Journal of Computer Science and Technology

, Volume 13, Issue 6, pp 608–614 | Cite as

Experimental study on strategy of combining SAT algorithms

Regular Papers


The effectiveness of many SAT algorithms is mainly reflected by their significant performances on one or several classes of specific SAT problems. Different kinds of SAT algorithms all have their own hard instances respectively. Therefore, to get the better performance on all kinds of problems, SAT solver should know how to select different algorithms according to the feature of instances. In this paper the differences of several effective SAT algorithms are analyzed and two new parameters ϕ and δ are proposed to characterize the feature of SAT instances. Experiments are performed to study the relationship between SAT algorithms and some statistical parameters including ϕ, δ. Based on this analysis, a strategy is presented for designing a faster SAT tester by carefully combining some existing SAT algorithms. With this strategy, a faster SAT tester to solve many kinds of SAT problem is obtained.


Satisfiability problem propositional formula algorithm optimization 


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  1. [1]
    James M, Crawford, Andrew B Baker. Experimental results on the application of satisfiability algorithms to scheduling problems.AAAI-94, pp. 1092–1097.Google Scholar
  2. [2]
    Freeman J W. Improvements to propositional satisfiability search algorithms. Dissertation at Univ. of Pennesylvania, 1995.Google Scholar
  3. [3]
    Minton S, Johnson M Det al. Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems.Artificial Intelligence, 1990, 58: 161–205.CrossRefGoogle Scholar
  4. [4]
    Selman B, Levesque H, Mitchell D. A new method for solving hard satisfiability problems. InAAAI-92, 1992, pp. 440–446.Google Scholar
  5. [5]
    Gu J. Local search for satisfiability (SAT) problem.IEEE Transactions on Systems, Man, and Cybernetics, July/August 1993, 23(4).Google Scholar
  6. [6]
    Konolige K. Easy to be hard: Difficult problems for greedy algorithms. InProceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, 1994, pp. 374–378.Google Scholar
  7. [7]
    Sandholm T. A New Order Parameter for 3-SAT. InAAAI-94, Workshop Program on Experimental Evaluation of Reasoning and Search Methods, pp. 57–63.Google Scholar
  8. [8]
    Lakhdat Sais. A computational study of DP with symmetries on hard satisfiability problems. InAAAI-94 Workshop Program on Experimental Evaluation of Reasoning and Search Methods, pp. 52–56.Google Scholar
  9. [9]
    Kalev Kask, Rina Dechter. GSAT and local consistency. InIJCAI-95, pp.548–554.Google Scholar
  10. [10]
    Crawford J M, Auton L D. Experimental results on the crossover point in satisfiability problems. InProceedings of the Eleventh National Conference on Artificial Intelligence, 1993, pp.21–27.Google Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 1998

Authors and Affiliations

  1. 1.Department of Computer ScienceBeijing University of Aeronautics and AstronauticsBeijingP.R. China

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