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1.2. Baseline results
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<div class="section" id="Preliminaries">
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<h1><span class="section-number">1. </span>Preliminaries<a class="headerlink" href="#Preliminaries" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="Benchmark-challenges">
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<h2><span class="section-number">1.1. </span>Benchmark challenges<a class="headerlink" href="#Benchmark-challenges" title="Permalink to this headline">¶</a></h2>
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<p>When evaluating the performance of a conventional MIP solver, <em>benchmark sets</em>, such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the <em>training instances</em>) before solving the actual set of instances we are
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interested in (the <em>test instances</em>). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits.</p>
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<p>In MIPLearn, each optimization problem comes with a set of <strong>benchmark challenges</strong>, which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.</p>
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<h2><span class="section-number">1.2. </span>Baseline results<a class="headerlink" href="#Baseline-results" title="Permalink to this headline">¶</a></h2>
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<p>To illustrate the performance of <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code>, and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see <a class="reference external" href="index.md#references">references</a>. We compare three solvers:</p>
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<ul class="simple">
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<li><p><strong>baseline:</strong> Gurobi 9.1 with default settings (a conventional state-of-the-art MIP solver)</p></li>
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<li><p><strong>ml-exact:</strong> <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code> with default settings, using Gurobi 9.0 as internal MIP solver</p></li>
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<li><p><strong>ml-heuristic:</strong> Same as above, but with <code class="docutils literal notranslate"><span class="pre">mode="heuristic"</span></code></p></li>
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</ul>
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<p>All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.</p>
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<a class='right-next' id="next-link" href="../stab/" title="next page"><span class="section-number">2. </span>Maximum Weight Stable Set</a>
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