Neighbourhood Clause Weight Redistribution in Local Search for SAT

  • Abdelraouf Ishtaiwi
  • John Thornton
  • Abdul Sattar
  • Duc Nghia Pham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3709)


In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. This paper introduces a new approach to clause weighting, known as Divide and Distribute Fixed Weights (DDFW), that transfers weights from neighbouring satisfied clauses to unsatisfied clauses in order to break out from local minima. Unlike earlier approaches, DDFW continuously redistributes a fixed quantity of weight between clauses, and so does not require a weight smoothing heuristic to control weight growth. It also exploits inherent problem structure by redistributing weights between neighbouring clauses.

To evaluate our ideas, we compared DDFW with two of the best reactive local search algorithms, AdaptNovelty+ and RSAPS. In both these algorithms, a problem sensitive parameter is automatically adjusted during the search, whereas DDFW uses a fixed default parameter. Our empirical results show that DDFW has consistently better performance over a range of SAT benchmark problems. This gives a strong indication that neighbourhood weight redistribution strategies could be the key to a next generation of structure exploiting, parameter-free local search SAT solvers.


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  1. 1.
    Hoos, H., Stulze, T.: Stochastic Local Search. Morgan Kaufmann, Cambridge (2005)zbMATHGoogle Scholar
  2. 2.
    Morris, P.: The Breakout method for escaping from local minima. In: Proceedings of 11th AAAI, pp. 40–45 (1993)Google Scholar
  3. 3.
    Cha, B., Iwama, K.: Adding new clauses for faster local search. In: Proceedings of 13th AAAI, pp. 332–337 (1996)Google Scholar
  4. 4.
    Frank, J.: Learning short-term clause weights for GSAT. In: Proceedings of 15th IJCAI, pp. 384–389 (1997)Google Scholar
  5. 5.
    McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proceedings of 14th AAAI, pp. 321–326 (1997)Google Scholar
  6. 6.
    Wu, Z., Wah, B.: An efficient global-search strategy in discrete Lagrangian methods for solving hard satisfiability problems. In: Proceedings of 17th AAAI, pp. 310–315 (2000)Google Scholar
  7. 7.
    Schuurmans, D., Southey, F.: Local search characteristics of incomplete SAT procedures. In: Proceedings of 10th AAAI, pp. 297–302 (2000)Google Scholar
  8. 8.
    Hutter, F., Tompkins, D., Hoos, H.: Scaling and Probabilistic Smoothing: Efficient dynamic local search for SAT. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 233–248. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Thornton, J., Pham, D., Bain, S., Ferreira Jr., V.: Additive versus multiplicative clause weighting for SAT. In: Proceedings of 19th AAAI, pp. 191–196 (2004)Google Scholar
  10. 10.
    Hoos, H.H.: An adaptive noise mechanism for Walksat. In: Proceedings of 19th AAAI, pp. 655–660 (2002)Google Scholar
  11. 11.
    Schuurmans, D., Southey, F., Holte, R.: The exponentiated subgradient algorithm for heuristic boolean programming. In: Proceedings of 17th IJCAI, pp. 334–341 (2001)Google Scholar
  12. 12.
    Pullan, W., Zhao, L.: Resolvent clause weighting local search. In: Proceedings of 17th Canadian AI, pp. 233–247 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Abdelraouf Ishtaiwi
    • 1
  • John Thornton
    • 1
  • Abdul Sattar
    • 1
  • Duc Nghia Pham
    • 1
  1. 1.Institute for Integrated and Intelligent Systems 

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