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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Too much information: Why CDCL solvers need to forget learned clauses
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SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling
TL;DR: A practical MFS mining algorithm that targets large MFSs, named SATMargin, that adopts random walk in the search space to perform efficient search and utilizes a customized conflict learning Boolean Satisfiability (SAT) algorithm to accelerate SM queries.
Dissertation
Search-space Aware Learning Techniques for Unbounded Model Checking and Path Delay Testing
TL;DR: This dissertation targets two related problems in Design Verification and Testing: Unbounded Model Checking and Path Delay Fault Testing, that commonly suffer from extremely large memory requirements, and proposes efficient representations and intelligent learning techniques that reason on the problem structure and take advantage of the repeated search space, thereby alleviating the memory required and time taken to solve these problems.
References
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