Abstract
SCSat is a SAT solver aimed at quickly finding a model for hard satisfiable instances using soft constraints. Soft constraints themselves are not necessarily maximally satisfied and may be relaxed if they are too strong to obtain a model. Appropriately given soft constraints can reduce search space drastically without losing many models, thus help find a model faster. In this way, we have succeeded to obtain several rare Ramsey graphs which contribute to raise the known best lower bound for the Ramsey number R(4,8) from 56 to 58. © 2013 Springer-Verlag.
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Fujita, H., Koshimura, M., & Hasegawa, R. (2013). SCSat: A soft constraint guided SAT solver. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7962 LNCS, pp. 415–421). https://doi.org/10.1007/978-3-642-39071-5_32
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