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Stochastic Programming 84 Part II pp 1–28Cite as

Algorithms for stochastic programs: The case of nonstochastic tenders

Part of the Mathematical Programming Studies book series (MATHPROGRAMM,volume 28)

Abstract

We consider solution strategies for stochastic programs whose deterministic equivalent programs take on the form: Find x∈ℝn, χ∈ℝm such that x≥0, Ax=b, Tx=χ and z=cx+Ψ(χ) is minimized. We suggest algorithms based upon (i) extensions of the revised simplex method, (ii) inner approximations (generalized programming techniques), (iii) outer approximations (min-max) strategies.

Key words

  • Stochastic Programs with Recourse
  • Generalized Programming
  • Nonstochastic Tenders
  • Inner Linearization

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Author information

Authors and Affiliations

  1. International Institute for Applied Systems Analysis, A-2361, Laxenburg, Austria

    J. L. Nazareth

  2. Department of Mathematics, University of California at Davis, 95616, CA, USA

    R. J.-B. Wets

Authors
  1. J. L. Nazareth
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  2. R. J.-B. Wets
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Editor information

Andras Prékopa Roger J.- B. Wets

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© 1986 The Mathematical Programming Society, Inc.

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Nazareth, J.L., Wets, R.JB. (1986). Algorithms for stochastic programs: The case of nonstochastic tenders. In: Prékopa, A., Wets, R.J.B. (eds) Stochastic Programming 84 Part II. Mathematical Programming Studies, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0121123

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  • DOI: https://doi.org/10.1007/BFb0121123

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00926-6

  • Online ISBN: 978-3-642-00927-3

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