Abstract
In 2020 Feng & Bacchus revisited variants of the all-UIP learning strategy, which considerably improved performance of their version of CaDiCaL submitted to the SAT Competition 2020, particularly on large planning instances. We improve on their algorithm by tightly integrating this idea with learned clause minimization. This yields a clean shrinking algorithm with complexity linear in the size of the implication graph. It is fast enough to unconditionally shrink learned clauses until completion. We further define trail redundancy and show that our version of shrinking removes all redundant literals. Independent experiments with the three SAT solvers CaDiCaL, Kissat, and Satch confirm the effectiveness of our approach.
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Notes
- 1.
Source code and log files are available at http://fmv.jku.at/sat_shrinking.
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Acknowledgment
This work is supported by Austrian Science Fund (FWF), NFN S11408-N23 (RiSE), and the LIT AI Lab funded by the State of Upper Austria. We also thank Sibylle Möhle and the anonymous reviewers for suggesting textual improvements.
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Fleury, M., Biere, A. (2021). Efficient All-UIP Learned Clause Minimization. In: Li, CM., Manyà, F. (eds) Theory and Applications of Satisfiability Testing – SAT 2021. SAT 2021. Lecture Notes in Computer Science(), vol 12831. Springer, Cham. https://doi.org/10.1007/978-3-030-80223-3_12
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