Complexity analysis and numerical implementation of primal-dual interior-point methods for convex quadratic optimization based on a finite barrier

被引：27
|
作者：
Cai, Xinzhong [1 ]
Wang, Guoqiang [2 ]
Zhang, Zihou [2 ]
机构：
[1] Shanghai Univ Engn Sci, Coll Adv Vocat Technol, Shanghai 200437, Peoples R China
[2] Shanghai Univ Engn Sci, Coll Fundamental Studies, Shanghai 201620, Peoples R China
来源：
NUMERICAL ALGORITHMS | 2013年 / 62卷 / 02期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Convex quadratic optimization; Interior-point methods; Kernel function; Large- and small-update methods; Iteration bound; ALGORITHMS;
D O I：
10.1007/s11075-012-9581-y
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we present primal-dual interior-point methods for convex quadratic optimization based on a finite barrier, which has been investigated earlier for the case of linear optimization by Bai et al. (SIAM J Optim 13(3):766-782, 2003). By means of the feature of the finite kernel function, we study the complexity analysis of primal-dual interior-point methods based on the finite barrier and derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods, namely, and , respectively, which are as good as the linear optimization analogue. Numerical tests demonstrate the behavior of the algorithms with different parameters.
引用
收藏
页码：289 / 306
页数：18
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