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    Quadratic two-stage stochastic optimization with coherent measures of risk

    251418.pdf (311.1Kb)
    Access Status
    Open access
    Authors
    Sun, Jie
    Liao, L.
    Rodrigues, B.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Sun, J. and Liao, L. and Rodrigues, B. 2017. Quadratic two-stage stochastic optimization with coherent measures of risk. Mathematical Programming. 168 (1-2): pp. 599-613.
    Source Title
    Mathematical Programming
    DOI
    10.1007/s10107-017-1131-x
    ISSN
    0025-5610
    School
    Department of Mathematics and Statistics
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160102819
    Remarks

    The final publication is available at Springer via http://dx.doi.org/10.1007/s10107-017-1131-x

    URI
    http://hdl.handle.net/20.500.11937/52156
    Collection
    • Curtin Research Publications
    Abstract

    A new scheme to cope with two-stage stochastic optimization problems uses a risk measure as the objective function of the recourse action, where the risk measure is defined as the worst-case expected values over a set of constrained distributions. This paper develops an approach to deal with the case where both the first and second stage objective functions are convex linear-quadratic. It is shown that under a standard set of regularity assumptions, this two-stage quadratic stochastic optimization problem with measures of risk is equivalent to a conic optimization problem that can be solved in polynomial time.

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