Many applications of SAT solving can profit from minimal models-a partial variable assignment that is still a witness for satisfiability. Examples include software verification, model checking, and counterexample-guided abstraction refinement. In this paper, we examine how a given model can be minimized for SAT instances that have been obtained by Tseitin encoding of a full propositional logic formula. Our approach uses a SAT solver to efficiently minimize a given model, focusing on only the input variables. Experiments show that some models can be reduced by over 50 percent. © 2013 Springer-Verlag.
CITATION STYLE
Iser, M., Sinz, C., & Taghdiri, M. (2013). Minimizing models for Tseitin-encoded SAT instances. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7962 LNCS, pp. 224–232). https://doi.org/10.1007/978-3-642-39071-5_17
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