build based on c2a3368

dev/DCsOPF/index.html

@@ -9,11 +9,11 @@
 Nl, N = size(A, 1), size(A, 2)
 Bbr = diagm(0 => -(2 .+ 10 * rand(Nl))) # line parameters
 Ψ = [zeros(Nl) -Bbr * A[:, 2:end] * inv(A[:, 2:end]&#39; * Bbr * A[:, 2:end])] # PTDF matrix</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">5×4 Matrix{Float64}:
- 0.0  -0.441173  -0.10916   -0.0565592
- 0.0  -0.329036  -0.524526  -0.271774
- 0.0  -0.22979   -0.366315  -0.671667
- 0.0  -0.558827   0.10916    0.0565592
- 0.0   0.22979    0.366315  -0.328333</code></pre><p>Now we can continue the remaining ingredients that specify our systems:</p><pre><code class="language-julia hljs">Cp, Cd = [1 0; 0 0; 0 0; 0 1], [0 0; 1 0; 0 1; 0 0] # book-keeping
+ 0.0  -0.558172  -0.238942  -0.137026
+ 0.0  -0.138155  -0.237974  -0.136471
+ 0.0  -0.303674  -0.523084  -0.726503
+ 0.0  -0.441828   0.238942   0.137026
+ 0.0   0.303674   0.523084  -0.273497</code></pre><p>Now we can continue the remaining ingredients that specify our systems:</p><pre><code class="language-julia hljs">Cp, Cd = [1 0; 0 0; 0 0; 0 1], [0 0; 1 0; 0 1; 0 0] # book-keeping
 Ng, Nd = size(Cp, 2), size(Cd, 2)
 c = 4 .+ 10 * rand(Ng) # cost function parameters
 λp, λl = 1.6 * ones(Ng), 1.6 * ones(Nl) # lambdas for chance constraint reformulations
@@ -43,4 +43,4 @@
             [1 / λl[i] * (mean(pl[1, :], mop) - plmin[i]); buildSOC(pl[i, :], mop)] in SecondOrderCone())
 @objective(model, Min, sum(mean(p[i, :], mop) * c[i] for i in 1:Ng))
 optimize!(model) # here we go</code></pre><p>Let&#39;s extract the numerical values of the optimal solution.</p><pre><code class="language- hljs">@assert termination_status(model)==MOI.OPTIMAL &quot;Model not solved to optimality.&quot;
-psol, plsol, obj = value.(p), value.(pl), objective_value(model)</code></pre><p>Great, we&#39;ve solved the problem. How do we now make sense of the solution? For instance, we can look at the moments of the generated power:</p><pre><code class="language- hljs">p_moments = [[mean(psol[i, :], mop) var(psol[i, :], mop)] for i in 1:Ng]</code></pre><p>Similarly, we can study the moments for the branch flows:</p><pre><code class="language- hljs">pbr_moments = [[mean(plsol[i, :], mop) var(plsol[i, :], mop)] for i in 1:Nl]</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../random_ode/">« Random ODE</a><a class="docs-footer-nextpage" href="../math/">Mathematical Background »</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="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Sunday 29 October 2023 02:22">Sunday 29 October 2023</span>. Using Julia version 1.9.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+psol, plsol, obj = value.(p), value.(pl), objective_value(model)</code></pre><p>Great, we&#39;ve solved the problem. How do we now make sense of the solution? For instance, we can look at the moments of the generated power:</p><pre><code class="language- hljs">p_moments = [[mean(psol[i, :], mop) var(psol[i, :], mop)] for i in 1:Ng]</code></pre><p>Similarly, we can study the moments for the branch flows:</p><pre><code class="language- hljs">pbr_moments = [[mean(plsol[i, :], mop) var(plsol[i, :], mop)] for i in 1:Nl]</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../random_ode/">« Random ODE</a><a class="docs-footer-nextpage" href="../math/">Mathematical Background »</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="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Saturday 2 December 2023 15:00">Saturday 2 December 2023</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/TypeHierarchy.ipynb

@@ -145,7 +145,7 @@
     "meas::Measure\n",
     "```\n",
     "The purpose of `name` is obvious.\n",
-    "The integer `deg` stands for the maxium degree of the polynomials.\n",
+    "The integer `deg` stands for the maximum degree of the polynomials.\n",
     "Rather than storing the polynomials $\\phi_i$ themselves we store the recurrence coefficients `α`, `β` that characterize the system of orthogonal polynomials.\n",
     "These recurrence coefficients are the single most important piece of information for the orthogonal polynomials.\n",
     "For several common measures, there exist analytic formulae.\n",

dev/TypeHierarchy_new.ipynb

@@ -79,7 +79,7 @@
     "\n",
     "\n",
     "The purpose of `name` is obvious.\n",
-    "The integer `deg` stands for the maxium degree of the polynomials.\n",
+    "The integer `deg` stands for the maximum degree of the polynomials.\n",
     "Rather than storing the polynomials $\\phi_i$ themselves we store the recurrence coefficients `α`, `β` that characterize the system of orthogonal polynomials.\n",
     "These recurrence coefficients are the single most important piece of information for the orthogonal polynomials.\n",
     "For several common measures, there exist analytic formulae.\n",
@@ -155,7 +155,7 @@
     "| `w_uni::Vector{Function}` | Weight functions of underlying univariate measures |\n",
     "| `dom::Vector{Tuple{Float64,Float64}}` | Domain |\n",
     "| `symmetric::Vector{Bool}` | Symmetry properties |\n",
-    "| `pars::Vector{Dict}` | Additioanl parameters |\n",
+    "| `pars::Vector{Dict}` | Additional parameters |\n",
     "\n",
     "All fields from `Measure` appear in vectorized versions (except for the weight $w$, which is the weight of the product measure)\n",
     "The only *new* field is `w_uni`, which stacks the univariate weight functions.\n",