Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10589-010-9328-4
Title: A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure
Authors: Meng, F. 
Sun, J. 
Goh, M. 
Keywords: Conditional value-at-risk
Sample average approximation
Smoothing method
Stochastic optimization
Issue Date: Oct-2011
Source: Meng, F., Sun, J., Goh, M. (2011-10). A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure. Computational Optimization and Applications 50 (2) : 379-401. ScholarBank@NUS Repository. https://doi.org/10.1007/s10589-010-9328-4
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 ε, 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. © 2010 Springer Science+Business Media, LLC.
Source Title: Computational Optimization and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/53387
ISSN: 09266003
DOI: 10.1007/s10589-010-9328-4
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