTDPqAJ8VsH+RKRc3ZjBCNu4TUuu/ecXtP70TsaqlhuaJs59lLApoynwcr/9VvXcdfl7aRnyZqJ6NmRk8UzlXu2d3Lz+mbdXxX8ffz3Xlxb9AauVV2YY0vKb733xueVs3+h3HFZG9eva6q5uRaKJW3x9wx9l/d6v9xQOrcyOSX3fvwMB/c9Qnf1GJmeVzDsbSGYO0VuIstPv/QxTg0O81bPN4mJHBmijOFaL9WCu3jnCtsN4FYs9U+Yt+MMRtbTlj/gvmasv1aX284r4U+0qHiCnMwg8sOc0LW6J3PaJbHnuzz97X+oXaM4pCzZcCVL2a++uM6y9sxwP4WhI7W+1deZsXIDPPqVTzbUwR8bG+eNnv/L0JhrwTqzjsrkGKH8KYbtVsIRZVnaek9YTzCGN6TT/jLKvdUqlOAe1tUlraM/pLe6iuMjecaqPnyVPKKcZxI/iWbV51Kmn+KYmkHF063gjxFjgrHcJEHhrhT2BiJU8DBBkKJotAYLelGPnVWfS0V/btHyMBU8nPKoQPMoYTJE8No6UClKTBTKlIp5zug67kEt/Pajf41flCDSynQk0ZzrDlwZKPDy4JNsrjyDxw5ME5R6V89FERs7AHq2JXzhmggCEEqdn+/aVzczSK+d/bw6gj5PQz9mCyRP4qOC1eBS8Xos1rdGa8HiqTiCOJvFDzT2FxpEH1w/+lzurmjAnjXrZz6xLHFepSDmrR3zdWEhRJcQ4rtCiF4hxM+FEO/Rx1NCiO8IIZ7Tv88jmf78aBvZw696/rOhdG4x17gJ9sTwKQb2qq3p1t/6esYCHSSLp9n/0P/mmt4/ZtUTH+fD9hcAiCZbGJOhadcaHC+w1evOEo6WlQiXfHHKTZtpY5DMUD9nTh4mVR5gX1XtKuQvDFKUHpLJNGVpIScz2IXR2iYN5Qnl2jnztd/jroN/ULt+deQYlCYJUKitXLQjah4xPjpQi1HkRvooF1zh3zz6Pa7e9wAnDrmxjXTff/FR+/P4Tj5aO1aeUJkwcjJLstTPRLADj9fLhPQTKSrht4MxbC38nvHGAmnj4W5ALdDqlSs5OpxjQvrxSyX8eREgrjNVGO+nMjFEQdqkE3GsYELV6h9uDL77Q1E+dNcm1rdGKNH4xXdWcwqpvujVvLov8eooOTvJhEe1c0RGWJUO8c6XqtrqG8VxfvjsGazyJEeK6pwWMcrBQwe4v/g5ypafQsfVzERywl2Ytz2W5c/5S4IyD56ZNkevc/VcDIsfXHeP73m6KwJxaLkMXvZXz+sye2K38VT0xoZjwbXX8YvAFQ258mfjiq4El3XEaI/PEVc4C+tbo9gewbtftO7sJy9T5nPIKwPvl1JuAnYB9wshNgMfBB6SUvYAD+m/5wUZbiYiJt0yAdWq+nLWMTF8Ct/gfvpoJtncTinSSUt1gNKQEk+75E7p081tZOssfk/Jtfh7OEpZW6LOxs8lX4JQl9pB6MQv9tTiCD9Jqah9oDBIAZ9ali9CWIUs/lKGYY+KDZTzanbSIkZrbqSqFHjHjiN12qgnrATfF1XT/8JoP1EtzpmBk+THp68uHqurg+/Tuf8i47oznIqbcjJDc3WQUkS5efIiSKKsV8pG4tgRNegEJhtF2tvsfuF65UqkVGVz/XISTylHUfixPB6GRQJrYgCRHyYjItheD56QXsU61Fh+wg5GeftNa/j2b95MaYrFL+u25wNUqmy5SkqOMulLMWkr67gaSPH937