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arXiv · 2504.03688

CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving

Shuli Zeng·Mengjie Zhou·Sijia Zhang·Yixiang Hu·Feng Wu·Xiang-Yang Li
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Abstract

Constraint ordering plays a critical role in the efficiency of Mixed-Integer Linear Programming (MILP) solvers, particularly for large-scale problems where poorly ordered constraints trigger increased LP iterations and suboptimal search trajectories. This paper introduces CLCR (Contrastive Learning-based Constraint Reordering), a novel framework that systematically optimizes constraint ordering to accelerate MILP solving. CLCR first clusters constraints based on their structural patterns and then employs contrastive learning with a pointer network to optimize their sequence, preserving problem equivalence while improving solver efficiency. Experiments on benchmarks show CLCR reduces solving time by 30% and LP iterations by 25% on average, without sacrificing solution accuracy. This work demonstrates the potential of data-driven constraint ordering to enhance optimization models, offering a new paradigm for bridging mathematical programming with machine learning.

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Shuli Zeng, Mengjie Zhou, Sijia Zhang, Yixiang Hu, Feng Wu, Xiang-Yang Li. 2025-03-23. CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving. https://arxiv.org/abs/2504.03688

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