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 Article
Extracting (Easily) Checkable Proofs from a Satisfiability Solver that Employs both Preorder and Postorder Resolution.
TL;DR: Modifications of the classical backtracking-search satisfiability algorithm of Davis, Putnam, Loveland and Logemann (DPLL) are described that are designed to extract checkable proofs of practical length when the formula is believed to be unsatisfiable.
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Program Synthesis using Conflict-Driven Learning
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Recent Improvements in the SMT Solver iSAT.
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Automating component-based system assembly
TL;DR: This work presents a powerful framework for automatically solving the system assembly problem directly from system requirements, and shows how to automatically solve system assembly problems using verification technology that takes advantage of current advances in Boolean satisfiability methods.
Book ChapterDOI
QuBE++: An efficient QBF solver
TL;DR: QuBE++ is the first QBF reasoning engine that uses lazy data structures both for unit clauses propagation and for pure literals detection, and features non-chronological backtracking and a branching heuristic that leverages the information gathered during the backtracking phase.
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