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Mixed-Integer Value Functions in Stochastic Programming

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Combinatorial Optimization — Eureka, You Shrink!

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2570))

Abstract

We discuss the role of mixed-integer value functions in the theoretical analysis of stochastic integer programs. It is shown how the interaction of value function properties with basic results from probability theory leads to structural statements in stochastic integer programming.

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Author information

Authors and Affiliations

  1. Institute of Mathematics, Gerhard-Mercator University Duisburg, Lotharstr. 65, D-47048, Duisburg, Germany

    Rüdiger Schultz

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  1. Rüdiger Schultz
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Editor information

Editors and Affiliations

  1. Institut für Informatik, Universität zu Köln, Pohligstr. 1, 50969, Köln, Germany

    Michael Jünger

  2. Institut für Informatik, Universität Heidelberg, Im Neuenheimer Feld 368, 69120, Heidelberg, Germany

    Gerhard Reinelt

  3. Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, CNR, viale Manzoni 30, 00185, Rome, Italy

    Giovanni Rinaldi

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Schultz, R. (2003). Mixed-Integer Value Functions in Stochastic Programming. In: Jünger, M., Reinelt, G., Rinaldi, G. (eds) Combinatorial Optimization — Eureka, You Shrink!. Lecture Notes in Computer Science, vol 2570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36478-1_16

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  • DOI: https://doi.org/10.1007/3-540-36478-1_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00580-3

  • Online ISBN: 978-3-540-36478-8

  • eBook Packages: Springer Book Archive

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