Solving large-scale sparse semidefinite programs for combinatorial optimization

被引：179
|
作者：
Benson, SJ [3 ]
Ye, YY
Zhang, X
机构：
[1] Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA
[2] Huazhong Univ Sci & Technol, Sch Mech Engn, Wuhan 430074, Hubei, Peoples R China
[3] Univ Iowa, Computat Optimizat Lab, Iowa City, IA 52242 USA
来源：
SIAM JOURNAL ON OPTIMIZATION | 2000年 / 10卷 / 02期
关键词：
semidefinite programming; dual potential reduction algorithm; maximum cut problem;
D O I：
10.1137/S1052623497328008
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We present a dual-scaling interior-point algorithm and show how it exploits the structure and sparsity of some large-scale problems. We solve the positive semidefinite relaxation of combinatorial and quadratic optimization problems subject to boolean constraints. We report the first computational results of interior-point algorithms for approximating maximum cut semidefinite programs with dimension up to 3,000.
引用
收藏
页码：443 / 461
页数：19
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