build based on 0822dd3

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.8","generation_timestamp":"2025-02-03T21:37:35","documenter_version":"1.8.0"}}
+{"documenter":{"julia_version":"1.11.3","generation_timestamp":"2025-02-03T21:41:41","documenter_version":"1.8.0"}}

dev/generated/man/ipopt_callbacks/index.html

@@ -35,7 +35,7 @@
   ├ MOI.VariableIndex in MOI.EqualTo{Float64}: 10
   ├ MOI.VariableIndex in MOI.GreaterThan{Float64}: 344
   ├ MOI.VariableIndex in MOI.LessThan{Float64}: 344
-  └ MOI.ScalarAffineFunction{Float64} in MOI.EqualTo{Float64}: 49, MathOptInterface.VariableIndex[MOI.VariableIndex(1), MOI.VariableIndex(2), MOI.VariableIndex(3), MOI.VariableIndex(4), MOI.VariableIndex(5), MOI.VariableIndex(6), MOI.VariableIndex(7), MOI.VariableIndex(8), MOI.VariableIndex(9), MOI.VariableIndex(10)  …  MOI.VariableIndex(541), MOI.VariableIndex(542), MOI.VariableIndex(543), MOI.VariableIndex(544), MOI.VariableIndex(545), MOI.VariableIndex(546), MOI.VariableIndex(547), MOI.VariableIndex(548), MOI.VariableIndex(549), MOI.VariableIndex(550)], NamedTrajectories.StructNamedTrajectory.NamedTrajectory{Float64}([1.0 0.9795918367346939 … 0.020408163265306145 0.0; 0.0 0.02040816326530612 … 0.9795918367346939 1.0; … ; 0.0001494811485018097 0.0006734155116616319 … 0.02397591866150699 -0.0021900864397636512; 0.2 0.2 … 0.2 0.2], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0053083861774873235, 0.0013502520746884225, -0.0074943329622914655, 0.0001494811485018097  …  1.0, 0.0, 0.0, 0.0, 0.0, 0.001176468691488928, 0.00999702649619722, -0.013944196524184264, -0.0021900864397636512, 0.2], 50, :Δt, 11, (ψ̃ = 4, a = 2, da = 2, dda = 2, Δt = 1, states = 8, controls = 3), (a = ([-1.0, -1.0], [1.0, 1.0]), da = ([-Inf, -Inf], [Inf, Inf]), dda = ([-1.0, -1.0], [1.0, 1.0]), Δt = ([0.1], [0.30000000000000004])), (ψ̃ = [1.0, 0.0, 0.0, 0.0], a = [0.0, 0.0]), (a = [0.0, 0.0],), (ψ̃ = [0.0, 1.0, 0.0, 0.0],), (ψ̃ = 1:4, a = 5:6, da = 7:8, dda = 9:10, Δt = 11:11, states = [1, 2, 3, 4, 5, 6, 7, 8], controls = [9, 10, 11]), NamedTuple(), 0, NamedTuple(), NamedTuple(), (:ψ̃, :a, :da, :dda, :Δt), (:ψ̃, :a, :da), (:dda, :Δt)), AbstractIntegrator[QuantumStatePadeIntegrator([1, 2, 3, 4], [5, 6], 11, true, 2, 2, 4, 11, 4, false, PiccoloQuantumObjects.QuantumSystems.var&quot;#8#14&quot;{Vector{SparseArrays.SparseMatrixCSC{Float64, Int64}}, SparseArrays.SparseMatrixCSC{Float64, Int64}}(SparseArrays.SparseMatrixCSC{Float64, Int64}[sparse([4, 3, 2, 1], [1, 2, 3, 4], [-1.0, -1.0, 1.0, 1.0], 4, 4), sparse([2, 1, 4, 3], [1, 2, 3, 4], [1.0, -1.0, 1.0, -1.0], 4, 4)], sparse([3, 4, 1, 2], [1, 2, 3, 4], [-0.1, 0.1, 0.1, -0.1], 4, 4)), PiccoloQuantumObjects.QuantumSystems.var&quot;#9#15&quot;{Vector{SparseArrays.SparseMatrixCSC{Float64, Int64}}}(SparseArrays.SparseMatrixCSC{Float64, Int64}[sparse([4, 3, 2, 1], [1, 2, 3, 4], [-1.0, -1.0, 1.0, 1.0], 4, 4), sparse([2, 1, 4, 3], [1, 2, 3, 4], [1.0, -1.0, 1.0, -1.0], 4, 4)])), DerivativeIntegrator([5, 6], [7, 8], true, 11, 11, 2, false), DerivativeIntegrator([7, 8], [9, 10], true, 11, 11, 2, false)], IpoptOptions{Float64}(1.0e-8, 100.0, 1000, 1.0e6, 1.0, 1.0e-6, 0.001, 1.0e-6, 15, 1.0e10, 