mit-wu-lab/learning-to-configure-separators
[NeurIPS 2023] Learning to Configure Separators in Branch-and-Cut
This project helps operations researchers and optimization specialists improve how quickly and efficiently they can solve complex problems using a technique called Branch-and-Cut. It takes standard problem definitions, like those for resource allocation or scheduling, and optimizes the internal settings of the solver. The output is a more efficient configuration that helps solve these problems faster.
No commits in the last 6 months.
Use this if you are an operations research scientist or an optimization engineer who regularly solves Mixed-Integer Linear Programs (MILPs) and wants to find better ways to configure your solvers to improve performance.
Not ideal if you are looking for a general-purpose solver for simple optimization problems or if you are not familiar with the concepts of Branch-and-Cut algorithms.
Stars
21
Forks
3
Language
Python
License
MIT
Category
Last pushed
Mar 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mit-wu-lab/learning-to-configure-separators"
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.