Kernel Function-Based Primal-Dual Interior-Point Methods for Symmetric Cones Optimization

被引：0

作者：

ZHAO Dequan ^{[1
]}

ZHANG Mingwang ^{[1
]}

机构：

[1] College of Science, China Three Gorges University

来源：

Wuhan University Journal of Natural Sciences | 2014年 / 19卷 / 06期

关键词：

symmetric cones optimization; Kernel function; Interior-point method; polynomial complexity;

D O I：

暂无

中图分类号：

O174.13 [凸函数、凸集理论];

学科分类号：

070104 ;

摘要：

In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure between the iterate and the center path. The kernel function is neither a self-regular function nor the usual logarithmic kernel function. Besides, by using Euclidean Jordan algebraic techniques, we achieve the favorable iteration complexity O( r1/2（log r）2 log（r/ ε）), which is as good as the convex quadratic semi-definite optimization analogue.

引用

页码：461 / 468

页数：8

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