Statistical Regimes Across Constrainedness Regions

  • Authors:

  • Carla P. Gomes;Cèsar Fernández;Bart Selman;Christian Bessière

  • Affiliations:

  • Department of Computer Science, Cornell University, Ithaca, USA;Department d'Informàtica, Universitat de Lleida, Lheida, Spain E-25001;Department of Computer Science, Cornell University, Ithaca, USA;LIRMM-CNRS, Montpellier Cedex 5, France 34392

  • Venue:
  • Constraints
  • Year:
  • 2005

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Abstract

Much progress has been made in terms of boosting the effectiveness of backtrack style search methods. In addition, during the last decade, a much better understanding of problem hardness, typical case complexity, and backtrack search behavior has been obtained. One example of a recent insight into backtrack search concerns so-called heavy-tailed behavior in randomized versions of backtrack search. Such heavy-tails explain the large variance in runtime often observed in practice. However, heavy-tailed behavior does certainly not occur on all instances. This has led to a need for a more precise characterization of when heavy-tailedness does and when it does not occur in backtrack search. In this paper, we provide such a characterization. We identify different statistical regimes of the tail of the runtime distributions of randomized backtrack search methods and show how they are correlated with the "sophistication" of the search procedure combined with the inherent hardness of the instances. We also show that the runtime distribution regime is highly correlated with the distribution of the depth of inconsistent subtrees discovered during the search. In particular, we show that an exponential distribution of the depth of inconsistent subtrees combined with a search space that grows exponentially with the depth of the inconsistent subtrees implies heavy-tailed behavior.