A new primal-dual path-following interior-point algorithm for semidefinite optimization

被引：57
|
作者：
Wang, G. Q. [1 ,2 ]
Bai, Y. Q. [2 ]
机构：
[1] Shanghai Univ Engn Sci, Coll Vocat Technol, Shanghai 200437, Peoples R China
[2] Shanghai Univ, Coll Sci, Dept Math, Shanghai 200444, Peoples R China
来源：
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS | 2009年 / 353卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Semidefinite optimization; Interior-point algorithm; Small-update method; Iteration bound; SEARCH DIRECTIONS; LARGE-UPDATE; CONVERGENCE;
D O I：
10.1016/j.jmaa.2008.12.016
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper we present a new primal-dual path-following interior-point algorithm for semidefinite optimization. The algorithm is based on a new technique for finding the search direction and the strategy of the central path. At each iteration, we use only full Nesterov-Todd step. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, 0(root n log n/epsilon), which is as good as the linear analogue. (C) 2008 Elsevier Inc. All rights reserved.
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页码：339 / 349
页数：11
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