Abstract
There is a wide consensus, which is supported by the hard experimental evidence of the SAT competitions, that clear progress in SAT solver performance has been observed in recent years. However, in the vast majority of practical applications of SAT, one is expected to use SAT solvers as oracles deciding a possibly large number of propositional formulas. In practice, this is often achieved through the use of incremental SAT. Given this fundamental use of SAT solvers, this paper investigates whether recent improvements in solver performance have an observable positive impact on the overall problem-solving efficiency in settings where incremental SAT is mandatory or at least expected. Our results, obtained on a number of well-known practically significant applications, suggest that most improvements made to SAT solvers in recent years have no positive impact on the overall performance when solvers are used incrementally.
Stepan Kochemazov is supported by the Ministry of Science and Higher Education of Russian Federation, research project no. 075-03-2020-139/2 (goszadanie no. 2019-1339). Joao Marques-Silva is supported by the AI Interdisciplinary Institute ANITI, funded by the French program “Investing for the Future – PIA3” under Grant agreement no. ANR-19-PI3A-0004, and by the H2020-ICT38 project COALA “Cognitive Assisted agile manufacturing for a Labor force supported by trustworthy Artificial intelligence”.
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Audemard, G., Lagniez, J.-M., Simon, L.: Improving glucose for incremental SAT solving with assumptions: application to MUS extraction. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 309–317. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39071-5_23
Audemard, G., Simon, L.: Predicting learnt clauses quality in modern SAT solvers. In: IJCAI, pp. 399–404 (2009)
Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability, 2nd edn. IOS Press, Amsterdam (2021)
Cook, S.A.: The complexity of theorem-proving procedures. In: STOC, pp. 151–158 (1971)
Davis, M., Logemann, G., Loveland, D.W.: A machine program for theorem-proving. Commun. ACM 5(7), 394–397 (1962)
Davis, M., Putnam, H.: A computing procedure for quantification theory. J. ACM 7(3), 201–215 (1960)
Eén, N., Sörensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24605-3_37
Fichte, J.K., Hecher, M., Szeider, S.: A time leap challenge for SAT-solving. In: Simonis, H. (ed.) CP 2020. LNCS, vol. 12333, pp. 267–285. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58475-7_16
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer set solving in practice. Synth. Lect. Artif. Intell. Mach. Learn. 6(3), 1–238 (2012). Morgan & Claypool Publishers
Gomes, C.P., Selman, B., Kautz, H.A.: Boosting combinatorial search through randomization. In: AAAI, pp. 431–437 (1998)
Hickey, R., Bacchus, F.: Speeding up assumption-based SAT. In: Janota, M., Lynce, I. (eds.) SAT 2019. LNCS, vol. 11628, pp. 164–182. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24258-9_11
Ignatiev, A., Morgado, A., Marques-Silva, J.: PySAT: a python toolkit for prototyping with SAT oracles. In: Beyersdorff, O., Wintersteiger, C.M. (eds.) SAT 2018. LNCS, vol. 10929, pp. 428–437. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94144-8_26
Ignatiev, A., Morgado, A., Marques-Silva, J.: RC2: an efficient MaxSAT solver. J. Satisf. Boolean Model. Comput. 11(1), 53–64 (2019)
Järvisalo, M., Heule, M.J.H., Biere, A.: Inprocessing rules. In: Gramlich, B., Miller, D., Sattler, U. (eds.) IJCAR 2012. LNCS (LNAI), vol. 7364, pp. 355–370. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31365-3_28
Katebi, H., Sakallah, K.A., Marques-Silva, J.P.: Empirical study of the anatomy of modern sat solvers. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 343–356. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21581-0_27
Kochemazov, S., Zaikin, O., Semenov, A.A., Kondratiev, V.: Speeding up CDCL inference with duplicate learnt clauses. In: ECAI, pp. 339–346 (2020)
