TY - JOUR
T1 - A Feasible Semismooth Gauss-Newton Method for Solving a Class of SLCPs
AU - Changfeng Ma
JO - Journal of Computational Mathematics
VL - 2
SP - 197
EP - 222
PY - 2012
DA - 2012/04
SN - 30
DO - http://doi.org/10.4208/jcm.1107-m3559
UR - https://global-sci.org/intro/article_detail/jcm/8425.html
KW - Stochastic linear complementarity problems, Gauss-Newton algorithm, Convergence analysis, Numerical results.
AB - <p style="text-align: justify;">In this paper, we consider a class of the stochastic linear complementarity problems (SLCPs) with finitely many elements. A feasible semismooth damped Gauss-Newton algorithm for the SLCP is proposed. The global and local 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 algorithm.</p>