qVa6+6Cjq28yr7YY4OZPEIySmZZsJOsck6xsApNXN46ppPUN348sb30vc2XWfxbx94EJ/U8ZPhw0yl3uIPXSz3gjMTeb7Cb3ngXT+GujINF8KV7/sKW9//jYZju9/8ALt/96vndZ2rulP8x288PzdMNGDz3B/dxe5NM83WDDCPwi+l7JNSPq4fjwG9QCdwN/B5fdrngXvmqw0iqm58Ketkuyihri2bBwqjp2maOMDpkMqIEMmV+ESZ4BmV4dMp3A1OuprjDa4eu1pgzydeQf7YE7RW+jiRuApwVy+eqYTp2KCOjR19gtYjD/KMdxOBVSoLIlIcpICack6IMJ5illAliwilGCfIpiOf55H/83F3cwzgMB0EJk4xPqIDohE1Owgl1G9xphcvyrU1MXyKXG668NeXQw7qNEd7zE13tPSg4s8cwhYVrKRaTJQTIVI6wOwPxQhG1aDj1MtxCLWvrz12qizmpB8/JezyOAWhrLmsJ8mm7I9YP/ojxi29aMhxH402Cr+oE7iSaLT4rXinu2AMsAoZ8sUKzSLDZKCZkk9ds1bQC2Dra9jIYZrHVG2bAjb9wR62eI6T0fsEhFJtRGN6ILLUfR8NrkQiSOWP6Dfzqm0PnU1GRqcHlOt9/BfN4o9oP7/9wgcoDYufF8TJJYToBrYDPwVapZR9oAYHoGWW17xDCPGYEOKxgYHpxbzOBUsLfzWrhU4L/9fFzfxH+F4GZIzK8FFWVE5QSKlsgVC7Wl3Yk1PC36FrwTwRu5W1u15GzmrMlrgy+xCf9nwCC8mxDW/jC+Xb+KPQb/PjplexdffrSLd1MSEDiKGDrKocZbTtOoIxNzPF2U91whMnUujHRxEZTFHBg48y1+7/Q9LCFe9nveuJFfrIDCurPqA3aY4ktXVz2q0BX8qcbiiK5lDOusIf06UXwnk3DdVTUANNMq/E159UolawQniF3tA7EicYSVCUHqKycXAJt29U7yMt/B2XAe7WeOHyCCVLifSTLffQW+nimeoKHm9SsyBbz2CssSlpsXMIvx2KN6Q4eopjTBTLNIkM5UCasl97E+tLdKxTC+F2WmrVbE9nE03rrmQtx/Hn1WcSTXcSDqt4TDWp3EmDVjODMkZQZ0px/XvVArWX/BHc9hF49ReYimPxCzG9VssFc7FcPYZlybyHloUQEeArwHullNlz9fdJKT8LfBZg586d51lRTWHHlBhKZ+NrHYwtrLiO237l3fT96ZW09z+CR0h8K1QqW/v6nfBfEBYq971dDFO2Amx/39cAKHmjUFai5ohglzVA1teGr+dmfv97Qe7qauO617lT5xErQST7HJaQiFgb4XCUkvRgi0qtkFXOTrF6Uu0ra4VTpIdmWLYdiDPkXUVk/Lv0DSrXTDiuLL9oUv8eUf77ovQgx8/UaslPSD9hocSqOua6ZpJFvQNQ0V385Sspq76lchoEBGK6do0nhJ5MEIomsSybIeK007haOb1KDaKHZDtvvHEDe/5lLxNa+CPlUYa8SrTab3sXr/6bKwH4h91qZuTTFr8/N2UxWl0QsqyFP0uIGDn8kYTKdNEZR3Z5jFyhTBcZjoWakeXGALjqsCopvFYXuLtvVw94/fB4mastlYWVbOlAWKrDwbYNMPRzjhQidMoEzSJDAR/+3b/P2fB7Pfi9qlLk+fi75yRihN9w4cyrxS+EsFGi/0UppePs6xdCtOvn24GZi+ZcBHxxlR+f2vvXPP0nNyN1Cp4vHCcetBkkUfNZN69T7pemti4GcV0CtqhQrdtwwnlcv7EFwP6WO+lKK3HaNGUHnnFvihXFg7pNrSTD/lqsoGzpyosf1krBAAARA0lEQVSBJuJStc+xeuuRCIi0MhlSGSqWtuxjaTW4BYJhctJPV1kNCM+Jbuz8AMW8Cu6O1pU7aDn9fY59dDMjA320VNXH31xxb0OwpCx+Z/ALJdSgUvS6IhOOxIkGvLWSug4l4SeZSDIqw/TKVWxdoT7LnC4NEKuOUtaLjnatTtMRD9Ac9XODXqxjh9X54Slxg3qBcz6zYUu9JhCJN2Tf+MvjFMaH8YsyMtQCIX3tmJsGiddP1pumx9IuLjsIrary443WU6qIXCSu31dAei1VLA5NhmsloifFuedix4L2xcnhd4jqTUv8sbnPMxhmYN4sfqFMm78DeqWUn6h76kHgTcCf6t//Pl9tCGr3x8byM1CGof7jpFErIYUQjHuTUIVjspUVqzfXXtfnX0NTwd383A67LgLpj8MEnJEJ2sQIeRHk6U3vY/NtbyaWCPLxV26dFlTK+dLES/t1mzpIhmyOyVbSYoySFo9KqBldaBJ/rJneO/8V+9lvsO6gCoeUbv9TfO2b8f+sD85AcEjVxEmk3ffKiighCvTRzHiwk87iISYmlcU/ZsVAqkFubUVlpDz9swfZQp689NHKELlCkZDfR7ja6LqJaOGvaOHPST8hj4eAR/npkW5AU4RSCCE4dMNfsG3lplqNlZzeESrEJEXtc7d0rfRKVdaW4FtBNZAky2ogkpYPUS2C7cZWKlr4+ze+kcFwgp2RRM3nnbUS+CvjjGb0wBFtYSR4Ax/a+1Y2t1/Z0K8xfztbS3pf3MQqaOqhLGyayXJCtKmCxF4/vPLvYeW1fPlEgn/cH+F99pcBKIpzzzyJBrxuIb+LweZ71GYy55iCaTDUM58W//XAG4AXCSH26p+7UIL/YiHEc8CL9d/zQsAfYLhumf/gIeW3D8eU8IS8yoP04/BurLqc3onkpobriDqrygqq12aEEqhscAVXvfq3iaXU1PtVO7umlXQtBVwXQ7Spg3jIpreqXA1l7e8WdT7qUKKZTdfcztoXvaV2zLfxdlh9Ey1dKmMmkemlIG0SUbdtEx71+HSoh3KwiXhluFYNM++dXg7APqA2AN/v3YhflDn4xA9UG6uNbqaYdiNVbL0UX9SJcEjPBpyNwCOqrzte/FpWbdhGxO8lYFs1Vw9AJeguxb96dapx6brOT2/Wg1Q12q4/APc+OsIfalvHzl96uzqoLf4Rfwfh6jjHjh0BwBNtJRIK88XKbTTFGqtJ5kIdakaHgJZN4LEZDasMoTFv3axryysg1k529V2cpLk2yymej8UfuMgWvz8CV74ZLpbryLCsmM+snh9JKYWUcquUcpv++U8p5ZCUcreUskf/Pr/dus8DyxIM17ltKn3KSo7GlQU/HlRuk1NdL214nb1S+ZurWugcMQLwaVdENaKm2qOr7jhrO+r3ME00d5IM+eiVquRtXNdzt7VbCiCqZyoisdK9iA7mre5eS1F6CMkcIyLeMGBVPEpcC6lNiGgrUXK1EhEl33ThXznyCADHk7sAuPxbv8y+73+VqHBTXrOE8dpKaKXOIJkU7kpMW9cZynq1eE/Z40AIQVcyRDhS55KIzpFm54tQwaplUwnHpVHn6qnowdL21/m3E6vA8jIa6iYsJ3joUTXIB1MdtOpaM11Tygg72/gN+zpr+x7k9KCf9zXWUQFqxcYc4bfkLJvbz0BbLOBW4zQYFpglvXIXVGDVIZpVdWSScWXNPdz5Nm4vfIzm7ssaXrP9JW/kwL3/gdWxTR2oE36/zl2vrLiGA/f+Jz2v/MhZ22BFlWjnpJ9wNKGEX6c5tpdVHn0w6W40nWjS1n8oRdUOUbVDNWFa0xIji3p8NH1Dw/s4tXx8nZdjp9T1g/17yEk/Hp8SvfpU1qDMc0qmsG94N/te9DmyhJGPfAqAklTW6ZhwLW2p4xuTHldwI2k1eA4yQ+aM5nNvuYq377689rd3LuG3LHJOjXhpY4XTgFA+eE1VW/x2sC7Daut98Gs/JBfuIiwKXBM8SVV46Vh9GbvWpPjGu2+olaSoXSeuPqORqJt+Wm5W/wv1szQHp+aMswNVQE4vgTEbf3DPFv7yvu3nfL7BMJ8seeHPWEqIqlLUAp+plBL+ZDzGs7KrYUs6UFXy1l1xAwT08YD7fFinYgbDMdZdcT2W5+zTd19cibozCAVsiwNCWfy2VIW/ok1KQHPST1NCv58QWImVWHWBS6/HquX1x695fcP7JMsqe6m5ZyfhFpV+uDq/jwFPCwFd/uCkR1m5z3mUy+jfK9eTjIS4/KZX0JvazdZJtdfAgA6cTnjcQU9o4S9aruWcblOlF/oqus2h6YHpFckQLWn3uD/RPu2cepyU2ZwIKhePL9zg0qjonH1fsM7i9/qgdTNZqatVBp7Dat4AXrXL0pbO6TMej7M+IeW69uxOld1VP0tzcKpMlgLqs5m6GHAumqP+hhK8BsNCsuSF/4i9loPV9lpJ5TEZJB5VArapPUYyZLOxfZbMCMfSr7P4W7o3UZA2zd2bZ37NDARTSujGtDtECEFHqxKW/emXABDXtWsyIlIrMgVA587avqkOe5teRgWLDTt3Nxzv7X6DesnqzSQ7lK86Ro5RXxsBqTJ0BpuuIif9jO54J5PS5qvVW1iZVkKevOFXa9c6HVbrGSbrYgOegBb+uuye1nYlni3NLapYWkvj7MkhEHY/40hTx4znOOT1ptMZKwHN6yHduPReauH3h6bft9bObvUZ5J6Z9rlNJb12B5PSJtBzS+1Yct1VZAkT1Cuu60mEVOXNnrUqoBqgOO0cg2ExsKSrcwJ8M/Zq/nj0Nr7se4Bu+ulN3srVut71S7d2cOeW9hk3vwBmFP6O7g1UP3yGdedR4Cmmq2/Wb//3b/dfx5nxU2yKK9GNxpIUpM24Z4qY3f2pade74l3/SKVSnla3+/I3fZJK+eN4PB6aO1bX1hpMhjtJV1TaYmLby/Ff/Rmu8noZvem1fMX21WrJrN9xM5luVVCu9J2/g94fUKyLDXiCqm2VOuH36rUSl3V3wJ3TFy85hOuC0PGzCL9P7xjW1/kSum/8ANz4gYbnpUe5evzB6aWHt936SuSe/44o5WrpmbPR3LmG6u+fZoPX/RpE4mmqv3eMK2aYyQkh+N4HbmF08BQ8N+1pg2HRsOQt/pDfSxWLLqGCnOGrGt0js4o+uDnS/qmuoPP72JItSviLATebxe/10JII1xb0CMtiWCQaLGz1hJiWuSEsqxZwnYpHi5jX9jEg1AyjGltRqyUTjkRr5ySi4WmVFeOpZuKpZizt3qr43RiJ1xH++jIB9QuJZmirQzgYVvWHpEUi3TbjOQ4dVbV4a+Wtb5nxmtKr91sNzbB4yR9FbNTB+rNY/ACWd7rtY3m9sy60sixx1vYbDJc6S174nY0dfrblwzwRuo5Nu86ehVOjZvEn5j7vLATDUfZEb8W/ae73PtJxJxN6p6uLwYhPCZSdXsXYzQ9wzF5Det3Os7xK4Yh8NejOUnzatVKtL+UbSMCml0F3Y6B5Kh6PRY6AzkSaOy7y08s+zKPxO+hcM7PbKNRzM49HbsLnm8Vnfu27YOV1yk02D1geD08Er+XRK/5wXq5vMMw3S97V42x8cd0vvYlE6O3n9+IZgrsXypXv/9pZz7n2Hf/jeb9PPRPBdijuI9KymrU7dsOOJ875tbYWeasuWOt3Nu2oW8mMEHDfP53TNSdFgDFPguaznHfNq94/5/Nbb/lluOWXZz+hYzu89Zvn1KYLZfvvfGter28wzCdL3uIP+T3Yngvc/MCx+BfpsvhyVAWMU53nX5fcF1J999SVj3ACtMI/845LZ2NSBJiwp2f9GAyGF5Ylb/G/6souelqiF1Yca80tcN27oXPHxW7WC8KKm9/MIz+02dW+6rxfu3rLLn7y6OvYdJ1bNbutq4efrHgb3de/+oLac2b7e/DFTY10g2GhEc5u9ZcyO3fulI899thCN8NgMBgWFUKIPVLKacGuJe/qMRgMBkMjRvgNBoNhmWGE32AwGJYZRvgNBoNhmWGE32AwGJYZRvgNBoNhmWGE32AwGJYZRvgNBoNhmbEoFnAJIQaAoxfw0iZg8CI3Z6Ewfbk0MX25NDF9UaySUk4rj7UohP9CEUI8NtOqtcWI6culienLpYnpy9wYV4/BYDAsM4zwGwwGwzJjqQv/Zxe6ARcR05dLE9OXSxPTlzlY0j5+g8FgMExnqVv8BoPBYJiCEX6DwWBYZixZ4RdC3CGE+IUQ4oAQ4oML3Z7zQQhxRAixTwixVwjxmD6WEkJ8RwjxnP6dXOh2zoYQ4u+FEGeEEE/XHZu1/UKI39X36RdCiJcsTKunM0s/HhBCnNT3Zq8Q4q665y7JfgAIIbqEEN8VQvQKIX4uhHiPPr4Y78tsfVl090YIERBCPCqEeFL35SP6+PzeFynlkvsBPMBBYA3gA54ENi90u86j/UeApinH/gz4oH78QeBjC93OOdp/E7ADePps7Qc26/vjB1br++ZZ6D7M0Y8HgA/McO4l2w/dvnZgh34cBZ7VbV6M92W2viy6ewMIIKIf28BPgV3zfV+WqsV/NXBASnlISlkE/gW4e4Hb9Hy5G/i8fvx54J45zl1QpJQ/AIanHJ6t/XcD/yKlLEgpDwMHUPdvwZmlH7NxyfYDQErZJ6V8XD8eA3qBThbnfZmtL7NxKfdFSinH9Z+2/pHM831ZqsLfCRyv+/sEc/9jXGpI4NtCiD1CiHfoY61Syj5Q//hAy4K17sKYrf2L8V79uhDiKe0Kcqbgi6YfQohuYDvKulzU92VKX2AR3hshhEcIsRc4A3xHSjnv92WpCr+Y4dhiylu9Xkq5A7gTuF8IcdNCN2geWWz36n8Ca4FtQB/wF/r4ouiHECICfAV4r5QyO9epMxy7pPozQ18W5b2RUlaklNuAFcDVQogtc5x+UfqyVIX/BNBV9/cK4NQCteW8kVKe0r/PAP+Gmsr1CyHaAfTvMwvXwgtitvYvqnslpezXX9Qq8De40+xLvh9CCBsllF+UUn5VH16U92WmvizmewMgpRwFvgfcwTzfl6Uq/D8DeoQQq4UQPuA1wIML3KZzQggRFkJEncfA7cDTqPa/SZ/2JuDfF6aFF8xs