二元节包多面体的两组不等式

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED
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引用次数: 0

摘要

本文介绍了两组不等式,这是一类适用于knapsack和多重knapsack问题的有效不等式。两组不等式由来自一个背包约束的两个任意变量集生成。这一类切割平面不是传统的提升类型,因为开始时并不需要限制空间上的有效不等式。此外,它们无法使用任何现有的提升技术进行推导。本文提出了一种四元算法,可以高效地生成许多二集不等式。同时还推导出了面定义二集不等式的条件。计算实验测试了这些不等式作为预处理切分与 CPLEX(一种高性能数学编程求解器)在默认设置下的对比。总体而言,双集不等式将解决某些基准多重背包实例的时间缩短了 80%。计算结果还显示了这一新型切割平面在解决具有计算挑战性的二进制整数程序方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-set inequalities for the binary knapsack polyhedra

This paper presents two-set inequalities, a class of valid inequalities for knapsack and multiple knapsack problems. Two-set inequalities are generated from two arbitrary sets of variables from a knapsack constraint. This class of cutting planes is not a traditional type of lifting since a valid inequality over a restricted space is not required to start. Furthermore, they cannot be derived using any existing lifting technique. The paper presents a quadratic algorithm to efficiently generate many two-set inequalities. Conditions for facet-defining two-set inequalities are also derived. Computational experiments tested these inequalities as pre-processing cuts versus CPLEX, a high-performance mathematical programming solver, at default settings. Overall, two-set inequalities reduced the time to solve some benchmark multiple knapsack instances to up to 80%. Computational results also showed the potential of this new class of cutting planes to solve computationally challenging binary integer programs.

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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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