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Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies

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Book cover Stochastic Algorithms: Foundations and Applications (SAGA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5792))

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Abstract

We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible decision policy, synthesized by a strategy relying on any scenario tree approximation from stochastic programming and on supervised learning techniques from machine learning.

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Author information

Authors and Affiliations

  1. University of Liège, Systems and Modeling, B28, B-4000, Liège, Belgium

    Boris Defourny, Damien Ernst & Louis Wehenkel

Authors
  1. Boris Defourny
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  2. Damien Ernst
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  3. Louis Wehenkel
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Editor information

Editors and Affiliations

  1. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, W8-25, 152-8552, Tokyo, Japan

    Osamu Watanabe

  2. Division of Computer Science, N-14, W-9, Hokkaido University, 060-0814, Sapporo, Japan

    Thomas Zeugmann

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© 2009 Springer-Verlag Berlin Heidelberg

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Defourny, B., Ernst, D., Wehenkel, L. (2009). Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies. In: Watanabe, O., Zeugmann, T. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2009. Lecture Notes in Computer Science, vol 5792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04944-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-04944-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04943-9

  • Online ISBN: 978-3-642-04944-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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