MINION: A Fast, Scalable, Constraint Solver

  • Authors:

  • Ian P. Gent;Chris Jefferson;Ian Miguel

  • Affiliations:

  • School of Computer Science, University of St Andrews, St Andrews, Fife, KY16 9SS, UK. {ipg,ianm}@dcs.st-and.ac.uk;Oxford University Computing Laboratory, University of Oxford, Oxford, UK. chris.jefferson@comlab.ox.ac.uk. Research undertaken while at St Andrews University;School of Computer Science, University of St Andrews, St Andrews, Fife, KY16 9SS, UK. {ipg,ianm}@dcs.st-and.ac.uk

  • Venue:
  • Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
  • Year:
  • 2006

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Abstract

We present Minion, a new constraint solver. Empirical results on standard benchmarks show orders of magnitude performance gains over state-of-the-art constraint toolkits. These gains increase with problem size --MINION delivers scalable constraint solving. MINION is a general-purpose constraint solver, with an expressive input language based on the common constraint modelling device of matrix models. Focussing on matrix models supports a highly-optimised implementation, exploiting the properties of modern processors. This contrasts with current constraint toolkits, which, in order to provide ever more modelling and solving options, have become progressively more complex at the cost of both performance and usability. MINION is a black box from the user point of view, deliberately providing few options. This, combined with its raw speed, makes MINION a substantial step towards Puget's 'Model and Run' constraint solving paradigm.