Nonsmooth Levenberg-Marquardt Type Method for Solving a Class of Stochastic Linear Complementarity Problems with Finitely Many Elements

被引：2

作者：

Liu, Zhimin ^{[1
]}

Du, Shouqiang ^{[1
]}

Wang, Ruiying ^{[1
]}

机构：

[1] Qingdao Univ, Sch Math & Stat, 308 Qingdao Ningxia Rd, Qingdao 266071, Peoples R China

来源：

ALGORITHMS | 2016年 / 9卷 / 04期

基金：

中国国家自然科学基金;

关键词：

nonsmooth equations; stochastic linear complementarity problems; global convergence; Levenberg-Marquardt-type method;

D O I：

10.3390/a9040083

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible nonsmooth Levenberg-Marquardt-type method. The global convergence properties of the nonsmooth Levenberg-Marquardt-type method are also presented. Finally, the related numerical results illustrate that the proposed method is efficient for the related refinery production problem and the large-scale stochastic linear complementarity problems.

引用

页数：17

共 29 条

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