Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/115720
Title: Exponential convergence of sample average approximation methods for a class of stochastic mathematical programs with complementarity constraints
Authors: Meng, F.-W. 
Xu, H.-F.
Keywords: Exponential convergence
Sample average approximation
Stochastic mathematical programs with complementarity constraints
Weak stationary points
Issue Date: Nov-2006
Citation: Meng, F.-W.,Xu, H.-F. (2006-11). Exponential convergence of sample average approximation methods for a class of stochastic mathematical programs with complementarity constraints. Journal of Computational Mathematics 24 (6) : 733-748. ScholarBank@NUS Repository.
Abstract: In this paper, we propose a Sample Average Approximation (SAA) method for a class of Stochastic Mathematical Programs with Complementarity Constraints (SMPCC) recently considered by Birbil, Gürkan and Listes [3]. We study the statistical properties of obtained SAA estimators. In particular we show that under moderate conditions a sequence of weak stationary points of SAA programs converge to a weak stationary point of the true problem with probability approaching one at exponential rate as the sample size tends to infinity. To implement the SAA method more efficiently, we incorporate the method with some techniques such as Scholtes' regularization method and the well known smoothing NCP method. Some preliminary numerical results are reported.
Source Title: Journal of Computational Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/115720
ISSN: 02549409
Appears in Collections:Staff Publications

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