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    A model of distributionally robust two-stage stochastic convex programming with linear recourse

    Access Status
    Open access via publisher
    Authors
    Li, Bin
    Qian, X.
    Sun, Jie
    Teo, Kok Lay
    Yu, C.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, B. and Qian, X. and Sun, J. and Teo, K.L. and Yu, C. 2018. A model of distributionally robust two-stage stochastic convex programming with linear recourse. Applied Mathematical Modelling. 58: pp. 86-97.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.11.039
    ISSN
    0307-904X
    School
    Department of Mathematics and Statistics
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160102819
    URI
    http://hdl.handle.net/20.500.11937/67449
    Collection
    • Curtin Research Publications
    Abstract

    We consider distributionally robust two-stage stochastic convex programming problems, in which the recourse problem is linear. Other than analyzing these new models case by case for different ambiguity sets, we adopt a unified form of ambiguity sets proposed by Wiesemann, Kuhn and Sim, and extend their analysis from a single stochastic constraint to the two-stage stochastic programming setting. It is shown that under a standard set of regularity conditions, this class of problems can be converted to a conic optimization problem. Numerical results are presented to show the efficiency of the distributionally robust approach.

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