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Alinson S. Xavier
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@@ -5,7 +5,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>6. Benchmark Problems &#8212; MIPLearn 0.3</title>
<title>9. Benchmark Problems &#8212; MIPLearn 0.3</title>
<link href="../../_static/css/theme.css" rel="stylesheet" />
<link href="../../_static/css/index.c5995385ac14fb8791e8eb36b4908be2.css" rel="stylesheet" />
@@ -36,8 +36,8 @@
<script src="../../_static/sphinx-book-theme.12a9622fbb08dcb3a2a40b2c02b83a57.js"></script>
<link rel="index" title="Index" href="../../genindex/" />
<link rel="search" title="Search" href="../../search/" />
<link rel="next" title="7. Collectors &amp; Extractors" href="../collectors/" />
<link rel="prev" title="5. Solvers" href="../../guide/solvers/" />
<link rel="next" title="10. Collectors &amp; Extractors" href="../collectors/" />
<link rel="prev" title="8. Solvers" href="../../guide/solvers/" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="docsearch:language" content="en" />
@@ -66,6 +66,28 @@
</form><nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
<div class="bd-toc-item active">
<p class="caption">
<span class="caption-text">
Tutorials
</span>
</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-pyomo/">
1. Getting started (Pyomo)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-gurobipy/">
2. Getting started (Gurobipy)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../tutorials/getting-started-jump/">
3. Getting started (JuMP)
</a>
</li>
</ul>
<p class="caption">
<span class="caption-text">
User Guide
</span>
@@ -73,59 +95,59 @@
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="../../guide/problems/">
1. Benchmark Problems
4. Benchmark Problems
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../guide/collectors/">
2. Training Data Collectors
5. Training Data Collectors
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../guide/features/">
3. Feature Extractors
6. Feature Extractors
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../guide/primal/">
4. Primal Components
7. Primal Components
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../../guide/solvers/">
5. Solvers
8. Solvers
</a>
</li>
</ul>
<p class="caption">
<span class="caption-text">
API Reference
Python API Reference
</span>
</p>
<ul class="current nav bd-sidenav">
<li class="toctree-l1 current active">
<a class="current reference internal" href="#">
6. Benchmark Problems
9. Benchmark Problems
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../collectors/">
7. Collectors &amp; Extractors
10. Collectors &amp; Extractors
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../components/">
8. Components
11. Components
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../solvers/">
9. Solvers
12. Solvers
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="../helpers/">
10. Helpers
13. Helpers
</a>
</li>
</ul>
@@ -197,47 +219,47 @@
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.binpack">
6.1. miplearn.problems.binpack
9.1. miplearn.problems.binpack
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.multiknapsack">
6.2. miplearn.problems.multiknapsack
9.2. miplearn.problems.multiknapsack
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.pmedian">
6.3. miplearn.problems.pmedian
9.3. miplearn.problems.pmedian
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.setcover">
6.4. miplearn.problems.setcover
9.4. miplearn.problems.setcover
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.setpack">
6.5. miplearn.problems.setpack
9.5. miplearn.problems.setpack
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.stab">
6.6. miplearn.problems.stab
9.6. miplearn.problems.stab
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.tsp">
6.7. miplearn.problems.tsp
9.7. miplearn.problems.tsp
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.uc">
6.8. miplearn.problems.uc
9.8. miplearn.problems.uc
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#module-miplearn.problems.vertexcover">
6.9. miplearn.problems.vertexcover
9.9. miplearn.problems.vertexcover
</a>
</li>
</ul>
@@ -252,9 +274,9 @@
<div>
<div class="section" id="benchmark-problems">
<h1><span class="section-number">6. </span>Benchmark Problems<a class="headerlink" href="#benchmark-problems" title="Permalink to this headline"></a></h1>
<h1><span class="section-number">9. </span>Benchmark Problems<a class="headerlink" href="#benchmark-problems" title="Permalink to this headline"></a></h1>
<div class="section" id="module-miplearn.problems.binpack">
