决策变元选择_决策分支策略——文献学习A branching heuristic for SAT solvers based on complete implication graphs

Posted 2020-12-13 yuweng1689

A branching heuristic for SAT solvers based on complete implication graphs

Xiao, F., Li, C., Luo, M. et al. A branching heuristic for SAT solvers based on complete implication graphs. Sci. China Inf. Sci. 62, 72103 (2019). https://doi.org/10.1007/s11432-017-9467-7

 

除了VSIDS和LRB之外，新的决策变元选择（活跃度）策略——基于activity_distance[v]

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Abstract

The performance of modern conflict-driven clause learning (CDCL) SAT solvers strongly depends on branching heuristics. State-of-the-art branching heuristics, such as variable state independent decaying sum (VSIDS) and learning rate branching (LRB), are computed and maintained by aggregating the occurrences of the variables in conflicts. However, these heuristics are not sufficiently accurate at the beginning of the search because they are based on very few conflicts.   We propose the distance branching heuristic, which, given a conflict, constructs a complete implication graph and increments the score of a variable considering the longest distance between the variable and the conflict rather than the simple presence of the variable in the graph. 译文：给定一个冲突，构造一个完整的隐含图，并考虑变量与冲突之间的最长距离，而不是简单地考虑变量在图中的存在，增加变量的得分。   We implemented the proposed distance branching heuristic in Maple_LCM and Glucose-3.0, two of the best CDCL SAT solvers, and used the resulting solvers to solve instances from the application and crafted tracks of the 2014 and 2016 SAT competitions and the main track of the 2017 SAT competition. The empirical results demonstrate that using the proposed distance branching heuristic in the initialization phase of Maple_LCM and Glucose-3.0 solvers improves performance. The Maple_LCM solver with the proposed distance branching heuristic in the initialization phase won the main track of the 2017 SAT competition.

 

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