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README.md
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![Build status](https://img.shields.io/github/workflow/status/ANL-CEEESA/MIPLearn/Test)
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MIPLearn MIPLearn
======== ========
**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits. **MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmarks](https://anl-ceeesa.github.io/MIPLearn/problems/) and [references](https://anl-ceeesa.github.io/MIPLearn/about/)).
Features Features
-------- --------
* **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver. * **MIPLearn proposes a flexible problem specification format,** which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding.
* **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) affine subspaces where the solution is likely to reside; (iv) variable branching priorities to accelerate the exploration of the branch-and-bound tree. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention. * **MIPLearn provides a reference implementation of a *Learning-Enhanced Solver*,** which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance.
* **MIPLearn provides a set of benchmark problems and random instance generators,** covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. * **MIPLearn provides a set of benchmark problems and random instance generators,** covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.
@@ -16,35 +21,10 @@ Features
Documentation Documentation
------------- -------------
For installation instructions, basic usage and benchmarks results, see the official documentation at: For installation instructions, basic usage and benchmarks results, see the [official documentation](https://anl-ceeesa.github.io/MIPLearn/).
* [https://anl-ceeesa.github.io/MIPLearn/](https://anl-ceeesa.github.io/MIPLearn/)
License License
------- -------
MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization Released under the modified BSD license. See `COPYING.md` for mode details.
Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials provided
with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to
endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.

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# MIPLearn # MIPLearn
**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](benchmark.md#benchmark-results) and [references](about.md#references) for more details). **MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](problems.md) and [references](about.md#references) for more details).
### Features ### Features
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### Souce Code ### Souce Code
* [https://github.com/iSoron/miplearn](https://github.com/iSoron/miplearn) * [https://github.com/ANL-CEEESA/MIPLearn](https://github.com/ANL-CEEESA/MIPLearn)

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The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions `n` and `p`. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution `w`. When `fix_graph=False`, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way. The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions `n` and `p`. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution `w`. When `fix_graph=False`, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.
### Benchmark challenges ### Challenge A
#### Challenge A
* Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$ * Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
* Random vertex weights $w_v \sim U(100, 150)$ * Random vertex weights $w_v \sim U(100, 150)$
@@ -96,7 +94,7 @@ By default, all generated prices, weights and capacities are rounded to the near
* Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212. * Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
* Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21. * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
#### Challenge A ### Challenge A
* 250 variables, 10 constraints, fixed weights * 250 variables, 10 constraints, fixed weights
* $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$ * $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$

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<div class="col-md-9" role="main"> <div class="col-md-9" role="main">
<h1 id="miplearn">MIPLearn</h1> <h1 id="miplearn">MIPLearn</h1>
<p><strong>MIPLearn</strong> is an extensible framework for <em>Learning-Enhanced Mixed-Integer Optimization</em>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <a href="benchmark/#benchmark-results">benchmark results</a> and <a href="about/#references">references</a> for more details).</p> <p><strong>MIPLearn</strong> is an extensible framework for <strong>Learning-Enhanced Mixed-Integer Optimization</strong>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.</p>
<p>The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <a href="problems/">benchmark results</a> and <a href="about/#references">references</a> for more details).</p>
<h3 id="features">Features</h3> <h3 id="features">Features</h3>
<ul> <ul>
<li> <li>
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</ul> </ul>
<h3 id="souce-code">Souce Code</h3> <h3 id="souce-code">Souce Code</h3>
<ul> <ul>
<li><a href="https://github.com/iSoron/miplearn">https://github.com/iSoron/miplearn</a></li> <li><a href="https://github.com/ANL-CEEESA/MIPLearn">https://github.com/ANL-CEEESA/MIPLearn</a></li>
</ul></div> </ul></div>
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<li class="third-level"><a href="#problem-definition">Problem definition</a></li> <li class="third-level"><a href="#problem-definition">Problem definition</a></li>
<li class="third-level"><a href="#random-instance-generator">Random instance generator</a></li> <li class="third-level"><a href="#random-instance-generator">Random instance generator</a></li>
<li class="third-level"><a href="#benchmark-challenges_1">Benchmark challenges</a></li> <li class="third-level"><a href="#challenge-a">Challenge A</a></li>
<li class="second-level"><a href="#multidimensional-0-1-knapsack-problem">Multidimensional 0-1 Knapsack Problem</a></li> <li class="second-level"><a href="#multidimensional-0-1-knapsack-problem">Multidimensional 0-1 Knapsack Problem</a></li>
<li class="third-level"><a href="#problem-definition_1">Problem definition</a></li> <li class="third-level"><a href="#problem-definition_1">Problem definition</a></li>
<li class="third-level"><a href="#random-instance-generator_1">Random instance generator</a></li> <li class="third-level"><a href="#random-instance-generator_1">Random instance generator</a></li>
<li class="third-level"><a href="#challenge-a_1">Challenge A</a></li>
</ul> </ul>
</div></div> </div></div>
<div class="col-md-9" role="main"> <div class="col-md-9" role="main">
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<p>Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a <em>stable set</em> if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.</p> <p>Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a <em>stable set</em> if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.</p>
<h3 id="random-instance-generator">Random instance generator</h3> <h3 id="random-instance-generator">Random instance generator</h3>
<p>The class <code>MaxWeightStableSetGenerator</code> can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter <code>fix_graph=True</code> is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>. When <code>fix_graph=False</code>, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.</p> <p>The class <code>MaxWeightStableSetGenerator</code> can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter <code>fix_graph=True</code> is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>. When <code>fix_graph=False</code>, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.</p>
<h3 id="benchmark-challenges_1">Benchmark challenges</h3> <h3 id="challenge-a">Challenge A</h3>
<h4 id="challenge-a">Challenge A</h4>
<ul> <ul>
<li>Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$</li> <li>Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$</li>
<li>Random vertex weights $w_v \sim U(100, 150)$</li> <li>Random vertex weights $w_v \sim U(100, 150)$</li>
@@ -230,7 +230,7 @@ from the provided probability distributions <code>K</code> and <code>u</code>.</
<li>Fréville, Arnaud. <em>The multidimensional 0–1 knapsack problem: An overview.</em> European Journal of Operational Research 155.1 (2004): 1-21.</li> <li>Fréville, Arnaud. <em>The multidimensional 0–1 knapsack problem: An overview.</em> European Journal of Operational Research 155.1 (2004): 1-21.</li>
</ul> </ul>
</div> </div>
<h4 id="challenge-a_1">Challenge A</h4> <h3 id="challenge-a_1">Challenge A</h3>
<ul> <ul>
<li>250 variables, 10 constraints, fixed weights</li> <li>250 variables, 10 constraints, fixed weights</li>
<li>$w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$</li> <li>$w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$</li>

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