IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-319-58821-6_10.html
   My bibliography  Save this book chapter

Stochastic Programming Extensions

In: Pyomo — Optimization Modeling in Python

Author

Listed:
  • William E. Hart

    (Sandia National Laboratories)

  • Carl D. Laird

    (Sandia National Laboratories)

  • Jean-Paul Watson

    (Sandia National Laboratories)

  • David L. Woodruff

    (University of California, Davis)

  • Gabriel A. Hackebeil

    (University of Michigan)

  • Bethany L. Nicholson

    (Sandia National Laboratories)

  • John D. Siirola

    (Sandia National Laboratories)

Abstract

This chapter describes PySP, a stochastic programming extension to Pyomo. PySP enables the expression of stochastic programming problems as extensions of deterministic models, which are often formulated first. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in Pyomo. Given these two models, PySP provides two paths for solving the corresponding stochastic program. The first alternative involves PySP writing the extensive form and invoking a standard deterministic solver. For more complex stochastic programs, PySP includes an implementation of Rockafellar and Wets’ Progressive Hedging algorithm, which provides an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language and the embedding of the base deterministic model in that language, PySP provides completely generic and highly configurable solver implementations.

Suggested Citation

  • William E. Hart & Carl D. Laird & Jean-Paul Watson & David L. Woodruff & Gabriel A. Hackebeil & Bethany L. Nicholson & John D. Siirola, 2017. "Stochastic Programming Extensions," Springer Optimization and Its Applications, in: Pyomo — Optimization Modeling in Python, edition 2, chapter 0, pages 165-199, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-58821-6_10
    DOI: 10.1007/978-3-319-58821-6_10
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spochp:978-3-319-58821-6_10. 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.