Abstract
In parallel MaxSAT solving, sharing learned clauses is expected to help to further prune the search space and boost the performance of a parallel solver. However, not all learned clauses should be shared since it could lead to an exponential blow up in memory and to sharing many irrelevant clauses. The main question is which learned clauses should be shared among the different threads. This paper reviews the existing heuristics for sharing learned clauses, namely, static and dynamic heuristics. Moreover, a new heuristic for clause sharing is presented based on freezing shared clauses. Shared clauses are only incorporated into the solver when they are expected to be useful in the near future. Experimental results show the importance of clause sharing and that the freezing heuristic outperforms other clause sharing heuristics.
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References
AnsĂ³tegui, C., Bonet, M.L., Levy, J.: Solving (Weighted) Partial MaxSAT through Satisfiability Testing. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 427–440. Springer, Heidelberg (2009)
Audemard, G., Lagniez, J.-M., Mazure, B., SaĂ¯s, L.: On Freezing and Reactivating Learnt Clauses. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 188–200. Springer, Heidelberg (2011)
Audemard, G., Simon, L.: Predicting Learnt Clauses Quality in Modern SAT Solvers. In: International Joint Conferences on Artificial Intelligence, pp. 399–404 (2009)
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)
Hamadi, Y., Jabbour, S., Piette, C., Sais, L.: Deterministic Parallel DPLL: System Description. In: Pragmatics of SAT Workshop (2011)
Hamadi, Y., Jabbour, S., Sais, L.: Control-Based Clause Sharing in Parallel SAT Solving. In: International Joint Conferences on Artificial Intelligence, pp. 499–504 (2009)
Li, C.M., Manyà , F.: MaxSAT, Hard and Soft Constraints. In: Handbook of Satisfiability, pp. 613–631. IOS Press (2009)
Manquinho, V., Marques-Silva, J., Planes, J.: Algorithms for Weighted Boolean Optimization. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 495–508. Springer, Heidelberg (2009)
Martins, R., Manquinho, V., Lynce, I.: Exploiting Cardinality Encodings in Parallel Maximum Satisfiability. In: International Conference on Tools with Artificial Intelligence, pp. 313–320 (2011)
Martins, R., Manquinho, V., Lynce, I.: Parallel Search for Boolean Optimization. In: RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (2011)
Zhang, L., Madigan, C.F., Moskewicz, M.W., Malik, S.: Efficient Conflict Driven Learning in Boolean Satisfiability Solver. In: International Conference on Computer-Aided Design, pp. 279–285 (2001)
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Martins, R., Manquinho, V., Lynce, I. (2012). Clause Sharing in Parallel MaxSAT. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_44
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DOI: https://doi.org/10.1007/978-3-642-34413-8_44
Publisher Name: Springer, Berlin, Heidelberg
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