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<ahref="https://github.com/ANL-CEEESA/MIPLearn/edit/master/src/docs/usage.md"><iclass="fab fa-github"></i> Edit on GitHub</a>
<ahref="https://github.com/ANL-CEEESA/MIPLearn/edit/dev/docs/usage.md"><iclass="fab fa-github"></i> Edit on GitHub</a>
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<h1id="usage">Usage</h1>
<h1id="usage">Usage</h1>
<h3id="installation">Installation</h3>
<h3id="installation">Installation</h3>
<p>MIPLearn is mainly written in Python, with some components written in Julia. For this
<p>In these docs, we describe the Python/Pyomo version of the package, although a <ahref="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>
reason, both Python 3.6+ and Julia 1.3+ are required. A mixed-integer solver is also required, and
<p>To install MIPLearn, run: </p>
its Python bindings must be properly installed. Supported solvers are CPLEX and
<pre><codeclass="bash">pip3 install miplearn
Gurobi. Optimization problems currently need to be specified in the Pyomo modeling language.
A JuMP interface to the package is currently under development.</p>
<p>To install MIPLearn, run the following commands: </p>
<p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported
<p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported
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<pre><codeclass="python">import miplearn
<pre><codeclass="python">import miplearn
</code></pre>
</code></pre>
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<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>
<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>
<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>