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Dissection Methods for Solutions in Chance Constrained Programming Problems Under Discrete Distributions

Author

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  • William M. Raike

    (The University of Texas at Austin)

Abstract

Under the assumption of discrete distributions for the random variables involved, deterministic equivalent problems are derived for a general class of chance constrained (but not necessarily linear) programming problems. These permit the explicit solution of such problems for all or most types of optimal stochastic decision rules which are of interest, including optimal multistage rules and not restricted to the class of linear rules. The formulation given encompasses certain cases of stochastic programming with recourse, and the deterministic equivalents derived for these reduce to well-known versions available in the literature.

Suggested Citation

  • William M. Raike, 1970. "Dissection Methods for Solutions in Chance Constrained Programming Problems Under Discrete Distributions," Management Science, INFORMS, vol. 16(11), pages 708-715, July.
    • Handle: RePEc:inm:ormnsc:v:16:y:1970:i:11:p:708-715
    DOI: 10.1287/mnsc.16.11.708
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    Cited by:

    1. Martin Branda, 2016. "Mean-value at risk portfolio efficiency: approaches based on data envelopment analysis models with negative data and their empirical behaviour," 4OR, Springer, vol. 14(1), pages 77-99, March.
    2. Singer, Nico, 2011. "A behavioral portfolio analysis of retirement portfolios," Thuenen-Series of Applied Economic Theory 104, University of Rostock, Institute of Economics.
    3. Youssouf A. F. Toukourou & Franc{c}ois Dufresne, 2015. "ON Integrated Chance Constraints in ALM for Pension Funds," Papers 1503.05343, arXiv.org.
    4. Martin Branda, 2013. "On relations between chance constrained and penalty function problems under discrete distributions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(2), pages 265-277, April.
    5. Lukáš Adam & Martin Branda, 2016. "Nonlinear Chance Constrained Problems: Optimality Conditions, Regularization and Solvers," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 419-436, August.

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