• Corpus ID: 233168592

Ecole: A Library for Learning Inside MILP Solvers

@article{Prouvost2021EcoleAL,
  title={Ecole: A Library for Learning Inside MILP Solvers},
  author={Antoine Prouvost and Justin Dumouchelle and Maxime Gasse and Didier Ch'etelat and Andrea Lodi},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.02828}
}
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers. It exposes sequential decision making that must be performed in the process of solving as Markov decision processes. This means that, rather than trying to predict solutions to combinatorial optimization problems directly, Ecole allows machine learning to work in cooperation with a stateof-the-art a mixed… 

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