当前SAT主要关键技术及其相关文献2022-11-1

当前SAT主要关键技术及其相关文献——参见下面这段叙述。

The annual SAT competitions have become an essential event for the distribution of SAT benchmarks and the development of new SAT-solving methods [5]. Sequential SAT solvers compete mainly in three categories: industrial, crafted, and random tracks. The SAT competitions have demonstrated how difficult it is for SAT solvers to perform well across all categories. Results show that conflict-driven clause-learning (CDCL) SAT solvers were most performant for solving industrial and crafted SAT benchmarks, whereas look-ahead and Stochastic Local Search (SLS)-based SAT solvers have dominated the random category [5]. Modern implementations of CDCL SAT solvers employ a lot of heuristics. Some of them can be considered baseline, such as the Variable State Independent Decaying Sum (VSIDS) [6], restarts [7], and Literal Block Distance (LBD) [8]. Several others were incorporated recently, including: Learnt Clause Minimization (LCM) [9], Distance (Dist) heuristic [10], Chronological Backtracking (ChronoBT) [11], duplicate learnts heuristic [12], Conflict History-Based (CHB) heuristic [13], Learning Rate-based Branching (LRB) heuristic [14], and the SLS component [15].

[5] SAT Competitions. 2002. Available online: http://www.satcompetition.org (accessed on 19 November 2019).

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[11] Nadel, A.; Ryvchin, V. Chronological Backtracking. In Proceedings of the Theory and Applications of Satisfiability Testing—SAT 2018, Oxford, UK, 9–12 July 2018; Beyersdorff, O., Wintersteiger, C.M., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 111–121. [Google Scholar]

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[15] Zhang, X.; Cai, S. Relaxed Backtracking with Rephasing. In Proceedings of the SAT Competition 2020, Alghero, Italy, 3–10 July 2020; Solver and Benchmark Descriptions. University of Helsinki, Department of Computer Science: Helsinki, Finland, 2020; Volume B-2020-1, pp. 15–16. [Google Scholar]