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Computer Science > Artificial Intelligence

arXiv:2112.06917 (cs)
[Submitted on 11 Dec 2021]

Title:Branching Strategy Selection Approach Based on Vivification Ratio

Authors: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.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2112.06917 [cs.AI]
  (or arXiv:2112.06917v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2112.06917

Submission history

From: Mao Luo [view email]
[v1] Sat, 11 Dec 2021 04:07:39 UTC (6,507 KB)
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