Efficient conflict driven learning in a boolean satisfiability solver
Lintao Zhang,Conor F. Madigan,Matthew H. Moskewicz,Sharad Malik +3 more
- pp 279-285
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TLDR
This paper generalizes various conflict driven learning strategies in terms of different partitioning schemes of the implication graph to re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes.Abstract:
One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.read more
Citations
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Book ChapterDOI
Constraint Answer Set Solving
TL;DR: An algorithmic framework for conflict-driven ASP solving that exploits CP solving capacities is developed, combining ASP solver clingo with the generic CP solver gecode, and its computational impact is empirically investigated.
Journal ArticleDOI
Propositional Satisfiability and Constraint Programming: A comparative survey
TL;DR: This survey overviews the two areas of Propositional Satisfiability and Constraint Programming in a comparative way, emphasizing the similarities and differences between the two and the points where one technology can benefit from ideas or experience acquired from the other.
Journal ArticleDOI
MINIMAXSAT: an efficient weighted max-SAT solver
TL;DR: A wide set of solving alternatives on a broad set of optimization benchmarks indicates that the performance of MINIMAXSAT is usually close to the best specialized alternative and, in some cases, even better.
BookDOI
Theory and Applications of Satisfiability Testing
TL;DR: A case study for Efficient Implementation of Algorithms of SAT Solvers and the Interaction Between Inference and Branching Heuristics.
Proceedings ArticleDOI
Iterative Abstraction using SAT-based BMC with Proof Analysis
TL;DR: This work uses the proofanalysis technique with SAT-based BMC, in order to generate effective abstract models and demonstrates the effectiveness of the prototype implementation on several large industry designs.
References
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