MIPLearn

MIPLearn is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML).

MIPLearn 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). For certain classes of problems, this approach has been shown to provide significant performance benefits (see benchmarks and references).

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.

• 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 is customizable and extensible. For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.

Documentation

For installation instructions, basic usage and benchmarks results, see the official documentation.

Acknowledgments

• 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.
• Based upon work supported by the U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.

Citing MIPLearn

If you use MIPLearn in your research (either the solver 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 (2020). DOI: 10.1287/ijoc.2020.0976

License

Released under the modified BSD license. See LICENSE for more details.