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    Robust Stochastic Optimization With Convex Risk Measures: A Discretized Subgradient Scheme

    Access Status
    Fulltext not available
    Authors
    Yu, H.
    Sun, Jie
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yu, H. and Sun, J. 2021. Robust Stochastic Optimization With Convex Risk Measures: A Discretized Subgradient Scheme. Journal of Industrial and Management Optimization. 17 (1): pp. 81-99.
    Source Title
    Journal of Industrial and Management Optimization
    DOI
    10.3934/jimo.2019100
    ISSN
    1547-5816
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160102819
    URI
    http://hdl.handle.net/20.500.11937/90790
    Collection
    • Curtin Research Publications
    Abstract

    We study the distributionally robust stochastic optimization problem within a general framework of risk measures, in which the ambiguity set is described by a spectrum of practically used probability distribution constraints such as bounds on mean-deviation and entropic value-at-risk. We show that a subgradient of the objective function can be obtained by solving a Finite-dimensional optimization problem, which facilitates subgradient-type algorithms for solving the robust stochastic optimization problem. We develop an algorithm for two-stage robust stochastic programming with conditional value at risk measure. A numerical example is presented to show the effectiveness of the proposed method.

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