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Computing Lower Bound for MAX-CSP Problems

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Developments in Applied Artificial Intelligence (IEA/AIE 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2718))

Abstract

The inefficiency of the branch and bound method for solving Constraint Optimization Problems is due in most cases to the poor quality of the lower bound used by this method. Many works have been proposed to improve the quality of this bound. In this, paper we investigate a set of lower bounds by considering two criteria: the quality and the computing cost. We study different ways to compute the parameters of the parametric lower bound and we propose heuristics for searching the parameters maximizing the parametric lower bound. Computational experiments performed over randomly generated problems show the advantages of our new branch and bound scheme.

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Author information

Authors and Affiliations

  1. Laboratoire LIPN-CNRS UMR7030, Avenue Jean-Baptiste Clément, F-93430, Villetaneuse

    Hachemi Bannaceur & Aomar Osmani

Authors
  1. Hachemi Bannaceur
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  2. Aomar Osmani
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Loughborough University, Loughborough, LE11 3TU, England

    Paul W. H. Chung  & Chris Hinde  & 

  2. Dept. of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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© 2003 Springer-Verlag Berlin Heidelberg

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Bannaceur, H., Osmani, A. (2003). Computing Lower Bound for MAX-CSP Problems. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_62

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  • DOI: https://doi.org/10.1007/3-540-45034-3_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40455-2

  • Online ISBN: 978-3-540-45034-4

  • eBook Packages: Springer Book Archive

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