0.01, 0.01, 1.0e-5, 1.0e8, 0.0001, 1, nothing, &quot;no&quot;, &quot;no&quot;, &quot;no&quot;, &quot;text&quot;, &quot;no&quot;, &quot;no&quot;, &quot;original&quot;, 1, 0.0, &quot;no&quot;, nothing, &quot;limited-memory&quot;, &quot;no&quot;, 1.0e-6, &quot;mumps&quot;, 10, 3), PiccoloOptions(false, false, true, true, :pade, 4, true, ExponentialAction.expv, false, true, true, true, nothing, 1.0, false, false, 1.0, false, nothing, :ϕ), Dict{Symbol, Any}(:objective_terms =&gt; Dict[Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :a), Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :da), Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :dda), Dict{Symbol, Any}(:loss =&gt; :InfidelityLoss, :eval_hessian =&gt; true, :type =&gt; :QuantumObjective, :Q =&gt; [100.0], :names =&gt; (:ψ̃,), :goals =&gt; ([0.0, 1.0, 0.0, 0.0],))], :linear_constraints =&gt; LinearConstraint[EqualityConstraint([1], [1, 2, 3, 4], [1.0, 0.0, 0.0, 0.0], 11, &quot;initial value of ψ̃&quot;), EqualityConstraint([1], [5, 6], [0.0, 0.0], 11, &quot;initial value of a&quot;), EqualityConstraint([49, 50], [5, 6], [0.0, 0.0], 11, &quot;final value of a&quot;), BoundsConstraint([2, 3, 4, 5, 6, 7, 8, 9, 10, 11  …  39, 40, 41, 42, 43, 44, 45, 46, 47, 48], 5:6, [(-1.0, 1.0), (-1.0, 1.0)], 11, &quot;bounds on a&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 7:8, [(-Inf, Inf), (-Inf, Inf)], 11, &quot;bounds on da&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 9:10, [(-1.0, 1.0), (-1.0, 1.0)], 11, &quot;bounds on dda&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 11:11, [(0.1, 0.30000000000000004)], 11, &quot;bounds on Δt&quot;), TimeStepsAllEqualConstraint([11, 22, 33, 44, 55, 66, 77, 88, 99, 110  …  451, 462, 473, 484, 495, 506, 517, 528, 539, 550], &quot;time step all equal constraint&quot;)], :nonlinear_constraints =&gt; Any[]))</code></pre><p>The callback function can be used to monitor the optimization progress, save intermediate results, or modify the optimization process. For example, the following callback function saves the optimization trajectory at each iteration - this can be useful for debugging or plotting the optimization progress. <code>trajectory_history_callback</code> from the <code>Callbacks</code> module</p><pre><code class="language-julia hljs">callback, trajectory_history = QuantumCollocation.Callbacks.trajectory_history_callback(prob)
+  └ MOI.ScalarAffineFunction{Float64} in MOI.EqualTo{Float64}: 49, MathOptInterface.VariableIndex[MOI.VariableIndex(1), MOI.VariableIndex(2), MOI.VariableIndex(3), MOI.VariableIndex(4), MOI.VariableIndex(5), MOI.VariableIndex(6), MOI.VariableIndex(7), MOI.VariableIndex(8), MOI.VariableIndex(9), MOI.VariableIndex(10)  …  MOI.VariableIndex(541), MOI.VariableIndex(542), MOI.VariableIndex(543), MOI.VariableIndex(544), MOI.VariableIndex(545), MOI.VariableIndex(546), MOI.VariableIndex(547), MOI.VariableIndex(548), MOI.VariableIndex(549), MOI.VariableIndex(550)], NamedTrajectories.StructNamedTrajectory.NamedTrajectory{Float64}([1.0 0.9795918367346939 … 0.020408163265306145 0.0; 0.0 0.02040816326530612 … 0.9795918367346939 1.0; … ; 0.008173434661523461 -0.006509992676552897 … -0.005139075981297836 -0.001275869215216029; 0.2 0.2 … 0.2 0.2], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003888364295142631, 0.012703488484033687, 0.01334599062628588, 0.008173434661523461  …  1.0, 0.0, 0.0, 0.0, 0.0, -0.0017534148879423037, -0.010907322343564679, 0.009004211533910988, -0.001275869215216029, 0.2], 50, :Δt, 11, (ψ̃ = 4, a = 2, da = 2, dda = 2, Δt = 1, states = 8, controls = 3), (a = ([-1.0, -1.0], [1.0, 1.0]), da = ([-Inf, -Inf], [Inf, Inf]), dda = ([-1.0, -1.0], [1.0, 1.0]), Δt = ([0.1], [0.30000000000000004])), (ψ̃ = [1.0, 0.0, 0.0, 0.0], a = [0.0, 0.0]), (a = [0.0, 0.0],), (ψ̃ = [0.0, 1.0, 0.0, 0.0],), (ψ̃ = 1:4, a = 5:6, da = 7:8, dda = 9:10, Δt = 11:11, states = [1, 2, 3, 4, 5, 6, 7, 8], controls = [9, 10, 11]), NamedTuple(), 0, NamedTuple(), NamedTuple(), (:ψ̃, :a, :da, :dda, :Δt), (:ψ̃, :a, :da), (:dda, :Δt)), AbstractIntegrator[QuantumStatePadeIntegrator([1, 2, 3, 4], [5, 6], 11, true, 2, 2, 4, 11, 4, false, PiccoloQuantumObjects.QuantumSystems.var&quot;#8#14&quot;{Vector{SparseArrays.SparseMatrixCSC{Float64, Int64}}, SparseArrays.SparseMatrixCSC{Float64, Int64}}(SparseArrays.SparseMatrixCSC{Float64, Int64}[sparse([4, 3, 2, 1], [1, 2, 3, 4], [-1.0, -1.0, 1.0, 1.0], 4, 4), sparse([2, 1, 4, 3], [1, 2, 3, 4], [1.0, -1.0, 1.0, -1.0], 4, 4)], sparse([3, 4, 1, 2], [1, 2, 3, 4], [-0.1, 0.1, 0.1, -0.1], 4, 4)), PiccoloQuantumObjects.QuantumSystems.var&quot;#9#15&quot;{Vector{SparseArrays.SparseMatrixCSC{Float64, Int64}}}(SparseArrays.SparseMatrixCSC{Float64, Int64}[sparse([4, 3, 2, 1], [1, 2, 3, 4], [-1.0, -1.0, 1.0, 1.0], 4, 4), sparse([2, 1, 4, 3], [1, 2, 3, 4], [1.0, -1.0, 1.0, -1.0], 4, 4)])), DerivativeIntegrator([5, 6], [7, 8], true, 11, 11, 2, false), DerivativeIntegrator([7, 8], [9, 10], true, 11, 11, 2, false)], IpoptOptions{Float64}(1.0e-8, 100.0, 1000, 1.0e6, 1.0, 1.0e-6, 0.001, 1.0e-6, 15, 1.0e10, 0.01, 0.01, 1.0e-5, 1.0e8, 0.0001, 1, nothing, &quot;no&quot;, &quot;no&quot;, &quot;no&quot;, &quot;text&quot;, &quot;no&quot;, &quot;no&quot;, &quot;original&quot;, 1, 0.0, &quot;no&quot;, nothing, &quot;limited-memory&quot;, &quot;no&quot;, 1.0e-6, &quot;mumps&quot;, 10, 3), PiccoloOptions(false, false, true, true, :pade, 4, true, ExponentialAction.expv, false, true, true, true, nothing, 1.0, false, false, 1.0, false, nothing, :ϕ), Dict{Symbol, Any}(:objective_terms =&gt; Dict[Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :a), Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :da), Dict{Symbol, Any}(:dim =&gt; 11, :times =&gt; 1:50, :R =&gt; [0.01, 0.01], :eval_hessian =&gt; true, :baseline =&gt; [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0], :type =&gt; :QuadraticRegularizer, :name =&gt; :dda), Dict{Symbol, Any}(:loss =&gt; :InfidelityLoss, :eval_hessian =&gt; true, :type =&gt; :QuantumObjective, :Q =&gt; [100.0], :names =&gt; (:ψ̃,), :goals =&gt; ([0.0, 1.0, 0.0, 0.0],))], :linear_constraints =&gt; LinearConstraint[EqualityConstraint([1], [1, 2, 3, 4], [1.0, 0.0, 0.0, 0.0], 11, &quot;initial value of ψ̃&quot;), EqualityConstraint([1], [5, 6], [0.0, 0.0], 11, &quot;initial value of a&quot;), EqualityConstraint([49, 50], [5, 