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Resolution Complexity of Random Constraints

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Principles and Practice of Constraint Programming - CP 2002 (CP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2470))

Abstract

Random instances are widely used as benchmarks in evaluating algorithms for finite-domain constraint satisfaction problems (CSPs). We present an analysis that shows why deciding satisfiability of instances from some distributions is challenging for current complete methods. For a typical random CSP model, we show that when constraints are not too tight almost all unsatisfiable instances have a structural property which guarantees that unsatisfiability proofs in a certain resolution-like system must be of exponential size. This proof system can efficiently simulate the reasoning of a large class of CSP algorithms which will thus have exponential running time on these instances.

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Authors and Affiliations

  1. Simon Fraser University, Burnaby, Canada

    David G. Mitchell

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  1. David G. Mitchell
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Editors and Affiliations

  1. Brown University, Box 1910, Providence, RI, 02912, USA

    Pascal Van Hentenryck

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© 2002 Springer-Verlag Berlin Heidelberg

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Mitchell, D.G. (2002). Resolution Complexity of Random Constraints. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_20

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  • DOI: https://doi.org/10.1007/3-540-46135-3_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44120-5

  • Online ISBN: 978-3-540-46135-7

  • eBook Packages: Springer Book Archive

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