学习通过等级序列/集合模型切割,以实现高效的混合整数编程
概括
本研究介绍了一种层次序列/集合模型 (HEM),用于改进混合整数线性编程 (MILP) 解析器. HEM学习最佳切割选择策略,显著提高MILP解决效率.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 混合整数线性编程 (MILP) 对现实世界的应用至关重要.
- 当前的MILP解决方案依赖于人类设计的启发式方法来进行切割选择.
- 现有的机器学习方法往往忽略了切割的数量和顺序.
研究的目的:
- 开发一个数据驱动的方法来学习MILP中有效的切割选择策略.
- 解决选择哪些切割优先,选择多少切割,以及以什么顺序的同时挑战.
- 通过先进的机器学习来提高MILP解决者的整体效率.
主要方法:
- 为MILP切割选择提出了一种新的等级序列/集模型 (HEM).
- HEM具有双层架构:一个用于切割枢纽性的高层模块和一个用于有序子集选择的低层模块.
- 在较低级别的模块中,制定了切片选择作为序列/设置到序列的学习问题.
主要成果:
- HEM有效地学习如何选择切片的数量,标识和顺序.
- 拟议的模型显著提高了跨11个具有挑战性的基准的MILP解决效率.
- 与现有的启发式和基于学习的方法相比,表现出卓越的性能.
结论:
- HEM代表了第一个数据驱动的方法,同时优化MILP切割选择标准 (P1-P3).
- 层次模型为改善优化解决方案性能提供了一个强大的新方向.
- HEM显示了实际应用,包括在华为的现实问题上.
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