Random Constraint Satisfaction: Flaws and Structure

  • Authors:
  • Ian P. Gent;Ewan Macintyre;Patrick Prosser;Barbara M. Smith;Toby Walsh

  • Affiliations:
  • School of Computer Science, University of St Andrews, St Andrews, Fife KY16 9SS, United Kingdom ipg@dcs.st-and.ac.uk;Cisco Systems (Scotland) Ltd., Broadwood Business Park, Cumbernauld, Glasgow G68 9LE, United Kingdom emacinty@cisco.com;Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, United Kingdom pat@dcs.gla.ac.uk;School of Computing & Mathematics, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom b.m.smith@hud.ac.uk;Department of Computer Science, University of York, York YO10 5DD, United Kingdom tw@cs.york.ac.uk

  • Venue:
  • Constraints
  • Year:
  • 2001

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Abstract

A recent theoretical result by Achlioptas et al. shows that many models of random binary constraint satisfaction problems become trivially insoluble as problem size increases. This insolubility is partly due to the presence of ‘flawed variables,’ variables whose values are all ‘flawed’ (or unsupported). In this paper, we analyse how seriously existing work has been affected. We survey the literature to identify experimental studies that use models and parameters that may have been affected by flaws. We then estimate theoretically and measure experimentally the size at which flawed variables can be expected to occur. To eliminate flawed values and variables in the models currently used, we introduce a ‘flawless’ generator which puts a limited amount of structure into the conflict matrix. We prove that such flawless problems are not trivially insoluble for constraint tightnesses up to 1/2. We also prove that the standard models B and C do not suffer from flaws when the constraint tightness is less than the reciprocal of domain size. We consider introducing types of structure into the constraint graph which are rare in random graphs and present experimental results with such structured graphs.