Learning-Sensitive Backdoors with Restarts

Zulkoski, Edward; Martins, Ruben; Wintersteiger, Christoph M.; Robere, Robert; Jia, Liang; Czarnecki, Krzysztof

doi:10.1007/978-3-319-98334-9_30

Cited by 3 publications

(14 citation statements)

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“…Boolean structural measure proposed for SAT include the phase transition [16], backbone size [17], and backdoor size [18,19]. We propose three new related measures to the backbones and backdoors: the backbone frequency, backbone coverage, and backdoor coverage (The reader is referred to Appendix A where the evidence of the backbone and backdoor-related measures on industrial, crafted, and random benchmark instances drawn from 2002-2020 SAT competitions are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have shown, for example, that backbone/backdoor-guided branching heuristics improve the solver performance [18,[22][23][24]. Despite these efforts, this problem remains an open issue [19,25]. One reason is that the computational estimation of most Boolean structural measures (e.g., backbone and backdoor) is intractable [26].…”

Section: Introductionmentioning

confidence: 99%

“…One reason is that the computational estimation of most Boolean structural measures (e.g., backbone and backdoor) is intractable [26]. Second, as a consequence, the structural measures of SAT and their impact on the behavior of SAT solvers have not been fully investigated [19,25].…”

Section: Introductionmentioning

confidence: 99%

“…Definition 18 (Learning Sensitive with Restarts (LSR) backdoor [19]). A subset of variables B LSR is an LSR backdoor with respect to sub-solver Γ if there exists a search tree exploration order with restarts such that a CDCL SAT solver branches only on variables in B LSR , and with a subsolver Γ and learnt clauses at the leaves of the search tree, it either finds a satisfying assignment for φ or proves that φ is unsatisfiable.…”

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confidence: 99%

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Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors

2022

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Boolean structural measures were introduced to explain the high performance of conflict-driven clause-learning (CDCL) SAT solvers on industrial SAT instances. Those considered in this study include measures related to backbones and backdoors: backbone size, backbone frequency, and backdoor size. A key area of research is to improve the performance of CDCL SAT solvers by exploiting these measures. For the purpose of guiding the CDCL SAT solver for branching on backbone and backdoor variables, this study proposes low-overhead heuristics for computing these variables. Through these heuristics, a set of modifications to the Variable State Independent Decaying Sum (VSIDS) decision heuristic is suggested to exploit backbones and backdoors and potentially improve the performance of CDCL SAT solvers. In total, fifteen variants of two competitive base solvers, MapleLCMDistChronoBT-DL-v3 and LSTech, were developed. Empirical evaluation was conducted on 32 industrial families from 2002–2021 SAT competitions. According to the results, modifying the VSIDS heuristic in the base solvers to exploit backbones and backdoors improves its performance. In particular, our new CDCL SAT solver, LSTech_BBsfcr_v1, solved more industrial SAT instances than the winning CDCL SAT solvers in 2020 and 2021 SAT competitions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors

2022

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“…community structure [4]). By contrast, the term rigorous refers to parameters that characterize classes of formulas that are fixed-parameter tractable (FPT), such as: backdoors [46,50], backbones [30], treewidth, and branchwidth [1,39], among many others [39]; or have been used to prove complexity-theoretic bounds over randomly-generated classes of Boolean formulas such as clause-variable ratio (a.k.a., density) [16,41].…”

Section: Introductionmentioning

confidence: 99%