An Infeasible Primal-Dual Interior-Point Algorithm for Linearly Constrained Convex Optimization Based on A Parametric Kernel Function

被引：1
|
作者：
Wang, Guoqiang [1 ]
Wang, Baocun [2 ]
Fan, Qingduan [2 ]
机构：
[1] Shanghai Univ Engn Sci, Coll Vocat Technol, Shanghai 200437, Peoples R China
[2] Shanghai Univ Engn Sci, Coll Fundamental Studies, Shanghai 200336, Peoples R China
来源：
INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL SCIENCES AND OPTIMIZATION, VOL 2, PROCEEDINGS | 2009年
关键词：
Linearly constrained convex optimization; Primal-dual methods; Interior-point algorithm;
D O I：
10.1109/CSO.2009.156
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we present an infeasible primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric kernel function, with parameters p is an element of [0, 1] and q >= 1. Numerical test shows that the efficiency of the proposed algorithm and investigates the behavior of the algorithm with different parameters p, q and theta.
引用
收藏
页码：900 / +
页数：2
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