SCSat: a soft constraint guided SAT solver

Authors:
Hiroshi Fujita;Miyuki Koshimura;Ryuzo Hasegawa
Affiliations:
Dept. of Informatics, Kyushu University, Fukuoka, Japan;Dept. of Informatics, Kyushu University, Fukuoka, Japan;Dept. of Informatics, Kyushu University, Fukuoka, Japan
Venue:
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Year:
2013

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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.