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A New Conjugate Gradient Projection Method for Solving Stochastic Generalized Linear Complementarity Problems 被引量:2
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作者 Zhimin Liu Shouqiang Du Ruiying Wang 《Journal of Applied Mathematics and Physics》 2016年第6期1024-1031,共8页
In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient proje... In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient projection method is given for solving the stochastic generalized linear complementarity problems. The global convergence of the conjugate gradient projection method is proved and the related numerical results are also reported. 展开更多
关键词 stochastic Generalized linear complementarity problems Fischer-Burmeister Function Conjugate Gradient Projection Method Global Convergence
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A FEASIBLE SEMISMOOTH GAUSS-NEWTON METHOD FOR SOLVING A CLASS OF SLCPS
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作者 Changfeng Ma 《Journal of Computational Mathematics》 SCIE CSCD 2012年第2期197-222,共26页
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 an... 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. 展开更多
关键词 stochastic linear complementarity problems Gauss-Newton algorithm Con-vergence analysis Numerical results.
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