On Reducing Clause DataBase in Glucose

11 pages•Published: September 27, 2016

Abstract

Modern CDCL SAT solvers generally save the variable value when backtracking. We propose a new measure called nbSAT based on the saved assignment to predict the usefulness of a learnt clause when reducing clause database in Glucose 3.0. Roughly speaking, The nbSAT value of a learnt clause is equal to the number of literals satised by the current partial assignment plus the number of other literals that would be satised by the saved assignment. We study the nbSAT measure by empirically show that it may be more accurate than the LBD measure originally used in Glucose. Based on this study, we implement an improvement in Glucose 3.0 to remove half of learnt clauses with large nbSAT values instead of half of clauses with large LBD values. This improvement, together with a resolution method to keep the learnt clauses or resolvents produced using a learnt clause that subsume an original clause, makes Glucose 3.0 more eective for the application and crafted instances from the SAT 2014 competition.

Keyphrases: glucose, learnt clause database, nbsat

In: Boris Konev, Stephan Schulz and Laurent Simon (editors). IWIL-2015. 11th International Workshop on the Implementation of Logics, vol 40, pages 67-77.

Links:	https://easychair.org/publications/paper/zpPk
	https://doi.org/10.29007/b8kb

BibTeX entry

@inproceedings{IWIL-2015:Reducing_Clause_DataBase_Glucose,
  author    = {Chu Min Li and Fan Xiao and Ruchu Xu},
  title     = {On Reducing Clause DataBase in Glucose},
  booktitle = {IWIL-2015. 11th International Workshop on the Implementation of Logics},
  editor    = {Boris Konev and Stephan Schulz and Laurent Simon},
  series    = {EPiC Series in Computing},
  volume    = {40},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/zpPk},
  doi       = {10.29007/b8kb},
  pages     = {67-77},
  year      = {2016}}

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