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

  • Peter Nightingale
  • Özgür Akgün
  • Ian P. Gent
  • Christopher Jefferson
  • Ian Miguel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8656)

Abstract

When solving a problem using constraint programming, constraint modelling is widely acknowledged as an important and difficult task. Even a constraint modelling expert may explore many models and spend considerable time modelling a single problem. Therefore any automated assistance in the area of constraint modelling is valuable. Common sub-expression elimination (CSE) is a type of constraint reformulation that has proved to be useful on a range of problems. In this paper we demonstrate the value of an extension of CSE called Associative-Commutative CSE (AC-CSE). This technique exploits the properties of associativity and commutativity of binary operators, for example in sum constraints. We present a new algorithm, X-CSE, that is able to choose from a larger palette of common subexpressions than previous approaches. We demonstrate substantial gains in performance using X-CSE. For example on BIBD we observed speed increases of more than 20 times compared to a standard model and that using X-CSE outperforms a sophisticated model from the literature. For Killer Sudoku we found that X-CSE can render some apparently difficult instances almost trivial to solve, and we observe speed increases up to 350 times. For BIBD and Killer Sudoku the common subexpressions are not present in the initial model: an important part of our methodology is reformulations at the preprocessing stage, to create the common subexpressions for X-CSE to exploit. In summary we show that X-CSE, combined with preprocessing and other reformulations, is a powerful technique for automated modelling of problems containing associative and commutative constraints.

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References

  1. 1.
    Araya, I., Neveu, B., Trombettoni, G.: Exploiting common subexpressions in numerical CSPs. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 342–357. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Beldiceanu, N., Simonis, H.: A constraint seeker: Finding and ranking global constraints from examples. In: Lee (ed.) [10], pp. 12–26Google Scholar
  3. 3.
    Bessiere, C., Cardon, S., Debruyne, R., Lecoutre, C.: Efficient algorithms for singleton arc consistency. Constraints 16(1), 25–53 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Choi, C.W., Lee, J.H.M.: Solving the salinity control problem in a potable water system. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 33–48. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Frisch, A., Jefferson, C., Miguel, I.: CSPLib problem 035: Molnar’s problem, http://www.csplib.org/Problems/prob035
  6. 6.
    Frisch, A.M., Jefferson, C., Miguel, I.: Constraints for breaking more row and column symmetries. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 318–332. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Frisch, A.M., Jefferson, C., Miguel, I.: Symmetry-breaking as a prelude to implied constraints: A constraint modelling pattern. In: Proc. 16th European Conference on Artificial Intelligence, ECAI 2004 (2004)Google Scholar
  8. 8.
    Frisch, A.M., Miguel, I., Walsh, T.: CGRASS: A system for transforming constraint satisfaction problems. In: O’Sullivan, B. (ed.) CologNet 2002. LNCS (LNAI), vol. 2627, pp. 15–30. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Jefferson, C., Moore, N., Nightingale, P., Petrie, K.E.: Implementing logical connectives in constraint programming. Artificial Intelligence 174, 1407–1429 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Lee, J. (ed.): CP 2011. LNCS, vol. 6876. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  11. 11.
    Mears, C., Niven, T., Jackson, M., Wallace, M.: Proving symmetries by model transformation. In: Lee (ed.) [10], pp. 591–605Google Scholar
  12. 12.
    Nightingale, P.: Savile Row, a constraint modelling assistant (2014), http://savilerow.cs.st-andrews.ac.uk/Google Scholar
  13. 13.
    Puget, J.F.: Constraint programming next challenge: Simplicity of use. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 5–8. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Puget, J.F.: Symmetry breaking using stabilizers. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 585–599. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Rendl, A.: Effective Compilation of Constraint Models. Ph.D. thesis, University of St Andrews (2010)Google Scholar
  16. 16.
    Rendl, A., Miguel, I., Gent, I.P., Jefferson, C.: Automatically enhancing constraint model instances during tailoring. In: Bulitko, V., Beck, J.C. (eds.) SARA. AAAI (2009)Google Scholar
  17. 17.
    Smith, B.M.: Symmetry and search in a network design problem. In: Barták, R., Milano, M. (eds.) CPAIOR 2005. LNCS, vol. 3524, pp. 336–350. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Stuckey, P.J., Tack, G.: MiniZinc with functions. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 268–283. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Van Hentenryck, P.: The OPL Optimization Programming Language. MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Peter Nightingale
    • 1
  • Özgür Akgün
    • 1
  • Ian P. Gent
    • 1
  • Christopher Jefferson
    • 1
  • Ian Miguel
    • 1
  1. 1.School of Computer ScienceUniversity of St. AndrewsSt. AndrewsUK

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