Extending Clause Learning SAT Solvers with Complete Parity Reasoning (extended version)
Abstract
Instances of logical cryptanalysis, circuit verification, and bounded model checking can often be succinctly represented as a combined satisfiability (SAT) problem where an instance is a combination of traditional clauses and parity constraints. This paper studies how such combined problems can be efficiently solved by augmenting a modern SAT solver with an xor-reasoning module in the DPLL(XOR) framework. A new xor-reasoning module that deduces all possible implied literals using incremental Gauss-Jordan elimination is presented. A decomposition technique that can greatly reduce the size of parity constraint matrices while allowing still to deduce all implied literals is presented. It is shown how to eliminate variables occuring only in parity constraints while preserving the decomposition. The proposed techniques are evaluated experimentally.
Cite
@article{arxiv.1207.0988,
title = {Extending Clause Learning SAT Solvers with Complete Parity Reasoning (extended version)},
author = {Tero Laitinen and Tommi Junttila and Ilkka Niemelä},
journal= {arXiv preprint arXiv:1207.0988},
year = {2012}
}