A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure

Meng, F, Sun, J and Goh, M 2011, 'A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure', Computational Optimization and Applications, vol. 50, no. 2, pp. 379-401.


Document type: Journal Article
Collection: Journal Articles

Title A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure
Author(s) Meng, F
Sun, J
Goh, M
Year 2011
Journal name Computational Optimization and Applications
Volume number 50
Issue number 2
Start page 379
End page 401
Total pages 23
Publisher Springer
Abstract This paper is concerned with solving single CVaR and mixed CVaR minimization problems. A CHKS-type smoothing sample average approximation (SAA) method is proposed for solving these two problems, which retains the convexity and smoothness of the original problem and is easy to implement. For any fixed smoothing constant e, this method produces a sequence whose cluster points are weak stationary points of the CVaR optimization problems with probability one. This framework of combining smoothing technique and SAA scheme can be extended to other smoothing functions as well. Practical numerical examples arising from logistics management are presented to show the usefulness of this method.
Subject Applied Mathematics not elsewhere classified
Keyword(s) Conditional value-at-risk
Sample average approximation
Smoothing method
Stochastic optimization
DOI - identifier 10.1007/s10589-010-9328-4
Copyright notice © 2010 Springer Science+Business Media, LLC
ISSN 0926-6003
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