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A randomized diversification strategy for solving satisfiability problem with long clauses

Abstract

Satisfiability problem (SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search (SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScoreSAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and thus propose a new SLS algorithm. We perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61370156, 61402070, 61503074), Program for New Century Excellent Talents in University (Grant No. NCET-13-0724), and Natural Science Foundation of Liaoning Province (Grant No. 2015020023).

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Correspondence to Minghao Yin.

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Gao, J., Li, R. & Yin, M. A randomized diversification strategy for solving satisfiability problem with long clauses. Sci. China Inf. Sci. 60, 092109 (2017). https://doi.org/10.1007/s11432-016-0258-4

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Keywords

  • SAT
  • local search
  • randomized diversification strategy
  • phase transition
  • long clause