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Coverage-Based Clause Reduction Heuristics for CDCL Solvers

机译:CDCL求解器的基于覆盖基因减少启发式

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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.
机译:许多启发式,如决定,重启和减少的启发式,并入到CDCL溶剂中,以提高性能。在本文中,我们专注于学习的子句降低启发式,用于抑制内存消耗和维持传播速度。减少启发式机构由评估标准组成,用于测量学习条款的有用性,以及减少策略,以便根据标准选择要删除的子句。 LBD(文字块距离)用作许多求解器中的评估标准。对于减少策略,我们提出了一种基于二手LBD的覆盖率的新简明模式。实验结果表明,该策略可以实现比传统策略更高的覆盖率,提高饱和实例的绩效。

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