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.
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页码：451 / 469
页数：18
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