Abstract
The proof system of Dual-Rail MaxSAT (DRMaxSAT) was recently shown to be capable of efficiently refuting families of formulas that are well-known to be hard for resolution, concretely when the MaxSAT solving approach is either MaxSAT resolution or core-guided algorithms. Moreover, DRMaxSAT based on MaxSAT resolution was shown to be stronger than general resolution. Nevertheless, existing experimental evidence indicates that the use of MaxSAT algorithms based on the computation of minimum hitting sets (MHSes), i.e. MaxHS-like algorithms, are as effective, and often more effective, than core-guided algorithms and algorithms based on MaxSAT resolution. This paper investigates the use of MaxHS-like algorithms in the DRMaxSAT proof system. Concretely, the paper proves that the propositional encoding of the pigenonhole and doubled pigenonhole principles have polynomial time refutations when the DRMaxSAT proof system uses a MaxHS-like algorithm.
This work was supported by FCT grants ABSOLV (PTDC/CCI-COM/28986/2017), FaultLocker (PTDC/CCI-COM/29300/2017), SAFETY (SFRH/BPD/120315/2016), and SAMPLE (CEECIND/04549/2017); grant TIN2016-76573-C2-2-P (TASSAT 3); and Simons Foundation grant 578919.
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Notes
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Despite ongoing efforts, and with the exception of families of problem instances known to be hard for resolution, the performance of implementations of proof systems stronger than resolution is still far from what CDCL SAT solvers achieve in practice.
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References
Ansótegui, C., Bonet, M.L., Levy, J.: SAT-based MaxSAT algorithms. Artif. Intell. 196, 77–105 (2013)
Audemard, G., Katsirelos, G., Simon, L.: A restriction of extended resolution for clause learning SAT solvers. In: AAAI (2010)
Bacchus, F., Hyttinen, A., Järvisalo, M., Saikko, P.: Reduced cost fixing in MaxSAT. In: Beck, J.C. (ed.) CP 2017. LNCS, vol. 10416, pp. 641–651. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66158-2_41
Berre, D.L., Parrain, A.: The Sat4j library, release 2.2. JSAT 7(2–3), 59–6 (2010)
Biere, A.: Lingeling, plingeling and treengeling entering the SAT competition 2013. In: Balint, A., Belov, A., Heule, M., Järvisalo, M. (eds.) Proceedings of SAT Competition 2013, Department of Computer Science Series of Publications B, vol. B-2013-1, pp. 51–52. University of Helsinki (2013)
Biere, A.: Lingeling essentials, a tutorial on design and implementation aspects of the SAT solver lingeling. In: Pragmatics of SAT workshop, p. 88 (2014)
Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability, Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press (2009)
Birnbaum, E., Lozinskii, E.L.: Consistent subsets of inconsistent systems: structure and behaviour. J. Exp. Theor. Artif. Intell. 15(1), 25–46 (2003)
Bonet, M.L., Buss, S., Ignatiev, A., Marques-Silva, J., Morgado, A.: Maxsat resolution with the dual rail encoding. In: AAAI. pp. 6565–6572 (2018)
Bonet, M.L., Levy, J., Manyà, F.: Resolution for Max-SAT. Artif. Intell. 171(8–9), 606–618 (2007)
Bryant, R.E., Beatty, D.L., Brace, K.S., Cho, K., Sheffler, T.J.: COSMOS: a compiled simulator for MOS circuits. In: DAC, pp. 9–16 (1987)
Cook, S.A., Reckhow, R.A.: The relative efficiency of propositional proof systems. J. Symb. Log. 44(1), 36–50 (1979)
Davies, J., Bacchus, F.: Solving MAXSAT by solving a sequence of simpler SAT instances. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 225–239. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_19
Davies, J., Bacchus, F.: Exploiting the power of mip solvers in maxsat. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 166–181. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39071-5_13
Davies, J., Bacchus, F.: Postponing optimization to speed Up MAXSAT solving. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 247–262. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40627-0_21
Eén, N., Sörensson, N.: Translating pseudo-boolean constraints into SAT. JSAT 2(1–4), 1–26 (2006)
Elffers, J., Nordström, J.: Divide and conquer: towards faster pseudo-boolean solving. In: IJCAI, pp. 1291–1299 (2018). www.ijcai.org
Fu, Z., Malik, S.: On solving the partial MAX-SAT problem. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 252–265. Springer, Heidelberg (2006). https://doi.org/10.1007/11814948_25
Huang, J.: Extended clause learning. Artif. Intell. 174(15), 1277–1284 (2010)
Ignatiev, A., Morgado, A., Marques-Silva, J.: On Tackling the limits of resolution in SAT solving. In: Gaspers, S., Walsh, T. (eds.) SAT 2017. LNCS, vol. 10491, pp. 164–183. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66263-3_11
Kiesl, B., Rebola-Pardo, A., Heule, M.J.H.: Extended resolution simulates DRAT. In: Galmiche, D., Schulz, S., Sebastiani, R. (eds.) IJCAR 2018. LNCS (LNAI), vol. 10900, pp. 516–531. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94205-6_34
Koshimura, M., Zhang, T., Fujita, H., Hasegawa, R.: QMaxSAT: a partial Max-SAT solver. JSAT 8(1/2), 95–100 (2012)
Larrosa, J., Heras, F., de Givry, S.: A logical approach to efficient Max-SAT solving. Artif. Intell. 172(2–3), 204–233 (2008)
Li, C.M., Manyà, F.: MaxSAT. In: Biere et al. [7], pp. 613–631
Marques-Silva, J., Planes, J.: On using unsatisfiability for solving maximum satisfiability CoRR abs/0712.1097 (2007)
Martins, R., Joshi, S., Manquinho, V., Lynce, I.: Incremental cardinality constraints for MaxSAT. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 531–548. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10428-7_39
Martins, R., Manquinho, V., Lynce, I.: Open-WBO: a modular MaxSAT solver,. In: Sinz, C., Egly, U. (eds.) SAT 2014. LNCS, vol. 8561, pp. 438–445. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09284-3_33
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 18(4), 478–534 (2013)
Morgado, A., Ignatiev, A., Marques-Silva, J.: MSCG: robust core-guided MaxSAT solving. JSAT 9, 129–134 (2015)
Narodytska, N., Bacchus, F.: Maximum satisfiability using core-guided MaxSAT resolution. In: AAAI, pp. 2717–2723 (2014)
Palopoli, L., Pirri, F., Pizzuti, C.: Algorithms for selective enumeration of prime implicants. Artif. Intell. 111(1–2), 41–72 (1999)
Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)
Saikko, P., Berg, J., Järvisalo, M.: LMHS: a SAT-IP hybrid MaxSAT solver. In: Creignou, N., Le Berre, D. (eds.) SAT 2016. LNCS, vol. 9710, pp. 539–546. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40970-2_34
Sinz, C.: Towards an optimal CNF encoding of boolean cardinality constraints. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 827–831. Springer, Heidelberg (2005). https://doi.org/10.1007/11564751_73
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Morgado, A., Ignatiev, A., Bonet, M.L., Marques-Silva, J., Buss, S. (2019). DRMaxSAT with MaxHS: First Contact. In: Janota, M., Lynce, I. (eds) Theory and Applications of Satisfiability Testing – SAT 2019. SAT 2019. Lecture Notes in Computer Science(), vol 11628. Springer, Cham. https://doi.org/10.1007/978-3-030-24258-9_17
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