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2020 | OriginalPaper | Buchkapitel

Trail Saving on Backtrack

verfasst von : Randy Hickey, Fahiem Bacchus

Erschienen in: Theory and Applications of Satisfiability Testing – SAT 2020

Verlag: Springer International Publishing

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Abstract

A CDCL SAT solver can backtrack a large distance when it learns a new clause, e.g, when the new learnt clause is a unit clause the solver has to backtrack to level zero. When the length of the backtrack is large, the solver can end up reproducing many of the same decisions and propagations when it redescends the search tree. Different techniques have been proposed to reduce this potential redundancy, e.g., partial/chronological backtracking and trail saving on restarts. In this paper we present a new trail saving technique that is not restricted to restarts, unlike prior trail saving methods. Our technique makes a copy of the part of the trail that is backtracked over. This saved copy can then be used to improve the efficiency of the solver’s subsequent redescent. Furthermore, the saved trail also provides the solver with the ability to look ahead along the previous trail which can be exploited to improve its efficiency. Our new trail saving technique offers different tradeoffs in comparison with chronological backtracking and often yields superior performance. We also show that our technique is able to improve the performance of state-of-the-art solvers.

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Fußnoten
1
Our approach uses standard trails in which the decision levels are contiguous. Chronological backtracking [6, 8, 11] generates trails with non-contiguous decision levels.
 
2
C-bt can also be extremely useful in contexts where each descent can be very expensive, e.g., when doing theory propagation in SMT solving, or component analysis in #SAT solving. In these cases, C-bt, by avoiding backtracking and subsequent redescent, has considerable potential for improving solver performance.
 
3
Our implementation is available at https://​github.​com/​rgh000/​cadical-trail.
 
Literatur
1.
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Zurück zum Zitat Luo, M., Li, C., Xiao, F., Manyà, F., Lü, Z.: An effective learnt clause minimization approach for CDCL SAT solvers. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 703–711. ijcai.org (2017). https://doi.org/10.24963/ijcai.2017/98 Luo, M., Li, C., Xiao, F., Manyà, F., Lü, Z.: An effective learnt clause minimization approach for CDCL SAT solvers. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 703–711. ijcai.org (2017). https://​doi.​org/​10.​24963/​ijcai.​2017/​98
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Zurück zum Zitat Xiao, F., Luo, M., Li, C.M., Manya, F., Lü, Z.: MapleLRB LCM, Maple LCM, Maple LCM dist, MapleLRB LCMoccRestart and glucose-3.0+ width in SAT competition 2017. In: Balyo, T., Heule, M.J.H., Järvisalo, M.J. (eds.) Proceedings of SAT Competition 2017: Solver and Benchmark Descrptions, pp. 22–23. University of Helsinki (2017). http://hdl.handle.net/10138/224324 Xiao, F., Luo, M., Li, C.M., Manya, F., Lü, Z.: MapleLRB LCM, Maple LCM, Maple LCM dist, MapleLRB LCMoccRestart and glucose-3.0+ width in SAT competition 2017. In: Balyo, T., Heule, M.J.H., Järvisalo, M.J. (eds.) Proceedings of SAT Competition 2017: Solver and Benchmark Descrptions, pp. 22–23. University of Helsinki (2017). http://​hdl.​handle.​net/​10138/​224324
Metadaten
Titel
Trail Saving on Backtrack
verfasst von
Randy Hickey
Fahiem Bacchus
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-51825-7_4