CORE MACHINE LEARNING

Contrastive Predict-and-Search for Mixed Integer Linear Programs

August 12, 2024

Abstract

Mixed integer linear programs (MILP) are flexible and powerful tools for modeling and solving many difficult real-world combinatorial optimization problems. In this paper, we propose a novel machine learning (ML)-based framework ConPaS that learns to predict solutions to MILPs with contrastive learning. For training, we collect high-quality solutions as positive samples. We also collect low-quality or infeasible solutions as negative samples using novel optimization-based or sampling approaches. We then learn to make discriminative predictions by contrasting the positive and negative samples. During testing, we predict and fix the assignments for a subset of integer variables and then solve the resulting reduced MILP to find high-quality solutions. Empirically, ConPaS achieves state-of-the-art results compared to other ML-based approaches in terms of the quality of and the speed at which solutions are found.

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AUTHORS

Written by

Arman Zharmagambetov

Yuandong Tian

Aaron Ferber

Bistra Dilkina

Taoan Huang

Publisher

ICML

Research Topics

Core Machine Learning

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