MIPLearn/docs/index.md

MIPLearn

MIPLearn is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS).

Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions. Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value).

Table of Contents

---
maxdepth: 1
caption: JuMP Tutorials
numbered: true
---
jump-tutorials/getting-started.ipynb
#jump-tutorials/lazy-constraints.ipynb
#jump-tutorials/user-cuts.ipynb
#jump-tutorials/customizing-ml.ipynb

---
maxdepth: 1
caption: Pyomo Tutorials
numbered: true
---
pyomo-tutorials/getting-started.ipynb
#pyomo-tutorials/lazy-constraints.ipynb
#pyomo-tutorials/user-cuts.ipynb
#pyomo-tutorials/customizing-ml.ipynb

---
maxdepth: 1
caption: Benchmarks
numbered: true
---
benchmarks/preliminaries.ipynb
benchmarks/stab.ipynb
#benchmarks/uc.ipynb
#benchmarks/facility.ipynb
benchmarks/knapsack.ipynb
benchmarks/tsp.ipynb

---
maxdepth: 1
caption: MIPLearn Internals
numbered: true
---
#internals/solver-interfaces.ipynb
#internals/data-collection.ipynb
#internals/abstract-component.ipynb
#internals/primal.ipynb
#internals/static-lazy.ipynb
#internals/dynamic-lazy.ipynb

Source Code

• https://github.com/ANL-CEEESA/MIPLearn

Authors

• Alinson S. Xavier, Argonne National Laboratory <axavier@anl.gov>
• Feng Qiu, Argonne National Laboratory <fqiu@anl.gov>

Acknowledgments

• Based upon work supported by U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.
• Based upon work supported by Laboratory Directed Research and Development (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

References

If you use MIPLearn in your research, or the included problem generators, we kindly request that you cite the package as follows:

• Alinson S. Xavier, Feng Qiu. MIPLearn: An Extensible Framework for Learning-Enhanced Optimization. Zenodo (2020). DOI: 10.5281/zenodo.4287567

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:

• Alinson S. Xavier, Feng Qiu, Shabbir Ahmed. Learning to Solve Large-Scale Unit Commitment Problems. INFORMS Journal on Computing (2021). DOI: 10.1287/ijoc.2020.0976

License

MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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:

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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
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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
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