7X8QeI0Qwi+EWA30AI8uQPvOCefLqLkXdW/gEu+HEEIAfwf0Sik/UffUorsvs/VlMd4bIUSzECKhHweB24BnmO/7stBR7XmMlt+FivYfBD600O05j3avQUXtnwR+7rQdSAMPAc/p36mFbuscffhn1FS7hLJQ3jZX+4EP6fv0C+DOhW7/WfrxBWAf8JT+ErZf6v3QbbsB5RJ4Ctirf+5apPdltr4sunsDbAWe0G1+GviwPj6v98WUbDAYDIZlxlJ19RgMBoNhFozwGwwGwzLDCL/BYDAsM4zwGwwGwzLDCL/BYDAsM7wL3QCD4VJGCFFBpQh6gcPAG6RaaGMwLFqMxW8wzE1eSrlNSrkFVbDt/oVukMHwfDHCbzCcO4+gC2IJIb4nhNipHzcJIY7ox28WQnxVCPEtXUv9zxauuQbDzBjhNxjOASGEB9jNuZX+2AbcB1wO3CeE6DrL+QbDC4oRfoNhboK6ZO4QkAK+cw6veUhKmZFSTgL7gVXz2UCD4Xwxwm8wzE1eqpK5q1C7uTk+/jLu9ycw5TWFuscVTBKF4RLDCL/BcA5IKTPAbwAf0CWBjwBX6qdfuVDtMhguBCP8BsM5IqV8AlU19TXAnwPvFEL8GGha0IYZDOeJqc5pMBgMywxj8RsMBsMywwi/wWAwLDOM8BsMBsMywwi/wWAwLDOM8BsMBsMywwi/wWAwLDOM8BsMBsMy4/8D/nxc5Pt2+oYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparison plots\n",
    "create_plots(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's have a look at the amount of Bellman and N-K steps needed in the nested fixed point algorithm\n",
    "create_nfxp_plots(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Su, C.‐L. and Judd, K.L. (2012), Constrained Optimization Approaches to Estimation of Structural Models. Econometrica, 80: 2213-2230. doi:10.3982/ECTA7925\n",
    "\n",
    "Dubé, J.‐P., Fox, J.T. and Su, C.‐L. (2012), Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation. Econometrica, 80: 2231-2267. doi:10.3982/ECTA8585\n",
    "\n",
    "Dong, Baiyu and Hsieh, Yu-Wei and Zhang, Xing, Implementing Maximum Likelihood Estimation of Empirical Matching Models: MPEC versus NFXP (April 20, 2017). USC-INET Research Paper No. 17-16. Available at SSRN: https://ssrn.com/abstract=2959116 or http://dx.doi.org/10.2139/ssrn.2959116"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}