Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming

• URL: http://arxiv.org/abs/2402.05501v1
• Date: Thu, 8 Feb 2024 09:19:26 GMT
• Title: Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
• Authors: Lara Scavuzzo and Karen Aardal and Andrea Lodi and Neil Yorke-Smith
• Abstract summary: Mixed Linear Programming (MILP) offers a powerful modeling language for a wide range of applications. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency.
• Score: 11.293025183996832
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades, enormous algorithmic progress has been made in solving MILPs, and many commercial and academic software packages exist. Nevertheless, the availability of data, both from problem instances and from solvers, and the desire to solve new problems and larger (real-life) instances, trigger the need for continuing algorithmic development. MILP solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This paper presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.

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