Journal of the ACM (JACM)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Recovering and Exploiting Structural Knowledge from CNF Formulas
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CL '00 Proceedings of the First International Conference on Computational Logic
Integrating Equivalency Reasoning into Davis-Putnam Procedure
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extending SAT Solvers to Cryptographic Problems
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Building a Hybrid SAT Solver via Conflict-Driven, Look-Ahead and XOR Reasoning Techniques
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Extending Clause Learning DPLL with Parity Reasoning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Equivalence Class Based Parity Reasoning with DPLL(XOR)
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
March_eq: implementing additional reasoning into an efficient look-ahead SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Classifying and propagating parity constraints
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Modern conflict-driven clause learning (CDCL) SAT solvers are very good in solving conjunctive normal form (CNF) formulas. However, some application problems involve lots of parity (xor) constraints which are not necessarily efficiently handled if translated into CNF. This paper studies solving CNF formulas augmented with xor-clauses in the DPLL(XOR) framework where a CDCL SAT solver is coupled with a separate xor-reasoning module. New techniques for analyzing xor-reasoning derivations are developed, allowing one to obtain smaller CNF clausal explanations for xor-implied literals and also to derive and learn new xor-clauses. It is proven that these new techniques allow very short unsatisfiability proofs for some formulas whose CNF translations do not have polynomial size resolution proofs, even when a very simple xor-reasoning module capable only of unit propagation is applied. The efficiency of the proposed techniques is evaluated on a set of challenging logical cryptanalysis instances.