Using Community Structure to Detect Relevant Learnt Clauses

  • Carlos Ansótegui
  • Jesús Giráldez-Cru
  • Jordi Levy
  • Laurent Simon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9340)


Nowadays, Conflict-Driven Clause Learning (CDCL) techniques are one of the key components of modern SAT solvers specialized in industrial instances. Last years, one of the focuses has been put on strategies to select which learnt clauses are removed during the search. Originally, one need for removing clauses was motivated by the finiteness of memory. Recently, it has been shown that more aggressive clause deletion policies may improve solvers performance, even when memory is sufficient. Also, the utility of learnt clauses has been related to the modular structure of industrial SAT instances.

In this paper, we show that augmenting SAT instances with learnt clauses does not always make them easier for the SAT solver. In fact, it makes worse the solver performance in many cases. However, we identify a set of highly useful learnt clauses, and we show that augmenting SAT instances with this set of clauses contributes to improve the solver performance in many cases, especially in satisfiable formulas. These clauses are related to the community structure of the formula, and they can be computed in a fast preprocessing step. This would suggest that the community structure may play an important role in clause deletion policies.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos Ansótegui
    • 1
  • Jesús Giráldez-Cru
    • 2
  • Jordi Levy
    • 2
  • Laurent Simon
    • 3
  1. 1.DIEIUniversitat de LleidaLleidaSpain
  2. 2.Artificial Intelligence Research InstituteSpanish National Research Council (IIIA-CSIC)BarcelonaSpain
  3. 3.LaBRIUniversité de BordeauxTalenceFrance

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