Coverage-Based Clause Reduction Heuristics for CDCL Solvers
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Many heuristics, such as decision, restart, and clause reduction heuristics, are incorporated in CDCL solvers in order to improve performance. In this paper, we focus on learnt clause reduction heuristics, which are used to suppress memory consumption and sustain propagation speed. The reduction heuristics consist of evaluation criteria, for measuring the usefulness of learnt clauses, and a reduction strategy in order to select clauses to be removed based on the criteria. LBD (literals blocks distance) is used as the evaluation criteria in many solvers. For the reduction strategy, we propose a new concise schema based on the coverage ratio of used LBDs. The experimental results show that the proposed strategy can achieve higher coverage than the conventional strategy and improve the performance for both SAT and UNSAT instances.
- 1.Audemard, G., Simon, L.: Predicting learnt clauses quality in modern SAT solvers. In: Proceedings of IJCAI-2009, pp. 399–404 (2009)Google Scholar
- 2.Audemard, G., Simon, L.: Glucose 3.1 in the SAT 2014 competition (2014). http://satcompetition.org/edacc/sc14/solver-description-download/118. SAT Competition 2014 Solver Description
- 3.Bayardo Jr., R.J., Schrag, R.: Using CSP look-back techniques to solve real-world SAT instances. In: Proceedings of the 14th National Conference on Artificial Intelligence (AAAI 1997), pp. 203–208 (1997)Google Scholar
- 4.Biere, A.: Lingeling and Friends at the SAT Competition 2011 (2011). http://fmv.jku.at/papers/biere-fmv-tr-11-1.pdf. SAT Competition 2011 Solver Description
- 7.Nabeshima, H., Iwanuma, K., Inoue, K.: On-the-fly lazy clause simplification based on binary resolvents. In: ICTAI, pp. 987–995. IEEE (2013)Google Scholar