6], [0.0, 0.0], 11, &quot;final value of a&quot;), BoundsConstraint([2, 3, 4, 5, 6, 7, 8, 9, 10, 11  …  39, 40, 41, 42, 43, 44, 45, 46, 47, 48], 5:6, [(-1.0, 1.0), (-1.0, 1.0)], 11, &quot;bounds on a&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 7:8, [(-Inf, Inf), (-Inf, Inf)], 11, &quot;bounds on da&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 9:10, [(-1.0, 1.0), (-1.0, 1.0)], 11, &quot;bounds on dda&quot;), BoundsConstraint([1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 11:11, [(0.1, 0.30000000000000004)], 11, &quot;bounds on Δt&quot;), TimeStepsAllEqualConstraint([11, 22, 33, 44, 55, 66, 77, 88, 99, 110  …  451, 462, 473, 484, 495, 506, 517, 528, 539, 550], &quot;time step all equal constraint&quot;)], :nonlinear_constraints =&gt; Any[]))</code></pre><p>The callback function can be used to monitor the optimization progress, save intermediate results, or modify the optimization process. For example, the following callback function saves the optimization trajectory at each iteration - this can be useful for debugging or plotting the optimization progress. <code>trajectory_history_callback</code> from the <code>Callbacks</code> module</p><pre><code class="language-julia hljs">callback, trajectory_history = QuantumCollocation.Callbacks.trajectory_history_callback(prob)
 solve!(prob, max_iter=20, callback=callback)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">
 ******************************************************************************
 This program contains Ipopt, a library for large-scale nonlinear optimization.
@@ -55,4 +55,4 @@
 )
 
 best_trajectory_callback, best_trajectory_list = best_rollout_fidelity_callback(prob2)
-solve!(prob2, max_iter=20, callback=best_trajectory_callback)</code></pre><p>fidelity of the last iterate</p><pre><code class="language- hljs">@show Losses.fidelity(prob2)</code></pre><p>fidelity of the best iterate</p><pre><code class="language- hljs">@show QuantumCollocation.fidelity(best_trajectory_list[end], prob2.system)</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../rollouts/">« Rollouts</a><a class="docs-footer-nextpage" href="../../../lib/">Library »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Monday 3 February 2025 21:37">Monday 3 February 2025</span>. Using Julia version 1.10.8.</p></section><footer class="modal-card-foot"></footer></div></div></div></body><div data-docstringscollapsed="true"></div></html>
+solve!(prob2, max_iter=20, callback=best_trajectory_callback)</code></pre><p>fidelity of the last iterate</p><pre><code class="language- hljs">@show Losses.fidelity(prob2)</code></pre><p>fidelity of the best iterate</p><pre><code class="language- hljs">@show QuantumCollocation.fidelity(best_trajectory_list[end], prob2.system)</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../rollouts/">« Rollouts</a><a class="docs-footer-nextpage" href="../../../lib/">Library »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Monday 3 February 2025 21:41">Monday 3 February 2025</span>. Using Julia version 1.11.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body><div data-docstringscollapsed="true"></div></html>