miplearn
Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML)
Science Score: 59.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 9 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
1 of 6 committers (16.7%) from academic institutions -
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.7%) to scientific vocabulary
Repository
Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML)
Basic Info
Statistics
- Stars: 172
- Watchers: 7
- Forks: 21
- Open Issues: 3
- Releases: 4
Metadata Files
README.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). 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 may provide significant performance benefits.
Documentation
- Tutorials:
- User Guide
- Python API Reference
Authors
- Alinson S. Xavier (Argonne National Laboratory)
- Feng Qiu (Argonne National Laboratory)
- Xiaoyi Gu (Georgia Institute of Technology)
- Berkay Becu (Georgia Institute of Technology)
- Santanu S. Dey (Georgia Institute of Technology)
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.
- Based upon work supported by the U.S. Department of Energy Advanced Grid Modeling Program.
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, Xiaoyi Gu, Berkay Becu, Santanu S. Dey. MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.4). Zenodo (2024). 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.
Owner
- Name: ANL-CEEESA
- Login: ANL-CEEESA
- Kind: organization
- Location: Argonne, IL
- Website: https://github.com/argonne-national-laboratory
- Repositories: 8
- Profile: https://github.com/ANL-CEEESA
Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis (CEEESA)
GitHub Events
Total
- Watch event: 24
- Push event: 8
- Fork event: 4
Last Year
- Watch event: 24
- Push event: 8
- Fork event: 4
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 658
- Total Committers: 6
- Avg Commits per committer: 109.667
- Development Distribution Score (DDS): 0.495
Top Committers
| Name | Commits | |
|---|---|---|
| Alinson S. Xavier | g****t@a****g | 332 |
| Alinson S Xavier | a****r@a****v | 321 |
| Feng | h****s@u****m | 2 |
| Gregor Hendel | h****l@z****e | 1 |
| Álinson S. Xavier | i****n@g****m | 1 |
| bknueven | b****e@s****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 4
- Total pull requests: 2
- Average time to close issues: about 3 hours
- Average time to close pull requests: 4 days
- Total issue authors: 4
- Total pull request authors: 2
- Average comments per issue: 0.25
- Average comments per pull request: 1.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- cycao77 (1)
- akazachk (1)
- mzy2240 (1)
- samwu-learn (1)
Pull Request Authors
- GregorCH (1)
- bknueven (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 127 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 21
- Total maintainers: 1
pypi.org: miplearn
Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
- Homepage: https://github.com/ANL-CEEESA/MIPLearn/
- Documentation: https://miplearn.readthedocs.io/
- License: other
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Latest release: 0.4.3
published 9 months ago
Rankings
Maintainers (1)
Dependencies
- decorator >=4,<5
- h5py >=3,<4
- matplotlib >=3,<4
- mypy ==0.790
- networkx >=2,<3
- numpy >=1,<1.21
- overrides >=3,<4
- p_tqdm >=1,<2
- pandas >=1,<2
- pyomo >=5,<6
- pytest >=6,<7
- python-markdown-math >=0.8,<0.9
- scikit-learn >=0.24,<0.25
- seaborn >=0.11,<0.12
- tqdm >=4,<5