An alternating direction method for solving convex nonlinear semidefinite programming problems

被引：15
|
作者：
Zhang, Su [1 ]
Ang, James [2 ]
Sun, Jie [2 ,3 ]
机构：
[1] Nankai Univ, Inst Modern Management, Sch Business, Tianjin, Peoples R China
[2] Natl Univ Singapore, Dept Decis Sci, Singapore 119245, Singapore
[3] Natl Univ Singapore, Risk Management Inst, Singapore 119245, Singapore
来源：
OPTIMIZATION | 2013年 / 62卷 / 04期
关键词：
alternating direction method; convex semidefinite programming; matrix completion; variational inequalities; 65L20; 65K10; 90C25; MAXIMUM EIGENVALUE FUNCTIONS; SMOOTHING NEWTON METHOD; SPECTRAL BUNDLE METHODS; CUTTING PLANE METHOD; P-PROPERTIES; OPTIMIZATION;
D O I：
10.1080/02331934.2011.611883
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
An alternating direction method is proposed for solving convex semidefinite optimization problems. This method only computes several metric projections at each iteration. Convergence analysis is presented and numerical experiments in solving matrix completion problems are reported.
引用
收藏
页码：527 / 543
页数：17
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