Proceedings ArticleDOI
Chaff: engineering an efficient SAT solver
Matthew W. Moskewicz,Conor F. Madigan,Ying Zhao,Lintao Zhang,Sharad Malik +4 more
- pp 530-535
TLDR
The development of a new complete solver, Chaff, is described which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy.Abstract:
Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.read more
Citations
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Journal ArticleDOI
Search in the patience game ‘Black Hole’
Ian P. Gent,Christopher Jefferson,Tom Kelsey,Inês Lynce,Ian Miguel,Peter Nightingale,Barbara M. Smith,S. Armagan Tarim +7 more
TL;DR: An evaluation of different AI search paradigms applied to a natural planning problem, namely Planning, Constraint Programming, SAT, Mixed-Integer Programming and a specialised solver shows that Black Hole is winnable approximately 87% of the time and that given instances can be trivially solved, easy to solve, hard to solve and even intractable, depending on the AI methodology used to obtain solutions.
Proceedings ArticleDOI
Safe composition of non-monotonic features
TL;DR: It is observed that more expressive features increase the complexity of developed programs rapidly -- up to the point where tools and automated concepts as presented in this paper are indispensable for verification.
Proceedings ArticleDOI
Embedded tutorial: formal equivalence checking between system-level models and RTL
A. Koelbl,Yuan Lu,Anmol Mathur +2 more
TL;DR: This tutorial discusses how to formally verify sequential equivalence between SLMs and RTL, for both timed and untimed models.
Dissertation
Nogood processing in csps
TL;DR: This thesis proposes that a fundamental restriction of nogood learning, which is intended to be the analogous to clause learning in CSPs, can be lifted and shows that more efficient methods can be integrated into specific global constraints and demonstrated on several widely used global constraints.
Proceedings ArticleDOI
Local Search for Boolean Relations on the Basis of Unit Propagation
TL;DR: A restricted version of the method, used for unit clause derivation and equivalent-literal identification, is implemented in a preprocessor engine for a SAT-solver and shows that the proposed technique is useful for solving real-world instances in the formal verification domain.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Journal ArticleDOI
Tabu Search—Part II
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.
Book ChapterDOI
Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey
TL;DR: In this article, the authors survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory, and indicate some problems for future research and include a selective bibliography.
Book
A machine program for theorem-proving
TL;DR: The programming of a proof procedure is discussed in connection with trial runs and possible improvements.