IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v25y1977i1p173-177.html
   My bibliography  Save this article

Technical Note—Minimax Procedure for a Class of Linear Programs under Uncertainty

Author

Listed:
  • R. Jagannathan

    (University of Iowa, Iowa City, Iowa)

Abstract

We consider a linear programming problem with random a ij and b i elements that have known (finite) mean and variance, but whose distribution functions are otherwise unspecified. A minimax solution of the stochastic programming model is obtained by solving an equivalent deterministic convex programming problem. We derive these deterministic equivalents under different assumptions regarding the stochastic nature of the random parameters.

Suggested Citation

  • R. Jagannathan, 1977. "Technical Note—Minimax Procedure for a Class of Linear Programs under Uncertainty," Operations Research, INFORMS, vol. 25(1), pages 173-177, February.
    • Handle: RePEc:inm:oropre:v:25:y:1977:i:1:p:173-177
    DOI: 10.1287/opre.25.1.173
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.25.1.173
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.25.1.173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.
    2. Woonghee Tim Huh & Paat Rusmevichientong, 2014. "Online Sequential Optimization with Biased Gradients: Theory and Applications to Censored Demand," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 150-159, February.
    3. Steffen Rebennack, 2022. "Data-driven stochastic optimization for distributional ambiguity with integrated confidence region," Journal of Global Optimization, Springer, vol. 84(2), pages 255-293, October.
    4. Cai, Jun & Liu, Fangda & Yin, Mingren, 2024. "Worst-case risk measures of stop-loss and limited loss random variables under distribution uncertainty with applications to robust reinsurance," European Journal of Operational Research, Elsevier, vol. 318(1), pages 310-326.
    5. Ch. Pflug, Georg & Timonina-Farkas, Anna & Hochrainer-Stigler, Stefan, 2017. "Incorporating model uncertainty into optimal insurance contract design," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 68-74.
    6. Kyungchul Park & Kyungsik Lee, 2016. "Distribution-robust single-period inventory control problem with multiple unreliable suppliers," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 949-966, October.
    7. Ioana Popescu, 2007. "Robust Mean-Covariance Solutions for Stochastic Optimization," Operations Research, INFORMS, vol. 55(1), pages 98-112, February.
    8. Liu, Haiyan & Mao, Tiantian, 2022. "Distributionally robust reinsurance with Value-at-Risk and Conditional Value-at-Risk," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 393-417.
    9. Woonghee Tim Huh & Paat Rusmevichientong, 2009. "A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 103-123, February.
    10. Jun Cai & Zhanyi Jiao & Tiantian Mao, 2024. "Worst-case values of target semi-variances with applications to robust portfolio selection," Papers 2410.01732, arXiv.org, revised Oct 2024.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:25:y:1977:i:1:p:173-177. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.