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Represent trained machine learning models as Pyomo optimization formulations
| Date | Stars |
|---|---|
| 2026-07-31 | 346 |
| 2026-08-06 | 346 |
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.. image:: https://user-images.githubusercontent.com/282580/146039921-b3ea73af-7da3-47c1-bdfb-c40ad537a737.png
:target: https://github.com/cog-imperial/OMLT
:alt: OMLT
:align: center
:width: 200px
.. image:: https://github.com/cog-imperial/OMLT/actions/workflows/main.yml/badge.svg
:target: https://github.com/cog-imperial/OMLT/actions?workflow=CI
:alt: CI Status
.. image:: https://codecov.io/gh/cog-imperial/OMLT/branch/main/graph/badge.svg?token=9U7WLDINJJ
:target: https://codecov.io/gh/cog-imperial/OMLT
.. image:: https://readthedocs.org/projects/omlt/badge/?version=latest
:target: https://omlt.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://user-images.githubusercontent.com/31448377/202018691-dfacb0f8-620d-4d48-b918-2fa8b8da3d26.png
:target: https://www.coin-or.org/
:alt: COIN
:width: 130px
===============================================
OMLT: Optimization and Machine Learning Toolkit
===============================================
OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment. The package provides various optimization formulations for machine learning models (such as full-space, reduced-space, and MILP) as well as an interface to import sequential Keras and general ONNX models.
Please reference the paper for this software package as:
::
@article{ceccon2022omlt,
title={OMLT: Optimization & Machine Learning Toolkit},
author={Ceccon, F. and Jalving, J. and Haddad, J. and Thebelt, A. and Tsay, C. and Laird, C. D and Misener, R.},
journal={Journal of Machine Learning Research},
volume={23},
number={349},
pages={1--8},
year={2022}
}
When utilizing linear model decision trees, please cite the following paper in addition:
::
@article{ammari2023,
title={Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications},
author= {Bashar L. Ammari and Emma S. Johnson and Georgia Stinchfield and Taehun Kim and Michael Bynum and William E. Hart and Joshua Pulsipher and Carl D. Laird},
journal={Computers \& Chemical Engineering},
volume = {178},
year = {2023},
issn = {0098-1354},
doi = {https://doi.org/10.1016/j.compchemeng.2023.108347}
}
When utilizing graph neural networks, please cite the following paper in addition:
::
@article{zhang2024,
title = {Augmenting optimization-based molecular design with graph neural networks},
author= {Shiqiang Zhang and Juan S. Campos and Christian Feldmann and Frederik Sandfort and Miriam Mathea and Ruth Misener},
journal = {Computers \& Chemical Engineering},
volume = {186},
pages = {108684},
year = {2024},
issn = {0098-1354},
doi = {https://doi.org/10.1016/j.compchemeng.2024.108684},
}
When utilizing conformal prediction for uncertainty quantification, please cite the following paper in addition:
::
@article{ovalle2026conformal,
title={Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees},
author={Ovalle, Daniel and Biegler, Lorenz and Grossmann, Ignacio and Laird, Carl and Dulce Rubio, Mateo},
journal={Advances in Neural Information Processing Systems},
volume={38},
pages={80387--80423},
year={2026}
}
Documentation
==============
The latest OMLT documentation can be found at the `readthedocs page <https://omlt.readthedocs.io/en/latest/index.html#>`_. Additionally, much of the current functionality is demonstrated using Jupyter notebooks available in the `notebooks folder <https://github.com/cog-imperial/OMLT/tree/main/docs/notebooks>`_.
Example
========
.. code-block:: Python
import tensorflow
import pyomo.environ aExcerpt of 9,386 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a424b317b8e93508, topic:onnx
matched fp:a424b317b8e93508, topic:neural-network