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Learning To Select Cuts For Efficient Mixed-Integer Programming.pdf

2105.13645.pdf
Preview of Learning to Select Cuts for Efficient Mixed-Integer Programming
🔗 Source: arxiv.org
📊 Size: 571 KB
👤 Author: Zeren Huang; Kerong Wang; Furui Liu; Hui-Ling Zhen; Weinan Zhang; Mingxuan Yuan; Jianye Hao; Yong Yu
⬇️ Downloads: 87

Summary

Cutting plane methods are crucial in solving mixed-integer programming (MIP) problems, and proper cut selection can significantly reduce computational burden. However, existing cut selection approaches rely heavily on heuristics, which are problem-dependent and lack generalization capability. A novel data-driven approach, Cut Ranking, is proposed to address this issue. It uses a scoring function to measure cut quality and selects the best cuts. The approach is based on multiple instance learning and uses static and dynamic problem-specific cut features to enable generalization. Extensive experiments demonstrate the superiority of Cut Ranking over traditional heuristics, and its deployment in an industrial solver achieves an average speedup of 12.42% without accuracy loss.

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Description

Cutting plane methods are crucial in solving mixed-integer programming (MIP) problems, and proper cut selection can significantly reduce computational burden.

Technical Information

  • File Format: PDF
  • File Size: 571 KB
  • Pages: 33
  • Language: EN
  • Author: Zeren Huang; Kerong Wang; Furui Liu; Hui-Ling Zhen; Weinan Zhang; Mingxuan Yuan; Jianye Hao; Yong Yu
  • Total Downloads: 87
  • Last Updated: 2 hours ago

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This PDF document about Learning to Select Cuts for Efficient Mixed-Integer Programming provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Learning to Select Cuts for Efficient Mixed-Integer Programming.

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