DocumentCode :
1903364
Title :
Extending Clause Learning SAT Solvers with Complete Parity Reasoning
Author :
Laitinen, Tiina ; Junttila, T. ; Niemela, I.
Author_Institution :
Dept. of Inf. & Comput. Sci., Aalto Univ., Aalto, Finland
Volume :
1
fYear :
2012
fDate :
7-9 Nov. 2012
Firstpage :
65
Lastpage :
72
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 still allowing 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.
Keywords :
computability; constraint theory; inference mechanisms; learning (artificial intelligence); DPLL framework; SAT problem; bounded model checking; circuit verification; clause learning SAT solver; complete parity reasoning; decomposition technique; incremental Gauss-Jordan elimination; logical cryptanalysis; parity constraint matrices; satisfiability problem; xor-reasoning module; Benchmark testing; Ciphers; Cognition; Equations; Matrix decomposition; Radiation detectors; Boolean satisfiability; Gaussian elimination; parity constraints; parity reasoning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on
Conference_Location :
Athens
ISSN :
1082-3409
Print_ISBN :
978-1-4799-0227-9
Type :
conf
DOI :
10.1109/ICTAI.2012.18
Filename :
6495030
Link To Document :
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