.png){kind=icon}
返回
Learning to select cuts for efficient mixed-integer programming
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2021.108353.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in the early stage, thus largely reducing the computational burden without hurting the solution accuracy. However, the major cut selection approaches heavily rely on heuristics, which strongly depend on the specific problem at hand and thus limit their generalization capability. In this paper, we propose a data-driven and generalizable cut selection approach, named CUT RANKING , in the settings of multiple instance learning. To measure the quality of the candidate cuts, a scoring function, which takes the instance-specific cut features as inputs, is trained and applied in cut ranking and selection. In order to evaluate our method, we conduct extensive experiments on both synthetic datasets and real-world datasets. Compared with commonly used heuristics for cut selection, the learning-based policy has shown to be more effective, and is capable of generalizing over multiple problems with different properties. CUT RANKING has been deployed in an industrial solver for large-scale MIPs. In the online A/B testing of the product planning problems with more than 10 7 variables and constraints daily, CUT RANKING has achieved the average speedup ratio of 12.42% over the production solver without any accuracy loss of solution. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Mixed-Integer programming
Cutting plane
Multiple instance learning
Generalization ability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.4W
被引数:
4.5W
机构
引用论文
Multiple instance learning: A survey of problem characteristics and applications多实例学习: 问题特征与应用综述.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problemPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容