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<link rel="../img/favicon.ico">
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<link rel="shortcut icon" href="../img/favicon.ico">
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<title>Usage - MIPLearn</title>
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<link href="../css/cinder.min.css" rel="stylesheet">
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</a>
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</li>
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<li>
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<a href="https://github.com/ANL-CEEESA/MIPLearn/edit/master/src/docs/usage.md"><i class="fab fa-github"></i> Edit on GitHub</a>
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<a href="https://github.com/ANL-CEEESA/MIPLearn/edit/dev/docs/usage.md"><i class="fab fa-github"></i> Edit on GitHub</a>
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<h1 id="usage">Usage</h1>
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<h3 id="installation">Installation</h3>
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<p>MIPLearn is mainly written in Python, with some components written in Julia. For this
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reason, both Python 3.6+ and Julia 1.3+ are required. A mixed-integer solver is also required, and
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its Python bindings must be properly installed. Supported solvers are CPLEX and
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Gurobi. Optimization problems currently need to be specified in the Pyomo modeling language.
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A JuMP interface to the package is currently under development.</p>
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<p>To install MIPLearn, run the following commands: </p>
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<pre><code class="bash">git clone https://github.com/ANL-CEEESA/MIPLearn.git
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cd MIPLearn
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make install
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<p>In these docs, we describe the Python/Pyomo version of the package, although a <a href="https://github.com/ANL-CEEESA/MIPLearn.jl">Julia/JuMP version</a> is also available. A mixed-integer solver is also required and its Python bindings must be properly installed. Supported solvers are currently CPLEX and Gurobi.</p>
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<p>To install MIPLearn, run: </p>
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<pre><code class="bash">pip3 install miplearn
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</code></pre>
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<p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported
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@@ -167,10 +165,6 @@ as follows:</p>
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<pre><code class="python">import miplearn
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</code></pre>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>To install MIPLearn in another Python environment, switch to that environment before running <code>make install</code>. To install the package in development mode, run <code>make develop</code> instead.</p>
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</div>
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<h3 id="using-learningsolver">Using <code>LearningSolver</code></h3>
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<p>The main class provided by this package is <code>LearningSolver</code>, a learning-enhanced MIP solver which uses information from previously solved instances to accelerate the solution of new instances. The following example shows its basic usage:</p>
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<pre><code class="python">from miplearn import LearningSolver
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</footer>
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<script>hljs.initHighlightingOnLoad();</script>
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<script>var base_url = ".."</script>
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<script src="../js/base.js"></script>
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