A FEASIBLE SEMISMOOTH GAUSS-NEWTON METHOD FOR SOLVING A CLASS OF SLCPS

In this paper, we consider a class of the stochastic linear complementarity problems （SLCPs） with finitely many elements. A feasible semismooth damped Gauss-Newton al- gorithm for the SLCP is proposed. The global and locally quadratic convergence of the proposed algorithm are obtained under suitable...

Full description

Saved in:
Bibliographic Details
Published inJournal of computational mathematics Vol. 30; no. 2; pp. 197 - 222
Main Author Ma, Changfeng
Format Journal Article
LanguageEnglish
Published Chinese Academy of Mathematices and System Sciences (AMSS) Chinese Academy of Sciences 01.03.2012
Subjects
Algorithms
Data smoothing
Gauss-Newton法
Iterative solutions
Mathematical complements
Mathematical functions
Mathematical inequalities
Mathematical vectors
Matrices
Newtons method
Variational inequalities
半光滑
局部二次收敛
数值结果
求解
算法理论
线性互补问题
退耕还林
Online AccessGet full text

Cover

More Information
Summary:In this paper, we consider a class of the stochastic linear complementarity problems （SLCPs） with finitely many elements. A feasible semismooth damped Gauss-Newton al- gorithm for the SLCP is proposed. The global and locally quadratic convergence of the proposed algorithm are obtained under suitable conditions. Some numerical results are reported in this paper, which confirm the good theoretical properties of the proposed al- gorithm.Mathematics subject classification： 90C33, 65K10.
Bibliography:Stochastic linear complementarity problems, Gauss-Newton algorithm, Con-vergence analysis, Numerical results.
In this paper, we consider a class of the stochastic linear complementarity problems （SLCPs） with finitely many elements. A feasible semismooth damped Gauss-Newton al- gorithm for the SLCP is proposed. The global and locally quadratic convergence of the proposed algorithm are obtained under suitable conditions. Some numerical results are reported in this paper, which confirm the good theoretical properties of the proposed al- gorithm.Mathematics subject classification： 90C33, 65K10.
11-2126/O1
ISSN:0254-9409
1991-7139
DOI:10.4208/jcm.1107-m3559