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Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms

In: Large Scale Optimization

Author

Listed:
  • Suvrajeet Sen

    (University of Arizona, SIE Department)

  • Jason Mai

    (University of Arizona, SIE Department)

  • Julia L. Higle

    (University of Arizona, SIE Department)

Abstract

Stochastic Decomposition (SD) is a randomized version of Benders’ decomposition for the solution of two stage stochastic linear programs with recourse. It combines a recursive sampling scheme within a decomposition-coordination framework in which the algorithm alternates between a master program and. a subprogram. The master program represents a piecewise linear approximation in which each cut is obtained by solving one linear subproblem, and then performing a series of updates based on previously generated outcomes. Using recursive updates, we devise an efficient computer implementation that allows us to address very large two stage stochastic programs with recourse. We report our computational experience with some very large stochastic programs that arise in aircraft fleet scheduling and telecommunications network planning.

Suggested Citation

  • Suvrajeet Sen & Jason Mai & Julia L. Higle, 1994. "Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms," Springer Books, in: W. W. Hager & D. W. Hearn & P. M. Pardalos (ed.), Large Scale Optimization, pages 388-410, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-3632-7_19
    DOI: 10.1007/978-1-4613-3632-7_19
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