Stronger linear programming relaxations of max-cut

被引：0

作者：

David Avis

Jun Umemoto

机构：

[1] Mcgill University and GERAD,Computer Science

[2] Kyoto University,Graduate School of Informatics

来源：

Mathematical Programming | 2003年 / 97卷

关键词：

Linear Programming Relaxation; Sparse Graph; Dense Graph; Random Sparse Graph; Integrality Ratio;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We consider linear programming relaxations for the max cut problem in graphs, based on k-gonal inequalities. We show that the integrality ratio for random dense graphs is asymptotically 1+1/k and for random sparse graphs is at least 1+3/k. There are O(nk)k-gonal inequalities. These results generalize work by Poljak and Tuza, who gave similar results for k=3.

引用

页码：451 / 469

页数：18

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