Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML)
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
Go to file
Alinson S. Xavier 31d47c37e1
Replace long README.md with link to documentation
6 years ago
.github/workflows GitHub Actions: fix permissions 6 years ago
docs Replace long README.md with link to documentation 6 years ago
miplearn Minor changes 6 years ago
.gitignore Temporarily remove unused files; make package work with Cbc 6 years ago
Makefile Modularize LearningSolver into components; implement branch-priority 6 years ago
README.md Replace long README.md with link to documentation 6 years ago
mkdocs.yml Add mkdocs 6 years ago
requirements.txt Implement BenchmarkRunner 6 years ago
setup.py Implement BenchmarkRunner 6 years ago

README.md

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.

For install instructions, basic usage and benchmarks, see the official documentation at:

http://axavier.org/projects/miplearn

License

MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.