Branching Strategy Selection Approach Based on Vivification Ratio

Mao Luo, Chu Min Li, Xinyun Wu, Shuolin Li, Zhipeng Lü
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Abstract

The two most effective branching strategies LRB and VSIDS perform differently on different types of instances. Generally, LRB is more effective on crafted instances, while VSIDS is more effective on application ones. However, distinguishing the types of instances is difficult. To overcome this drawback, we propose a branching strategy selection approach based on the vivification ratio. This approach uses the LRB branching strategy more to solve the instances with a very low vivification ratio. We tested the instances from the main track of SAT competitions in recent years. The results show that the proposed approach is robust and it significantly increases the number of solved instances. It is worth mentioning that, with the help of our approach, the solver Maple CM can solve more than 16 instances for the benchmark from the 2020 SAT competition.
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基于活化率的分支策略选择方法
两种最有效的分支策略LRB和VSIDS在不同类型的实例上的表现不同。通常,LRB在精心制作的实例上更有效,而VSIDS在应用程序实例上更有效。然而,区分实例的类型是困难的。为了克服这一缺点,我们提出了一种基于活化率的分支策略选择方法。这种方法更多地使用LRB分支策略来解决激活率非常低的实例。我们从近年来的SAT比赛主赛道上进行了实例测试。结果表明,该方法具有较强的鲁棒性,显著提高了求解实例的数量。值得一提的是,在我们的方法的帮助下,求解器Maple CM可以为2020年SAT竞赛的基准解决超过16个实例。
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