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Stochastic Programming Extensions

In: Pyomo – Optimization Modeling in Python

Author

Listed:
  • William E. Hart

    (Sandia National Laboratories)

  • Carl Laird

    (Texas A&M)

  • Jean-Paul Watson

    (Sandia National Laboratories)

  • David L. Woodruff

    (University of California, Davis)

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 writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, PySP includes an implementation of Rockefeller 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 (Python) and the embedding of the base deterministic model in that language (Pyomo), PySP provides completely generic and highly configurable solver implementations.

Suggested Citation

  • William E. Hart & Carl Laird & Jean-Paul Watson & David L. Woodruff, 2012. "Stochastic Programming Extensions," Springer Optimization and Its Applications, in: Pyomo – Optimization Modeling in Python, edition 127, chapter 0, pages 131-164, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-3226-5_9
    DOI: 10.1007/978-1-4614-3226-5_9
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