CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913777629265920 |
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| author | Zeng, Shuli Zhou, Mengjie Zhang, Sijia Hu, Yixiang Wu, Feng Li, Xiang-Yang |
| author_facet | Zeng, Shuli Zhou, Mengjie Zhang, Sijia Hu, Yixiang Wu, Feng Li, Xiang-Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving Zeng, Shuli Zhou, Mengjie Zhang, Sijia Hu, Yixiang Wu, Feng Li, Xiang-Yang Machine Learning Artificial Intelligence Optimization and Control 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. |
| title | CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving |
| topic | Machine Learning Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2504.03688 |