Opt-Mucca/PySCIPOpt-ML
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
This tool helps operations researchers and optimization specialists integrate trained machine learning models directly into their complex optimization problems. It takes machine learning models built with frameworks like Scikit-learn, XGBoost, or PyTorch, and converts them into a mathematical format that can be solved alongside traditional optimization constraints. The output is a more accurate and robust optimization solution that leverages the predictive power of ML.
No commits in the last 6 months. Available on PyPI.
Use this if you need to embed predictive insights from a machine learning model directly into a larger mixed-integer programming problem to make more informed decisions.
Not ideal if you are looking for a standalone machine learning library or a general-purpose optimization solver without the need to integrate ML models.
Stars
40
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
May 26, 2025
Commits (30d)
0
Dependencies
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Opt-Mucca/PySCIPOpt-ML"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SimonBlanke/Gradient-Free-Optimizers
Lightweight optimization with local, global, population-based and sequential techniques across...
Gurobi/gurobi-machinelearning
Formulate trained predictors in Gurobi models
emdgroup/baybe
Bayesian Optimization and Design of Experiments
heal-research/pyoperon
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
simon-hirsch/ondil
A package for online distributional learning.