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dc.contributor.authorMeng, F.
dc.contributor.authorSun, Jie
dc.contributor.authorGoh, M.
dc.date.accessioned2017-01-30T14:31:46Z
dc.date.available2017-01-30T14:31:46Z
dc.date.created2014-09-02T20:01:17Z
dc.date.issued2011
dc.identifier.citationMeng, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39234
dc.identifier.doi10.1007/s10589-010-9328-4
dc.description.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.

dc.publisherSpringer, Van Godewijckstraat
dc.titleA smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure
dc.typeJournal Article
dcterms.source.volume50
dcterms.source.startPage379
dcterms.source.endPage401
dcterms.source.issn09266003
dcterms.source.titleComputational Optimization and Applications
curtin.accessStatusFulltext not available


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