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Solving Generalized Maximum Dispersion with Linear Programming

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Algorithmic Aspects in Information and Management (AAIM 2007)

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Abstract

The Generalized Maximum Dispersion problem asks for a partition of a given graph into p vertex-disjoint sets, each of them having at most k vertices. The goal is to maximize the total edge-weight of the induced subgraphs. We present the first LP-based approximation algorithm.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, Missouri 63130-4899, USA

    Gerold Jäger

  2. Institut für Informatik, Christian-Albrechts-Universität zu Kiel, Christian-Albrechts-Platz 4, D-24118 Kiel, Germany

    Anand Srivastav

  3. Zentrum für Paralleles Rechnen Universität zu Köln, Weyertal 80, D-50931 Köln, Germany

    Katja Wolf

Authors
  1. Gerold Jäger
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  2. Anand Srivastav
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  3. Katja Wolf
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Editor information

Ming-Yang Kao Xiang-Yang Li

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Jäger, G., Srivastav, A., Wolf, K. (2007). Solving Generalized Maximum Dispersion with Linear Programming. In: Kao, MY., Li, XY. (eds) Algorithmic Aspects in Information and Management. AAIM 2007. Lecture Notes in Computer Science, vol 4508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72870-2_1

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  • DOI: https://doi.org/10.1007/978-3-540-72870-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72868-9

  • Online ISBN: 978-3-540-72870-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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