Skip to content

Research at St Andrews

Automatically improving constraint models in Savile Row

Research output: Contribution to journalArticle

Abstract

When solving a combinatorial problem using Constraint Programming (CP) or Satisfiability (SAT), modelling and formulation are vital and difficult tasks. Even an expert human may explore many alternatives in modelling a single problem. We make a number of contributions in the automated modelling and reformulation of constraint models. We study a range of automated reformulation techniques, finding combinations of techniques which perform particularly well together. We introduce and describe in detail a new algorithm, X-CSE, to perform Associative-Commutative Common Subexpression Elimination (AC-CSE) in constraint problems, significantly improving existing CSE techniques for associative and commutative operators such as +. We demonstrate that these reformulation techniques can be integrated in a single automated constraint modelling tool, called Savile Row, whose architecture we describe. We use Savile Row as an experimental testbed to evaluate each reformulation on a set of 50 problem classes, with 596 instances in total. Our recommended reformulations are well worthwhile even including overheads, especially on harder instances where solver time dominates. With a SAT solver we observed a geometric mean of 2.15 times speedup compared to a straightforward tailored model without recommended reformulations. Using a CP solver, we obtained a geometric mean of 5.96 times speedup for instances taking over 10 seconds to solve.
Close

Details

Original languageEnglish
Pages (from-to)35-61
Number of pages27
JournalArtificial Intelligence
Volume251
Early online date13 Jul 2017
DOIs
StatePublished - Oct 2017

    Research areas

  • Common subexpression elimination, Constraint satisfaction, Modelling, Propositional satisfiability, Reformulation

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. A framework for constraint based local search using ESSENCE

    Akgun, O., Attieh, S. W. A., Gent, I. P., Jefferson, C. A., Miguel, I. J., Nightingale, P. W., Salamon, A. Z., Spracklen, P. & Wetter, J. P. 13 Jul 2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Lang, J. (ed.). International Joint Conferences on Artificial Intelligence, p. 1242-1248 7 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Metamorphic testing of constraint solvers

    Akgun, O., Gent, I. P., Jefferson, C. A., Miguel, I. J. & Nightingale, P. W. 2018 Principles and Practice of Constraint Programming: 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings. Hooker, J. (ed.). Springer, p. 727-736 (Lecture Notes in Computer Science; vol. 11008)

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Automatic discovery and exploitation of promising subproblems for tabulation

    Akgun, O., Gent, I. P., Jefferson, C. A., Miguel, I. J., Nightingale, P. W. & Salamon, A. Z. 2018 Principles and Practice of Constraint Programming: 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings. Hooker, J. (ed.). Springer, p. 3-12 (Lecture Notes in Computer Science; vol. 11008)

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  4. Exploiting short supports for improved encoding of arbitrary constraints into SAT

    Akgün, Ö., Gent, I. P., Jefferson, C. A., Miguel, I. J. & Nightingale, P. W. 2016 Principles and Practice of Constraint Programming: 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. Rueher, M. (ed.). Springer, p. 3-12 (Lecture Notes in Computer Science; vol. 9892)

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  5. Automatically Improving Constraint Models in Savile Row through Associative-Commutative Common Subexpression Elimination

    Nightingale, P., Akgun, O., Gent, I. P., Jefferson, C. & Miguel, I. Sep 2014 Principles and Practice of Constraint Programming. OSullivan, B. (ed.). Cham: Springer, p. 590-605 16 p. (Lecture Notes in Computer Science; vol. 8656)

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

Related by journal

  1. Generating custom propagators for arbitrary constraints

    Gent, I. P., Jefferson, C., Linton, S., Miguel, I. & Nightingale, P. 1 Jun 2014 In : Artificial Intelligence. 211, 1, p. 1-33 33 p.

    Research output: Contribution to journalArticle

  2. The Extended Global Cardinality Constraint: An Empirical Survey

    Nightingale, P. Feb 2011 In : Artificial Intelligence. 175, 2, p. 586-614 29 p.

    Research output: Contribution to journalArticle

  3. Implementing logical connectives in constraint programming

    Jefferson, C. A., Moore, N. C. A., Nightingale, P. & Petrie, K. E. Nov 2010 In : Artificial Intelligence. 174, 16-17, p. 1407-1429 23 p.

    Research output: Contribution to journalArticle

  4. Filtering Algorithms for the Multiset Ordering Constraint

    Frisch, A., Hnich, B., Kiziltan, Z., Miguel, I. J. & Walsh, T. Feb 2009 In : Artificial Intelligence. 173, 2, p. 299-328 29 p.

    Research output: Contribution to journalArticle

ID: 250522992