<span id="miplearn-problems-binpack"></span><h2><span class="section-number">6.1. </span>miplearn.problems.binpack<a class="headerlink" href="#module-miplearn.problems.binpack" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-binpack"></span><h2><span class="section-number">9.1. </span>miplearn.problems.binpack<a class="headerlink" href="#module-miplearn.problems.binpack" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.binpack.BinPackData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.binpack.</span></code><code class="sig-name descname"><span class="pre">BinPackData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sizes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capacity</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.binpack.BinPackData" title="Permalink to this definition"></a></dt>
@@ -312,13 +334,13 @@ If <cite>False</cite>, generates completely different instances.</p></li>
<dl class="py function">
<dt id="miplearn.problems.binpack.build_binpack_model">
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.binpack.</span></code><code class="sig-name descname"><span class="pre">build_binpack_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#miplearn.problems.binpack.BinPackData" title="miplearn.problems.binpack.BinPackData"><span class="pre">miplearn.problems.binpack.BinPackData</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.binpack.build_binpack_model" title="Permalink to this definition"></a></dt>
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.binpack.</span></code><code class="sig-name descname"><span class="pre">build_binpack_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#miplearn.problems.binpack.BinPackData" title="miplearn.problems.binpack.BinPackData"><span class="pre">miplearn.problems.binpack.BinPackData</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.binpack.build_binpack_model" title="Permalink to this definition"></a></dt>
<dd><p>Converts bin packing problem data into a concrete Gurobipy model.</p>
</dd></dl>
</div>
<div class="section" id="module-miplearn.problems.multiknapsack">
<span id="miplearn-problems-multiknapsack"></span><h2><span class="section-number">6.2. </span>miplearn.problems.multiknapsack<a class="headerlink" href="#module-miplearn.problems.multiknapsack" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-multiknapsack"></span><h2><span class="section-number">9.2. </span>miplearn.problems.multiknapsack<a class="headerlink" href="#module-miplearn.problems.multiknapsack" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.multiknapsack.MultiKnapsackData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.multiknapsack.</span></code><code class="sig-name descname"><span class="pre">MultiKnapsackData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prices</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capacities</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.multiknapsack.MultiKnapsackData" title="Permalink to this definition"></a></dt>
@@ -385,13 +407,13 @@ integer.</p></li>
<dl class="py function">
<dt id="miplearn.problems.multiknapsack.build_multiknapsack_model">
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.multiknapsack.</span></code><code class="sig-name descname"><span class="pre">build_multiknapsack_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#miplearn.problems.multiknapsack.MultiKnapsackData" title="miplearn.problems.multiknapsack.MultiKnapsackData"><span class="pre">miplearn.problems.multiknapsack.MultiKnapsackData</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.multiknapsack.build_multiknapsack_model" title="Permalink to this definition"></a></dt>
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.multiknapsack.</span></code><code class="sig-name descname"><span class="pre">build_multiknapsack_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#miplearn.problems.multiknapsack.MultiKnapsackData" title="miplearn.problems.multiknapsack.MultiKnapsackData"><span class="pre">miplearn.problems.multiknapsack.MultiKnapsackData</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.multiknapsack.build_multiknapsack_model" title="Permalink to this definition"></a></dt>
<dd><p>Converts multi-knapsack problem data into a concrete Gurobipy model.</p>
</dd></dl>
</div>
<div class="section" id="module-miplearn.problems.pmedian">
<span id="miplearn-problems-pmedian"></span><h2><span class="section-number">6.3. </span>miplearn.problems.pmedian<a class="headerlink" href="#module-miplearn.problems.pmedian" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-pmedian"></span><h2><span class="section-number">9.3. </span>miplearn.problems.pmedian<a class="headerlink" href="#module-miplearn.problems.pmedian" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.pmedian.PMedianData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.pmedian.</span></code><code class="sig-name descname"><span class="pre">PMedianData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distances</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">demands</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capacities</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.pmedian.PMedianData" title="Permalink to this definition"></a></dt>
