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Scenario Reduction Techniques in Stochastic Programming

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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

Stochastic programming problems appear as mathematical models for optimization problems under stochastic uncertainty. Most computational approaches for solving such models are based on approximating the underlying probability distribution by a probability measure with finite support. Since the computational complexity for solving stochastic programs gets worse when increasing the number of atoms (or scenarios), it is sometimes necessary to reduce their number. Techniques for scenario reduction often require fast heuristics for solving combinatorial subproblems. Available techniques are reviewed and open problems are discussed.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Humboldt-University Berlin, Rudower Chaussee 25, 12489, Berlin, Germany

    Werner Römisch

Authors
  1. Werner Römisch
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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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Römisch, W. (2009). Scenario Reduction Techniques in Stochastic Programming. 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_1

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  • DOI: https://doi.org/10.1007/978-3-642-04944-6_1

  • 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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