https://github.com/bstellato/mlopt

The Machine Learning Optimizer
https://github.com/bstellato/mlopt

cvxpy machine-learning optimization

Last synced: 3 months ago
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The Machine Learning Optimizer

• Host: GitHub
• URL: https://github.com/bstellato/mlopt
• Owner: bstellato
• License: apache-2.0
• Created: 2018-08-22T18:00:55.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-02-10T22:47:58.000Z (almost 2 years ago)
• Last Synced: 2024-10-13T22:36:31.577Z (4 months ago)
• Topics: cvxpy, machine-learning, optimization
• Language: Python
• Homepage:
• Size: 10.6 MB
• Stars: 97
• Watchers: 9
• Forks: 23
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning Optimizer

`mlopt` is a package to learn how to solve numerical optimization problems from data. It relies on [cvxpy](https://cvxpy.org) for modeling and [gurobi](https://www.gurobi.com/) for solving the problem offline.

`mlopt` learns how to solve programs using [pytorch](https://pytorch.org/) ([pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)), [xgboost](https://xgboost.readthedocs.io/en/latest/) or [optimaltrees](https://docs.interpretable.ai/stable). The machine learning hyperparameter optimization is performed using [optuna](https://optuna.org/).

Online, `mlopt` only requires to predict the strategy and solve a linear system using [scikit-umfpack](https://github.com/scikit-umfpack/scikit-umfpack).

## Examples

To see `mlopt` in action, have a look at the notebooks in the [examples/](./examples/) folder.

## Documentation

Coming soon at [mlopt.org](https://mlopt.org)!

## Citing

If you use `mlopt` for research, please cite the following papers:

* [The Voice of Optimization](https://arxiv.org/pdf/1812.09991.pdf):

  ```

  @Article{bertsimas2021,

  author        = {{Bertsimas}, D. and {Stellato}, B.},

  title         = {The Voice of Optimization},

  journal       = {Machine Learning},

  year          = {2021},

  month         = {2},

  volume        = {110},

  issue         = {2},

  pages         = {249--277},

  }

  ```

* [Online Mixed-Integer Optimization in Milliseconds](https://arxiv.org/pdf/1907.02206.pdf)

  ```

  @article{stellato2019a,

    author = {{Bertsimas}, D. and {Stellato}, B.},

    title = {Online Mixed-Integer Optimization in Milliseconds},

    journal = {arXiv e-prints},

    year = {2019},

    month = jul,

    adsnote = {Provided by the SAO/NASA Astrophysics Data System},

    adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190702206B},

    archiveprefix = {arXiv},

    eprint = {1907.02206},

    keywords = {Mathematics - Optimization and Control},

    pdf = {https://arxiv.org/pdf/1907.02206.pdf},

    primaryclass = {math.OC},

  }

  ```

The code to **reproduce the results in the papers** is available at [bstellato/mlopt_benchmarks](https://github.com/bstellato/mlopt_benchmarks).

## Projects using mlopt framework

* [Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control](https://arxiv.org/pdf/2004.03736.pdf)