-

 

 

 




Optimization Online





 

Approximating Stationary Points of Stochastic Mathematical Programs with Equilibrium Constraints via Sample Averaging

Huifu Xu (h.xu***at***soton.ac.uk)
Jane J. Ye (janeye***at***uvic.ca)

Abstract: We investigate sample average approximation of a general class of one-stage stochastic mathematical programs with equilibrium constraints. By using graphical convergence of unbounded set-valued mappings, we demonstrate almost sure convergence of a sequence of stationary points of sample average approximation problems to their true counterparts as the sample size increases. In particular we show the convergence of M(Mordukhovich)-stationary point and C(Clarke)-stationary point of the sample average approximation problem to those of the true problem. The research complements the existing work in the literature by considering a general constraint to be represented by a stochastic generalized equation and exploiting graphical convergence of coderivative mappings.

Keywords: SMPEC, coderivative, graphical convergence, M-stationary point, C-stationary point, sample average approximation

Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 2: Stochastic Programming

Category 3: Complementarity and Variational Inequalities

Citation:

Download: [Postscript][PDF]

Entry Submitted: 08/11/2010
Entry Accepted: 08/11/2010
Entry Last Modified: 08/25/2010

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society