• 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