Diversification of Parallel Search of Portfolio SAT Solver by Search Similarity Index

Iida, Yoichiro; Sonobe, Tomohiro; Inaba, Masaaki

doi:10.1007/978-3-031-20862-1_5

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2023

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“…One approach is analyzing their behavior and characteristics with qualitative measurements. In our previous study, we proposed the search similarity index (SSI) (Iida, Sonobe, and Inaba 2022), a metric that quantifies the degree of similarity between searches. Using SSI, similar parallel searches are avoided, and the portfolio solver's performance is significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Understand Restart of SAT Solver Using Search Similarity Index (Student Abstract)

Iida

Sonobe

Inaba

2023

AAAI

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SAT solvers are widely used to solve many industrial problems because of their high performance, which is achieved by various heuristic methods. Understanding why these methods are effective is essential to improving them. One approach to this is analyzing them using qualitative measurements. In our previous study, we proposed search similarity index (SSI), a metric to quantify the similarity between searches. SSI significantly improved the performance of the parallel SAT solver. Here, we apply SSI to analyze the effect of restart, a key SAT solver technique. Experiments using SSI reveal the correlation between the difficulty of instances and the search change effect by restart, and the reason behind the effectiveness of the state-of-the-art restart method is also explained.

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Section: Introductionmentioning

confidence: 99%