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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Journal ArticleDOI
Conflict-driven answer set solving: From theory to practice
TL;DR: An approach to computing answer sets of logic programs, based on concepts successfully applied in Satisfiability (SAT) checking, to view inferences in Answer Set Programming (ASP) as unit propagation on nogoods, and presents the first full-fledged algorithmic framework for native conflict-driven ASP solving.
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Efficient Solving of Large Non-linear Arithmetic Constraint Systems with Complex Boolean Structure
TL;DR: This work provides a tight integration of recent SAT solving techniques with interval-based arithmetic constraint solving, able to handle large constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand arithmetic constraints over some thousands of variables.
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ManySAT: a Parallel SAT Solver
TL;DR: In this paper, ManySAT a new portfolio-based parallel SAT solver is thoroughly described, which benefits from the main weaknesses of modern SAT solvers: their sensitivity to parameter tuning and their lack of robustness.
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DPLL(T): Fast Decision Procedures
TL;DR: This work proposes a new approach, namely a general DPLL(X) engine, whose parameter X can be instantiated with a specialized solver Solver T for a given theory T, thus producing a systemDPLL(T).
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Towards understanding and harnessing the potential of clause learning
TL;DR: The first precise characterization of clause learning as a proof system (CL) is presented, and it is shown that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution satisfying a natural property.
References
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