A PRIMAL-DUAL INTERIOR-POINT METHOD FOR THE SEMIDEFINITE PROGRAMMING PROBLEM BASED ON A NEW KERNEL FUNCTION

被引：2
|
作者：
Touil, Imene [1 ]
Benterki, Djamel [2 ]
机构：
[1] Mohammed Sedd Ben Yahia Univ, Dept Math, LMAM, Jijel, Algeria
[2] Ferhat Abbas Univ, Dept Math, LMEN, Setif, Algeria
来源：
JOURNAL OF NONLINEAR FUNCTIONAL ANALYSIS | 2019年 / 2019卷
关键词：
Linear semidefinite programming; Primal-dual interior point methods; Kernel function; Complexity analysis; Worst-case iteration bound; COMPLEXITY ANALYSIS; ALGORITHM;
D O I：
10.23952/jnfa.2019.25
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The purpose of this paper is to improve the complexity of a large-update primal-dual interior point method for a semidefinite programming problem. We define a proximity function for the semidefinite programming problem based on a new parametric kernel function and prove that the worst-case iteration bound for the new correspondent algorithm is O (root n (lnn)(pq+1/pq) ln n/epsilon), where p,q >= 1.
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收藏
页数：17
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