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Guidelines for Computer Implementation

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Stochastic Decomposition

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 8))

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Abstract

The Stochastic Decomposition algorithms presented in previous chapters are designed to take computational advantage of the special structure of a two stage stochastic linear program with recourse. This structure resides in the large “core” of subproblem data that is common to all realizations of the random variable \(\tilde \omega \). To see this, recall that a two stage SLP may be stated as follows.

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References

  • Kall, R and J. Mayer [1992], A model management system for stochastic linear programming, in: System Modelling and Optimization, pp. 580–587 (Springer-Verlag).

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  • Mayer, J. [1995], Stochastic Linear Programming Algorithms: A comparison based on a model management system, Habilitationsschrift, University of Zurich, Zurich, Switzerland.

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  • Sen, S, J. Mai and J.L. Higle [1994], Solution of large scale stochastic programs with Stochastic Decomposition algorithms, in: Large Scale Optimization: State of the Art, W.W. Hager, D.W. Hearn and RM. Pardalos (eds.), Kluwer Academic Publishers, Boston, MA.

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© 1996 Springer Science+Business Media Dordrecht

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Higle, J.L., Sen, S. (1996). Guidelines for Computer Implementation. In: Stochastic Decomposition. Nonconvex Optimization and Its Applications, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4115-8_6

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  • DOI: https://doi.org/10.1007/978-1-4615-4115-8_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6845-8

  • Online ISBN: 978-1-4615-4115-8

  • eBook Packages: Springer Book Archive

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