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Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
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Communications of the ACM
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Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
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Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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Proceedings of the conference on Design, automation and test in Europe
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IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Journal of Artificial Intelligence Research
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CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Exact cover via satisfiability: an empirical study
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Variable and clause ordering in an FSA approach to propositional satisfiability
CIAA'11 Proceedings of the 16th international conference on Implementation and application of automata
Feasibility analysis for robustness quantification by symbolic model checking
Formal Methods in System Design
A finite state intersection approach to propositional satisfiability
Theoretical Computer Science
Dsharp: fast d-DNNF compilation with sharpSAT
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
SAT-Based analysis and quantification of information flow in programs
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
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We introduce sharpSAT, a new #SAT solver that is based on the well known DPLL algorithm and techniques from SAT and #SAT solvers. Most importantly, we introduce an entirely new approach of coding components, which reduces the cache size by at least one order of magnitude, and a new cache management scheme. Furthermore, we apply a well known look ahead based on BCP in a manner that is well suited for #SAT solving. We show that these techniques are highly beneficial, especially on large structured instances, such that our solver performs significantly better than other #SAT solvers.