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1.
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Using MIPLearn
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2.
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Benchmarks
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3.
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Customization
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4.
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About
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4.1.
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Authors
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Acknowledgments
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4.3.
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References
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4.4.
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License
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<h1><span class="sectnum">4.</span> About<a class="headerlink" href="#about" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="authors">
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<h2><span class="sectnum">4.1.</span> Authors<a class="headerlink" href="#authors" title="Permalink to this headline">¶</a></h2>
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<ul class="simple">
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<li><p><strong>Alinson S. Xavier,</strong> Argonne National Laboratory <<a class="reference external" href="mailto:axavier%40anl.gov">mailto:axavier<span>@</span>anl<span>.</span>gov</a>></p></li>
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<li><p><strong>Feng Qiu,</strong> Argonne National Laboratory <<a class="reference external" href="mailto:fqiu%40anl.gov">mailto:fqiu<span>@</span>anl<span>.</span>gov</a>></p></li>
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<div class="section" id="acknowledgments">
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<h2><span class="sectnum">4.2.</span> Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">¶</a></h2>
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<ul class="simple">
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<li><p>Based upon work supported by <strong>Laboratory Directed Research and Development</strong> (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357, and the <strong>U.S. Department of Energy Advanced Grid Modeling Program</strong> under Grant DE-OE0000875.</p></li>
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<div class="section" id="references">
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<h2><span class="sectnum">4.3.</span> References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
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<p>If you use MIPLearn in your research, or the included problem generators, we kindly request that you cite the package as follows:</p>
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<ul class="simple">
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<li><p><strong>Alinson S. Xavier, Feng Qiu.</strong> <em>MIPLearn: An Extensible Framework for Learning-Enhanced Optimization</em>. Zenodo (2020). DOI: <a class="reference external" href="https://doi.org/10.5281/zenodo.4287567">10.5281/zenodo.4287567</a></p></li>
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</ul>
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<p>If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:</p>
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<ul class="simple">
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<li><p><strong>Alinson S. Xavier, Feng Qiu, Shabbir Ahmed.</strong> <em>Learning to Solve Large-Scale Unit Commitment Problems.</em> INFORMS Journal on Computing (2020). DOI: <a class="reference external" href="https://doi.org/10.1287/ijoc.2020.0976">10.1287/ijoc.2020.0976</a></p></li>
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</ul>
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<div class="section" id="license">
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<h2><span class="sectnum">4.4.</span> License<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h2>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
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Redistribution and use in source and binary forms, with or without modification, are permitted
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provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this list of
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conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice, this list of
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conditions and the following disclaimer in the documentation and/or other materials provided
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with the distribution.
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3. Neither the name of the copyright holder nor the names of its contributors may be used to
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endorse or promote products derived from this software without specific prior written
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permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
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AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
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OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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</pre></div>
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