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

    Access Status
    Fulltext not available
    Authors
    Meng, F.
    Sun, Jie
    Goh, M.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Meng, F. and Sun, J. and Goh, M. 2011. A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure. Computational Optimization and Applications. 50: pp. 379-401.
    Source Title
    Computational Optimization and Applications
    DOI
    10.1007/s10589-010-9328-4
    ISSN
    09266003
    URI
    http://hdl.handle.net/20.500.11937/39234
    Collection
    • Curtin Research Publications
    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, 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.

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