Random Constraint Satisfaction: A More Accurate Picture

  • Authors:

  • Dimitris Achlioptas;Michael S. O. Molloy;Lefteris M. Kirousis;Yannis C. Stamatiou;Evangelos Kranakis;Danny Krizanc

  • Affiliations:

  • Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada optas@cs.toronto.edu;Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada molloy@cs.toronto.edu;Department of Computer Engineering and Informatics, University of Patras, Rio, 26500 Patras, Greece kirousis@ceid.upatras.gr;Department of Computer Engineering and Informatics, University of Patras, Rio, 26500 Patras, Greece stamatiu@ceid.upatras.gr;School of Computer Science, Carleton University, Ottawa, ON K1S 5B6, Canada kranakis@scs.carleton.ca;School of Computer Science, Carleton University, Ottawa, ON K1S 5B6, Canada krizanc@scs.carleton.ca

  • Venue:
  • Constraints
  • Year:
  • 2001

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Abstract

In the last few years there has been a great amount of interest in Random Constraint Satisfaction Problems, both from an experimental and a theoretical point of view. Quite intriguingly, experimental results with various models for generating random CSP instances suggest that the probability of such problems having a solution exhibits a “threshold-like” behavior. In this spirit, some preliminary theoretical work has been done in analyzing these models asymptotically, i.e., as the number of variables grows. In this paper we prove that, contrary to beliefs based on experimental evidence, the models commonly used for generating random CSP instances ido not have an asymptotic threshold. In particular, we prove that asymptotically ialmost all instances they generate are overconstrained, suffering from trivial, local inconsistencies. To complement this result we present an alternative, single-parameter model for generating random CSP instances and prove that, unlike current models, it exhibits non-trivial asymptotic behavior. Moreover, for this new model we derive explicit bounds for the narrow region within which the probability of having a solution changes dramatically.