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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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
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Speedup techniques utilized in modern SAT solvers
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An Explicating Theorem Prover for Quantified Formulas
TL;DR: This paper describes the design and performance of the explicating theorem prover, and describes new variations on the traditional Nelson-Oppen style of theorem provers inspired by fast propositional satisfiability solvers and proof-generating decision procedures.
Proceedings ArticleDOI
FPGA acceleration of enhanced boolean constraint propagation for SAT solvers
Jason Thong,Nicola Nicolici +1 more
TL;DR: The enhanced BCP core was implemented and integrated with a simple software SAT solver which communicates over PCI Express, and hardware performance counters show that a single processing engine is up to 4x faster than a state-of-the-art software SATsolver.
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An efficient solver for weighted Max-SAT
TL;DR: A new branch and bound algorithm for weighted Max-SAT, called Lazy, is presented which incorporates original data structures and inference rules, as well as a lower bound of better quality.
Proceedings ArticleDOI
Extending modern SAT solvers for models enumeration
TL;DR: This paper proposes an extension of Conflict Driven Clause Learning (CDCL) based SAT solvers to deal with the problem of enumerating all models of a Boolean formula in conjunctive normal form (CNF).
References
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