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@@ -801,7 +801,7 @@ <h3 class="anchored" data-anchor-id="simultaneous-sparse-formulation">Simultaneo
 \text{subject to} &amp;\quad \widetilde{\mathbf A} \bar{\bm z} + \tilde{\bm b} = \mathbf 0.
 \end{aligned}}
 </span></p>
-<div id="656e5e9e" class="cell">
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 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">function</span> <span class="fu">direct_dlqr_simultaneous</span>(A,B,x₀,Q,R,S,N)</span>

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@@ -756,7 +756,7 @@ <h2 class="anchored" data-anchor-id="why-optimization-modelling-languages">Why o
 \end{aligned}
 </span></p>
 <p>There are dozens of solvers that can be used to solve this problem. Here we demonstrate a usage of these two: <a href="https://osqp.org">OSQP</a> and <a href="https://oxfordcontrol.github.io/COSMO.jl/stable/">COSMO.jl</a>. And we are going to call the solvers in Julia (using the the wrappers <a href="https://github.com/osqp/OSQP.jl">OSQP.jl</a> for the former). First, we start with OSQP (in fact, this is their example):</p>
-<div id="fa2bbc1e" class="cell">
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 <details open="" class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">OSQP</span></span>
@@ -783,7 +783,7 @@ <h2 class="anchored" data-anchor-id="why-optimization-modelling-languages">Why o
   \text{subject to} &amp; \quad \begin{bmatrix} -1 &amp; -1\\ -1 &amp; 0\\ 0 &amp; -1\\ 1 &amp; 1\\ 1 &amp; 0\\ 0 &amp; 1\end{bmatrix}\bm x + \begin{bmatrix}1 \\ 0.7 \\ 0.7 \\ -1 \\ 0 \\ 0\end{bmatrix} \geq  \mathbf 0.
 \end{aligned}
 </span></p>
-<div id="e81e86be" class="cell">
+<div id="f0bdecce" class="cell">
 <details open="" class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">COSMO</span></span>
@@ -811,7 +811,7 @@ <h2 class="anchored" data-anchor-id="why-optimization-modelling-languages">Why o
 </div>
 <p>Although the two solvers are solving the same problem, the data has to be formatted differently for each of them (and the difference in syntax is not negligible either).</p>
 <p>What if we could formulate the same problem without considering the pecualiarities of each solver? It turns out that it is possible. In Julia we can use <a href="https://jump.dev/JuMP.jl/stable/">JuMP.jl</a>:</p>
-<div id="46d21a85" class="cell">
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 <details open="" class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">JuMP</span></span>
@@ -852,7 +852,7 @@ <h2 class="anchored" data-anchor-id="why-optimization-modelling-languages">Why o
 <section id="why-not-optimization-modelling-languages" class="level2">
 <h2 class="anchored" data-anchor-id="why-not-optimization-modelling-languages">Why not optimization modelling languages?</h2>
 <p>As a matter of fact, some optimization experts even keep avoiding OML/AML altogether. For example, if a company pays for a (not really cheap) license of <a href="https://www.gurobi.com/solutions/gurobi-optimizer/">Gurobi Optimizer</a> – a powerful optimization library for (MI)LP/QP/QCQP –, it may be the case that for a particular very large-scale optimization problem their optimization specialist will have hard time to find a third-party solver of comparable performance. If then <a href="https://www.gurobi.com/documentation/current/refman/py_python_api_overview.html">its Python API</a> makes definition of optimization problems convenient too (see the code below), maybe there is little regret that such problem definitions cannot be reused with a third-party solver. The more so that since it is tailored to Gurobi solver, it will offer control over the finest details.</p>
-<div id="523e23d5" class="cell">
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 <details open="" class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> <span class="bu">gurobipy</span> as gp</span>
@@ -902,7 +902,7 @@ <h2 class="anchored" data-anchor-id="optimization-modelling-in-julia">Optimizati
 <li><p><a href="https://jump.dev">JuMP.jl</a> not only constitutes one of the flagship packages of the Julia ecosystem but it is on par with the state of the art optimization modelling languages. Furthermore, being a free and open source software, it enjoys a vibrant community of developers and users. They even meet annually at JuMP-dev conference (<a href="https://jump.dev/meetings/jumpdev2023/">in 2023 in Boston, MA</a>).</p></li>
 <li><p><a href="https://jump.dev/Convex.jl">Convex.jl</a> is an implementation of the concept of <a href="https://fenchel.stanford.edu/home">Disciplined Convex Programming (DCP)</a> in Julia (below we also list its implementations in Matlab and Python). Even though it is now registered as a part of the JuMP.jl project, it is still a separate concept. Interesting, convenient, but it seems to be in a maintanence mode now.</p></li>
 </ul>
-<div id="60bf8318" class="cell">
+<div id="35af4eac" class="cell">
 <details open="" class="code-fold">
 <summary>Show the code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Convex</span>, <span class="bu">SCS</span></span>