@@ -447,13 +469,13 @@ different demands, capacities and distances.</p>
<dl class="py function">
<dt id="miplearn.problems.pmedian.build_pmedian_model">
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.pmedian.</span></code><code class="sig-name descname"><span class="pre">build_pmedian_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#miplearn.problems.pmedian.PMedianData" title="miplearn.problems.pmedian.PMedianData"><span class="pre">miplearn.problems.pmedian.PMedianData</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.pmedian.build_pmedian_model" title="Permalink to this definition"></a></dt>
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.pmedian.</span></code><code class="sig-name descname"><span class="pre">build_pmedian_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#miplearn.problems.pmedian.PMedianData" title="miplearn.problems.pmedian.PMedianData"><span class="pre">miplearn.problems.pmedian.PMedianData</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.pmedian.build_pmedian_model" title="Permalink to this definition"></a></dt>
<dd><p>Converts capacitated p-median data into a concrete Gurobipy model.</p>
</dd></dl>
</div>
<div class="section" id="module-miplearn.problems.setcover">
<span id="miplearn-problems-setcover"></span><h2><span class="section-number">6.4. </span>miplearn.problems.setcover<a class="headerlink" href="#module-miplearn.problems.setcover" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-setcover"></span><h2><span class="section-number">9.4. </span>miplearn.problems.setcover<a class="headerlink" href="#module-miplearn.problems.setcover" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.setcover.SetCoverData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.setcover.</span></code><code class="sig-name descname"><span class="pre">SetCoverData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">costs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">incidence_matrix</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.setcover.SetCoverData" title="Permalink to this definition"></a></dt>
@@ -461,7 +483,7 @@ different demands, capacities and distances.</p>
</div>
<div class="section" id="module-miplearn.problems.setpack">
<span id="miplearn-problems-setpack"></span><h2><span class="section-number">6.5. </span>miplearn.problems.setpack<a class="headerlink" href="#module-miplearn.problems.setpack" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-setpack"></span><h2><span class="section-number">9.5. </span>miplearn.problems.setpack<a class="headerlink" href="#module-miplearn.problems.setpack" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.setpack.SetPackData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.setpack.</span></code><code class="sig-name descname"><span class="pre">SetPackData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">costs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">incidence_matrix</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.setpack.SetPackData" title="Permalink to this definition"></a></dt>
@@ -469,7 +491,7 @@ different demands, capacities and distances.</p>
</div>
<div class="section" id="module-miplearn.problems.stab">
<span id="miplearn-problems-stab"></span><h2><span class="section-number">6.6. </span>miplearn.problems.stab<a class="headerlink" href="#module-miplearn.problems.stab" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-stab"></span><h2><span class="section-number">9.6. </span>miplearn.problems.stab<a class="headerlink" href="#module-miplearn.problems.stab" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.stab.MaxWeightStableSetData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.stab.</span></code><code class="sig-name descname"><span class="pre">MaxWeightStableSetData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">networkx.classes.graph.Graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.stab.MaxWeightStableSetData" title="Permalink to this definition"></a></dt>
@@ -490,7 +512,7 @@ remaining parameters are sampled in the same way.</p>
</div>
<div class="section" id="module-miplearn.problems.tsp">
<span id="miplearn-problems-tsp"></span><h2><span class="section-number">6.7. </span>miplearn.problems.tsp<a class="headerlink" href="#module-miplearn.problems.tsp" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-tsp"></span><h2><span class="section-number">9.7. </span>miplearn.problems.tsp<a class="headerlink" href="#module-miplearn.problems.tsp" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.tsp.TravelingSalesmanData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.tsp.</span></code><code class="sig-name descname"><span class="pre">TravelingSalesmanData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_cities</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distances</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.tsp.TravelingSalesmanData" title="Permalink to this definition"></a></dt>
