IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v85y1999i0p173-19310.1023-a1018917710373.html
   My bibliography  Save this article

Statistical approximations forstochastic linear programming problems

Author

Listed:
  • Julia Higle
  • Suvrajeet Sen

Abstract

Sampling and decomposition constitute two of the most successful approaches foraddressing large‐scale problems arising in statistics and optimization, respectively. In recentyears, these two approaches have been combined for the solution of large‐scale stochasticlinear programming problems. This paper presents the algorithmic motivation for suchmethods, as well as a broad overview of issues in algorithm design. We discuss both basicschemes as well as computational enhancements and stopping rules. We also introduce ageneralization of current algorithms to handle problems with random recourse. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • Julia Higle & Suvrajeet Sen, 1999. "Statistical approximations forstochastic linear programming problems," Annals of Operations Research, Springer, vol. 85(0), pages 173-193, January.
    • Handle: RePEc:spr:annopr:v:85:y:1999:i:0:p:173-193:10.1023/a:1018917710373
    DOI: 10.1023/A:1018917710373
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018917710373
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018917710373?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Harsha Gangammanavar & Yifan Liu & Suvrajeet Sen, 2021. "Stochastic Decomposition for Two-Stage Stochastic Linear Programs with Random Cost Coefficients," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 51-71, January.
    2. Suvrajeet Sen & Yifan Liu, 2016. "Mitigating Uncertainty via Compromise Decisions in Two-Stage Stochastic Linear Programming: Variance Reduction," Operations Research, INFORMS, vol. 64(6), pages 1422-1437, December.

    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:spr:annopr:v:85:y:1999:i:0:p:173-193:10.1023/a:1018917710373. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.