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 / 2018卷
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.
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页数：24
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