@@ -504,7 +526,7 @@ remaining parameters are sampled in the same way.</p>
</div>
<div class="section" id="module-miplearn.problems.uc">
<span id="miplearn-problems-uc"></span><h2><span class="section-number">6.8. </span>miplearn.problems.uc<a class="headerlink" href="#module-miplearn.problems.uc" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-uc"></span><h2><span class="section-number">9.8. </span>miplearn.problems.uc<a class="headerlink" href="#module-miplearn.problems.uc" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.uc.UnitCommitmentData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.uc.</span></code><code class="sig-name descname"><span class="pre">UnitCommitmentData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">demand</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_power</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_power</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_uptime</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_downtime</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cost_startup</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cost_prod</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cost_fixed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.uc.UnitCommitmentData" title="Permalink to this definition"></a></dt>
@@ -512,7 +534,7 @@ remaining parameters are sampled in the same way.</p>
<dl class="py function">
<dt id="miplearn.problems.uc.build_uc_model">
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.uc.</span></code><code class="sig-name descname"><span class="pre">build_uc_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#miplearn.problems.uc.UnitCommitmentData" title="miplearn.problems.uc.UnitCommitmentData"><span class="pre">miplearn.problems.uc.UnitCommitmentData</span></a></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.uc.build_uc_model" title="Permalink to this definition"></a></dt>
<code class="sig-prename descclassname"><span class="pre">miplearn.problems.uc.</span></code><code class="sig-name descname"><span class="pre">build_uc_model</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#miplearn.problems.uc.UnitCommitmentData" title="miplearn.problems.uc.UnitCommitmentData"><span class="pre">miplearn.problems.uc.UnitCommitmentData</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../solvers/#miplearn.solvers.gurobi.GurobiModel" title="miplearn.solvers.gurobi.GurobiModel"><span class="pre">miplearn.solvers.gurobi.GurobiModel</span></a><a class="headerlink" href="#miplearn.problems.uc.build_uc_model" title="Permalink to this definition"></a></dt>
<dd><p>Models the unit commitment problem according to equations (1)-(5) of:</p>
<blockquote>
<div><p>Bendotti, P., Fouilhoux, P. &amp; Rottner, C. The min-up/min-down unit
@@ -523,7 +545,7 @@ commitment polytope. J Comb Optim 36, 1024-1058 (2018).
</div>
<div class="section" id="module-miplearn.problems.vertexcover">
<span id="miplearn-problems-vertexcover"></span><h2><span class="section-number">6.9. </span>miplearn.problems.vertexcover<a class="headerlink" href="#module-miplearn.problems.vertexcover" title="Permalink to this headline"></a></h2>
<span id="miplearn-problems-vertexcover"></span><h2><span class="section-number">9.9. </span>miplearn.problems.vertexcover<a class="headerlink" href="#module-miplearn.problems.vertexcover" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="miplearn.problems.vertexcover.MinWeightVertexCoverData">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">miplearn.problems.vertexcover.</span></code><code class="sig-name descname"><span class="pre">MinWeightVertexCoverData</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">networkx.classes.graph.Graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#miplearn.problems.vertexcover.MinWeightVertexCoverData" title="Permalink to this definition"></a></dt>
@@ -538,8 +560,8 @@ commitment polytope. J Comb Optim 36, 1024-1058 (2018).
<div class='prev-next-bottom'>
<a class='left-prev' id="prev-link" href="../../guide/solvers/" title="previous page"><span class="section-number">5. </span>Solvers</a>
<a class='right-next' id="next-link" href="../collectors/" title="next page"><span class="section-number">7. </span>Collectors &amp; Extractors</a>
<a class='left-prev' id="prev-link" href="../../guide/solvers/" title="previous page"><span class="section-number">8. </span>Solvers</a>
<a class='right-next' id="next-link" href="../collectors/" title="next page"><span class="section-number">10. </span>Collectors &amp; Extractors</a>
</div>
@@ -549,7 +571,7 @@ commitment polytope. J Comb Optim 36, 1024-1058 (2018).
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