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On Computing Minimal Equivalent Subformulas

  • Anton Belov
  • Mikoláš Janota
  • Inês Lynce
  • Joao Marques-Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7514)

Abstract

A propositional formula in Conjunctive Normal Form (CNF) may contain redundant clauses — clauses whose removal from the formula does not affect the set of its models. Identification of redundant clauses is important because redundancy often leads to unnecessary computation, wasted storage, and may obscure the structure of the problem. A formula obtained by the removal of all redundant clauses from a given CNF formula \({\mathcal{F}}\) is called a Minimal Equivalent Subformula (MES) of \({\mathcal{F}}\). This paper proposes a number of efficient algorithms and optimization techniques for the computation of MESes. Previous work on MES computation proposes a simple algorithm based on iterative application of the definition of a redundant clause, similar to the well-known deletion-based approach for the computation of Minimal Unsatisfiable Subformulas (MUSes). This paper observes that, in fact, most of the existing algorithms for the computation of MUSes can be adapted to the computation of MESes. However, some of the optimization techniques that are crucial for the performance of the state-of-the-art MUS extractors cannot be applied in the context of MES computation, and thus the resulting algorithms are often not efficient in practice. To address the problem of efficient computation of MESes, the paper develops a new class of algorithms that are based on the iterative analysis of subsets of clauses. The experimental results, obtained on representative problem instances, confirm the effectiveness of the proposed algorithms. The experimental results also reveal that many CNF instances obtained from the practical applications of SAT exhibit a large degree of redundancy.

Keywords

Constraint Satisfaction Problem Conjunctive Normal Form Truth Assignment Model Rotation Propositional Formula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anton Belov
    • 1
  • Mikoláš Janota
    • 2
  • Inês Lynce
    • 2
  • Joao Marques-Silva
    • 1
    • 2
  1. 1.CASLUniversity College DublinIreland
  2. 2.IST/INESC-IDTechnical University of LisbonPortugal

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