Please use this identifier to cite or link to this item: https://doi.org/10.1137/050638242
Title: A regularized sample average approximation method for stochastic mathematical programs with nonsmooth equality constraints
Authors: Meng, F. 
Xu, H.
Keywords: Convergence of stationary points
Karush-Kuhn-Tucker conditions
P0-variational inequality
Regularization methods
Sample average approximation
Issue Date: 2006
Citation: Meng, F., Xu, H. (2006). A regularized sample average approximation method for stochastic mathematical programs with nonsmooth equality constraints. SIAM Journal on Optimization 17 (3) : 891-919. ScholarBank@NUS Repository. https://doi.org/10.1137/050638242
Abstract: We investigate a class of two stage stochastic programs where the second stage problem is subject to nonsmooth equality constraints parameterized by the first stage variant and a random vector. We consider the case when the parametric equality constraints have more than one solution. A regularization method is proposed to deal with the multiple solution problem, and a sample average approximation method is proposed to solve the regularized problem. We then investigate the convergence of stationary points of the regularized sample average approximation programs as the sample size increases. The established results are applied to stochastic mathematical programs with P 0-variational inequality constraints. Preliminary numerical results are reported. © 2006 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/112987
ISSN: 10526234
DOI: 10.1137/050638242
Appears in Collections:Staff Publications

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