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On Cross Decomposition for Mixed-Integer Programming

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Models, Methods and Decision Support for Management

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Abstract

Primal and dual decomposition procedures can lead – dependent on the structure of the problem – to efficient procedures for solving mixed—integer programming problems. Benders’ decomposition takes advantage of the primal structure of a problem by temporarily fixing the aggravating integer variables, while dual structures can be exploited by relaxing the complicating constraints in Lagrangian fashion and solving the Lagrangian dual with subgradient optimization, multiplier adjustment methods, or Dantzig-Wolfe decomposition.

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Author information

Authors and Affiliations

  1. Dep. of Business Administration, University of Ulm, Ulm, D-89069

    Paul Wentges

Authors
  1. Paul Wentges
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Editor information

Editors and Affiliations

  1. Lehrstuhl für Wirtschafts- und Sozialstatistik, University of Jena, Carl-Zeiss-Straße 3, Jena, 07743, Germany

    Peter Kischka

  2. Fachbereich Mathematik, Technical University of Berlin, Straße des 17. Juni 136, Berlin, 10623, Germany

    Rolf H. Möhring

  3. Institut für Statistik und Operations Research, University of Graz, Universitätsstraße 15, Graz, 8010, Austria

    Ulrike Leopold-Wildburger

  4. Forschungsinstitut für Anwendungsorientierte Wissensverarbeitung, University of Ulm, Helmholtzstraße 16, Ulm, 89081, Germany

    Franz-Josef Radermacher

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© 2001 Springer-Verlag Berlin Heidelberg

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Wentges, P. (2001). On Cross Decomposition for Mixed-Integer Programming. In: Kischka, P., Möhring, R.H., Leopold-Wildburger, U., Radermacher, FJ. (eds) Models, Methods and Decision Support for Management. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57603-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-57603-4_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-642-63306-5

  • Online ISBN: 978-3-642-57603-4

  • eBook Packages: Springer Book Archive

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