Approximate Model Counting Via Extension Rule and Clause Reduction
Authors: 
Hao Liu, Ziao Ni, Wenyang Zhou, Tongbo Zhang, Shuai LüAuthors Info & Claims
Hao Liu, Ziao Ni, Wenyang Zhou, Tongbo Zhang, Shuai LüAuthors Info & ClaimsPages 40 - 45
Abstract
Model counting is a problem to count different truth assignments for a given propositional formula. Approximate model counting via extension rule is a feasible method. In this paper we introduce the technique of clause reduction and avoiding multiple accesses to enhance the performance of #SAT solver. Using clause reduction can eliminate short clauses and restrict the size of the residual formula. We also optimize the code to avoid getting access to the same variable for several times. We combine these two optimizations with extension rule to achieve ERCRApprox algorithm. In addition, we control the upper bound of the approximate dispersion of ERCRApprox algorithm in a feasible method. Experiment results show that the algorithm increases the efficiency for solving model counting especially when some short clauses exist and the approximate dispersion in each case is always acceptable.
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Index Terms
- Approximate Model Counting Via Extension Rule and Clause Reduction
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Published In
May 2018
249 pages
ISBN:9781450364966
DOI:10.1145/3232116
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In-Cooperation
- Guilin: Guilin University of Technology, Guilin, China
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
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Association for Computing Machinery
New York, NY, United States
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Published: 19 May 2018
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ICIIP '18
ICIIP '18: 2018 3rd International Conference on Intelligent Information Processing
May 19 - 20, 2018
Guilin, China
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Overall Acceptance Rate 87 of 367 submissions, 24%
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