IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v90y1999i0p87-12910.1023-a1018977102079.html
   My bibliography  Save this article

Scalable parallel computations forlarge-scale stochastic programming

Author

Listed:
  • H. Vladimirou
  • S.A. Zenios

Abstract

Stochastic programming provides an effective framework for addressing decision problemsunder uncertainty in diverse fields. Stochastic programs incorporate many possiblecontingencies so as to proactively account for randomness in their input data; thus, theyinevitably lead to very large optimization programs. Consequently, efficient algorithms thatcan exploit the capabilities of advanced computing technologies ‐ including multiprocessorcomputers ‐ become imperative to solve large‐scale stochastic programs. This paper surveysthe state‐of‐the‐art in parallel algorithms for stochastic programming. Algorithms are reviewed,classified and compared. Qualitative comparisons are based on the applicability, scope, easeof implementation, robustness and reliability of each algorithm, while quantitative comparisonsare based on the computational performance of algorithmic implementations onmultiprocessor systems. Emphasis is placed on the potential of parallel algorithms to solvelarge‐scale stochastic programs. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • H. Vladimirou & S.A. Zenios, 1999. "Scalable parallel computations forlarge-scale stochastic programming," Annals of Operations Research, Springer, vol. 90(0), pages 87-129, January.
    • Handle: RePEc:spr:annopr:v:90:y:1999:i:0:p:87-129:10.1023/a:1018977102079
    DOI: 10.1023/A:1018977102079
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018977102079
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018977102079?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. Jacek Gondzio & Andreas Grothey, 2007. "Parallel interior-point solver for structured quadratic programs: Application to financial planning problems," Annals of Operations Research, Springer, vol. 152(1), pages 319-339, July.
    2. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.
    3. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2008. "A dynamic stochastic programming model for international portfolio management," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1501-1524, March.
    4. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
    5. Miles Lubin & J. Hall & Cosmin Petra & Mihai Anitescu, 2013. "Parallel distributed-memory simplex for large-scale stochastic LP problems," Computational Optimization and Applications, Springer, vol. 55(3), pages 571-596, July.

    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:90:y:1999:i:0:p:87-129:10.1023/a:1018977102079. 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.