Lagniez, J.-M., Biere, A.: Factoring out assumptions to speed up MUS extraction. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 276–292. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39071-5_21
Li, C., Xiao, F., Luo, M., Manyà, F., Lü, Z., Li, Y.: Clause vivification by unit propagation in CDCL sat solvers. Artif. Intell. 279, 103197 (2020)
Liang, J.H., Ganesh, V., Poupart, P., Czarnecki, K.: Learning rate based branching heuristic for SAT solvers. In: Creignou, N., Le Berre, D. (eds.) SAT 2016. LNCS, vol. 9710, pp. 123–140. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40970-2_9
Liang, J.H., Oh, C., Ganesh, V., Czarnecki, K., Poupart, P.: MapleCOMSPS, MapleCOMSPS\_LRB, MapleCOMSPS\_CHB. In: Procceedings of SAT Competition 2016, vol. B-2016-1, pp. 52–53 (2016)
Luby, M., Sinclair, A., Zuckerman, D.: Optimal speedup of Las Vegas algorithms. Inf. Process. Lett. 47(4), 173–180 (1993)
Luo, M., Li, C., Xiao, F., Manyà, F., Lü, Z.: An effective learnt clause minimization approach for CDCL SAT solvers. In: IJCAI, pp. 703–711 (2017)
Marques-Silva, J.: Search algorithms for satisfiability problems in combinational switching circuits. Ph.D. thesis, University of Michigan (1995)
Marques-Silva, J., Lynce, I.: On improving MUS extraction algorithms. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 159–173. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21581-0_14
Marques-Silva, J., Sakallah, K.A.: GRASP - a new search algorithm for satisfiability. In: ICCAD, pp. 220–227 (1996)
Marques-Silva, J., Sakallah, K.A.: GRASP: a search algorithm for propositional satisfiability. IEEE Trans. Comput. 48(5), 506–521 (1999). https://doi.org/10.1109/12.769433
Irkutsk Supercomputer Center of SB RAS. http://hpc.icc.ru
MaxSAT Evaluation 2018. https://maxsat-evaluations.github.io/2018/
MaxSAT Evaluation 2019. https://maxsat-evaluations.github.io/2019/
MaxSAT Evaluation 2020. https://maxsat-evaluations.github.io/2020/
Möhle, S., Biere, A.: Backing backtracking. In: Janota, M., Lynce, I. (eds.) SAT 2019. LNCS, vol. 11628, pp. 250–266. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24258-9_18
Morgado, A., Dodaro, C., Marques-Silva, J.: Core-guided MaxSAT with soft cardinality constraints. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 564–573. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10428-7_41
Morgado, A., Heras, F., Liffiton, M.H., Planes, J., Marques-Silva, J.: Iterative and core-guided MaxSAT solving: a survey and assessment. Constraints Int. J. 18(4), 478–534 (2013). https://doi.org/10.1007/s10601-013-9146-2
Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: engineering an efficient SAT solver. In: DAC, pp. 530–535 (2001)
Nadel, A., Ryvchin, V.: Chronological backtracking. In: Beyersdorff, O., Wintersteiger, C.M. (eds.) SAT 2018. LNCS, vol. 10929, pp. 111–121. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94144-8_7
Oh, C.: Between SAT and UNSAT: the fundamental difference in CDCL SAT. In: Heule, M., Weaver, S. (eds.) SAT 2015. LNCS, vol. 9340, pp. 307–323. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24318-4_23
Ohrimenko, O., Stuckey, P.J., Codish, M.: Propagation via lazy clause generation. Constraints Int. J. 14(3), 357–391 (2009). https://doi.org/10.1007/s10601-008-9064-x
Piette, C., Hamadi, Y., Saïs, L.: Vivifying propositional clausal formulae. In: ECAI, pp. 525–529 (2008)
Pipatsrisawat, K., Darwiche, A.: A lightweight component caching scheme for satisfiability solvers. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 294–299. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72788-0_28
Ryan, L.: Efficient algorithms for clause-learning SAT solvers. Master’s thesis, School of Computing Science, Simon Fraser University (2004)
Zhang, X., Cai, S.: Relaxed backtracking with rephasing. In: Proceedings of SAT Competition 2020, vol. B-2020-1, pp. 15–16 (2020)
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Kochemazov, S., Ignatiev, A., Marques-Silva, J. (2021). Assessing Progress in SAT Solvers Through the Lens of Incremental SAT. 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_20
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