A smoothing Levenberg-Marquardt algorithm for semi-infinite programming

被引:10
作者
Jin, Ping [1 ]
Ling, Chen [1 ]
Shen, Huifei [1 ]
机构
[1] Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Peoples R China
基金
美国国家科学基金会;
关键词
Semi-infinite programming (SIP) problem; KKT system; Nonsmooth equations; Smoothing Levenberg-Marquardt algorithm; Convergence; INEQUALITY CONSTRAINED OPTIMIZATION; NEWTON-TYPE ALGORITHM; VARIATIONAL-INEQUALITIES; ERROR-BOUNDS; SQP METHOD; CONVERGENCE; DISCRETIZATION;
D O I
10.1007/s10589-014-9698-0
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
In this paper, we present a smoothing Levenberg-Marquardt algorithm for the solution of the semi-infinite programming (SIP) problem. We first reformulate the KKT system of SIP problem into a system of constrained nonsmooth equations. Then we solve this system by a smoothing Levenberg-Marquardt algorithm. The feasibility is ensured via the aggregated constraint, and at each iteration of the presented algorithm only a quadratic programming has to be solved. Global and local superlinear convergence of this algorithm is established under a local error bound condition, which is much weaker than the nonsingularity condition. Preliminary numerical results are reported.
引用
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页码:675 / 695
页数:21
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