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Symmetry 2019, 11(2), 197;

An Adaptive Strategy for Tuning Duplicate Trails in SAT Solvers

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
National-Local Joint Engineering Laboratory of System Credibility Automatic Verification, Chengdu 610031, China
School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China
Author to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 3 February 2019 / Accepted: 3 February 2019 / Published: 11 February 2019
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In mainstream conflict driven clause learning (CDCL) solvers, because of frequent restarts and phase saving, there exists a large proportion of duplicate assignment trails before and after restarts, resulting in unnecessary time wastage during solving. This paper proposes a new strategy—identifying those duplicate assignments trails and dealing with them by changing the sort order. This approach’s performance is compared with that of the Luby static restart scheme and a dynamic Glucose-restart strategy. We show that the number of solved instances is increased by 3.2% and 4.6%. We also make a compassion with the MapleCOMSPS solver by testing against application benchmarks from the SAT Competitions 2015 to 2017. These empirical results provide further evidence of the benefits of the proposed heuristic, having the advantage of managing duplicate assignments trails and choosing appropriate decision variables adaptively. View Full-Text
Keywords: satisfiability problem; decision variable; restart; duplicate trails satisfiability problem; decision variable; restart; duplicate trails

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Chang, W.; Xu, Y.; Chen, S. An Adaptive Strategy for Tuning Duplicate Trails in SAT Solvers. Symmetry 2019, 11, 197.

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