AN INTERIOR-POINT ALGORITHM FOR SEMIDEFINITE OPTIMIZATION BASED ON A NEW PARAMETRIC KERNEL FUNCTION

被引：7

作者：

Fathi-Hafshejani, Sajad ^{[1
]}

Fakharzadeh, Alireza ^{[1
,2
]}

机构：

[1] Shiraz Univ Technol, Dept Math, POB 71555-313, Shiraz, Iran

[2] Fars Elites Fdn, POB 71966-98893, Shiraz, Iran

来源：

JOURNAL OF NONLINEAR FUNCTIONAL ANALYSIS | 2018年

关键词：

Kernel function; Semidefinite optimization; Primal-dual interior-point method; Large-update method;

D O I：

10.23952/jnfa.2018.14

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, an interior-point algorithm for Semidefinite Optimization (SDO) problems based on a new parametric kernel function is proposed. By means of some simple analysis tools, we prove that the primal-dual interior-point algorithm for solving SDO problems meets O (root lognlogn/epsilon), iteration complexity bound for large-update methods. Numerical results confirm that our new proposed kernel function is doing well in practice in comparison with some existing kernel functions in the literature.

引用

页数：24

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