Ecole: A Library for Learning Inside MILP Solvers

TL;DR

Ecole is a library that enables the integration of machine learning techniques into mixed-integer linear programming solvers by modeling the solving process as Markov decision processes, facilitating learning-driven optimization.

Contribution

The paper introduces Ecole, a flexible library that simplifies embedding machine learning into MILP solvers through a unified environment for sequential decision making.

Findings

Provides ready-to-use environments for learning in MILP solving

Enables easy extension for new training tasks

Facilitates cooperation between ML and optimization algorithms

Abstract

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 state-of-the-art a mixed-integer linear programming solver that acts as a controllable algorithm. Ecole provides a collection of computationally efficient, ready to use learning environments, which are also easy to extend to define novel training tasks. Documentation and code can be found at https://www.ecole.ai.