Abstract
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.
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- 1.
In Glucose 3.0 or later, the LBD update is executed only for clauses used in unit propagations on and after the first UIP in conflict analysis.
- 2.
In Glucose 3.0 or later, \(l_{\!\text{ first }}\) and \(l_{\!\text{ inc }}\) are 2000 and 300 respectively [2].
- 3.
SAT 2014 competition, SAT-Race 2015 and SAT 2016 competition.
- 4.
A clause is unused if it does not produce any propagation or conflict, except for the UIP propagation immediately after being learn it.
- 5.
We exclude Riss 6, which ranked 2nd in the competition. Because it uses Linux-specific APIs, we could not compile it in our computing environment (Mac OS X).
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Nabeshima, H., Inoue, K. (2017). Coverage-Based Clause Reduction Heuristics for CDCL Solvers. In: Gaspers, S., Walsh, T. (eds) Theory and Applications of Satisfiability Testing – SAT 2017. SAT 2017. Lecture Notes in Computer Science(), vol 10491. Springer, Cham. https://doi.org/10.1007/978-3-319-66263-3_9
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DOI: https://doi.org/10.1007/978-3-319-66263-3_9
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