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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A Framework for Certified Boolean Branch-and-Bound Optimization
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Satisfiability Checking for PC(ID)
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QBF reasoning on real-world instances
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Functional test generation based on word-level SAT
TL;DR: In this article, two approaches that enhance the capability of functional test generation by preserving arithmetic operators in the design are presented. But they are not directly applicable to behavioral and RTL designs containing significant arithmetic components.
Proceedings ArticleDOI
Improving SAT-solving with Machine Learning
TL;DR: This project aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem, and used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula.
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