Nonsmooth Levenberg-Marquardt type method for solving a class of stochastic linear complementarity problems with finitely many elements
type: Item
instance of: scholarly article
title: Nonsmooth Levenberg-Marquardt type method for solving a class of stochastic linear complementarity problems with finitely many elements
zbMATH Open document ID: 1461.90084
author: Zhi-Min Liu
author: Shouqiang Du
author: Ruiying Wang
published in: Algorithms
publication date: 2019-03-26T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C33
Mathematics Subject Classification ID: 65K15
zbMATH DE Number: 7042392
zbMATH Keywords: nonsmooth equations
zbMATH Keywords: stochastic linear complementarity problems
zbMATH Keywords: global convergence
zbMATH Keywords: Levenberg-Marquardt-type method
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a9040083
OpenAlex ID: W2560101520
cites work: Sample-path solution of stochastic variational inequalities
cites work: Expected Residual Minimization Method for Stochastic Linear Complementarity Problems
cites work: The \(SC^1\) 1property of an expected residual function arising from stochastic complementarity problems
cites work: Solving stochastic complementarity problems in energy market modeling using scenario reduction
cites work: Finite-Dimensional Variational Inequalities and Complementarity Problems
cites work: Q4003375
cites work: Stochastic nonlinear complementarity problems: stochastic programming reformulation and penalty-based approximation method
cites work: Convergence results of the ERM method for nonlinear stochastic variational inequality problems
cites work: Expected residual minimization method for stochastic variational inequality problems
cites work: Robust solution of monotone stochastic linear complementarity problems
cites work: Stochastic nonlinear complementarity problem and applications to traffic equilibrium under uncertainty
cites work: Feasible semismooth Newton method for a class of stochastic linear complementarity problems
cites work: Q4309474
cites work: A Feasible Semismooth Gauss-Newton Method for Solving a Class of SLCPs
cites work: On a semismooth least squares formulation of complementarity problems with gap reduction
cites work: A family of NCP functions and a descent method for the nonlinear complementarity problem
cites work: The convergence of a smoothing damped Gauss-Newton method for nonlinear complementarity problem
cites work: The semismooth-related properties of a merit function and a descent method for the nonlinear complementarity problem
cites work: ON SOME NCP-FUNCTIONS BASED ON THE GENERALIZED FISCHER–BURMEISTER FUNCTION
cites work: Projected filter trust region methods for a semismooth least squares formulation of mixed complementarity problems
cites work: A nonsmooth inexact Newton method for the solution of large-scale nonlinear complementarity problems
cites work: A nonsmooth version of Newton's method
cites work: A new class of penalized NCP-functions and its properties
cites work: Feasible descent algorithms for mixed complementarity problems
cites work: Global Convergence Properties of Some Iterative Methods for Linear Complementarity Problems
cites work: Some Noninterior Continuation Methods for Linear Complementarity Problems
cites work: The global linear convergence of an infeasible non-interior path-following algorithm for complementarity problems with uniform \(P\)-functions
cites work: The global linear convergence of a noninterior path-following algorithm for linear complementarity problems
cites work: A new smoothing and regularization Newton method for \(P_{0}\)-NCP
DOI: 10.3390/A9040083
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description: scientific article; zbMATH DE number 7042392