A smoothing Levenberg–Marquardt method for generalized semi-infinite programming

被引：0
|
作者：
Weiai Liu
Changyu Wang
机构：
[1] Tongji University,Department of Mathematics
[2] Qufu Normal University,Institute of Operations Research
来源：
Computational and Applied Mathematics | 2013年 / 32卷
关键词：
GSIP problem; FB function; Smoothing L–M method; Global convergence; Superlinear convergence; 90C34;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper is concerned with a numerical method for generalized semi-infinite programming problem. We first reformulate the generalized semi-infinite programming problem into an invariable-dimensional KKT system. Then by using perturbed Fischer–Burmeister function, we present a smoothing Levenberg–Marquardt method for solving this system of semismooth equations and show its global convergence under common conditions. Furthermore, the local superlinear convergence of the method is proved under local error bound condition. Finally, numerical results are given.
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页码：89 / 105
页数：16
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