Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Paturi, P. Pudlák, F. Zane (1997)
Satisfiability Coding LemmaProceedings 38th Annual Symposium on Foundations of Computer Science
Dale Schuurmans, F. Southey (2000)
Local search characteristics of incomplete SAT procedures
(2001)
UnitWalk: A new SAT solver that uses local search guided by unit clause elimination
M. Velev, R. Bryant (2001)
Effective use of boolean satisfiability procedures in the formal verification of superscalar and VLIW
B. Monien, Ewald Speckenmeyer (1985)
Solving satisfiability in less than 2n stepsDiscret. Appl. Math., 10
E. Koutsoupias, C. Papadimitriou (1992)
On the Greedy Algorithm for SatisfiabilityInf. Process. Lett., 43
E. Hirsch (2000)
SAT Local Search Algorithms: Worst-Case StudyJournal of Automated Reasoning, 24
(1998)
Stochastic local search – method
Hantao Zhang (1994)
Solving Open Quasigroup Problems by Propositional Reasoning
M. Moskewicz, Conor Madigan, Ying Zhao, Lintao Zhang, S. Malik (2001)
Chaff: engineering an efficient SAT solverProceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232)
David McAllester, B. Selman, Henry Kautz (1997)
Evidence for Invariants in Local Search
J. Gu, Q. Gu (1994)
Average Time Complexity of the SAT 1.2 Algorithm
Hantao Zhang, M. Stickel (2000)
Implementing the Davis–Putnam MethodJournal of Automated Reasoning, 24
Laurent Simon, P. Chatalic (2001)
SatEx: A Web-based Framework for SAT ExperimentationElectron. Notes Discret. Math., 9
(1984)
Obere Komplexitätsschranken für TAUT-Entscheidungen
Chu Li, Anbu Anbulagan (1997)
Heuristics Based on Unit Propagation for Satisfiability Problems
M. Velev, R. Bryant (2001)
Effective use of Boolean satisfiability procedures in the formal verification of superscalar and VLIW microprocessorsProceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232)
U. Schöning (1999)
A probabilistic algorithm for k-SAT and constraint satisfaction problems40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039)
Laurent Simon, Daniel Berre, E. Hirsch (2004)
The SAT2002 competitionAnnals of Mathematics and Artificial Intelligence, 43
Bart Selman, Hector Levesque, David Mitchell (1992)
A New Method for Solving Hard Satisfiability Problems
H. Hoos, T. Stützle, Ian Gent, H. Maaren, T. Walsh (2000)
SATLIB: An Online Resource for Research on SAT
B. Selman, Henry Kautz, Bram Cohen (1994)
Noise Strategies for Improving Local Search
S. Prestwich (2001)
Local Search and Backtracking vs Non-Systematic Backtracking
E. Dantsin, A. Goerdt, E. Hirsch, R. Kannan, J. Kleinberg, C. Papadimitriou, P. Raghavan, U. Schöning (2002)
A deterministic (2-2/(k+1))n algorithm for k-SAT based on local searchTheor. Comput. Sci., 289
J. Gu, Q.-P. Gu (1994)
Average time complexity of the SAT1.2 algorithm, in: Proceedings of the 5th Annual International Symposium on Algorithms and Computation, ISAAC’94Lecture Notes in Computer Science, 834
(1998)
An improved exponentialtime algorithm for kSAT , in : Proceedings of the 39 th Annual IEEE Symposium on Foundations of
Joao Marques-Silva, K. Sakallah (1999)
GRASP: A Search Algorithm for Propositional SatisfiabilityIEEE Trans. Computers, 48
Lei Zheng, Peter Stuckey (2002)
Improving SAT Using 2SAT
C. Papadimitriou (1991)
On selecting a satisfying truth assignment[1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science
Ian Gent, T. Walsh (1995)
Unsatisfied Variables in Local Search
A. Kamath, N. Karmarkar, K. Ramakrishnan, M. Resende (1992)
A continuous approach to inductive inferenceMathematical Programming, 57
Y. Asahiro, K. Iwama, Eiji Miyano (1993)
Random generation of test instances with controlled attributes
H. Hoos (1999)
On the Run-time Behaviour of Stochastic Local Search Algorithms for SAT
B. Monien, E. Speckenmeyer (1985)
Solving satisfiability in less then 2 n stepsDiscrete Applied Mathematics, 10
Ian Gent, T. Walsh (1993)
Towards an Understanding of Hill-Climbing Procedures for SAT
(1983)
Two propositional proof systems based on the splitting method
(1993)
Towards an understanding of hillclimbing procedures for SAT , in : Proceedings of the 11 th National Conference on Artificial Intelligence
David Johnson, M. Trick (1996)
Cliques, Coloring, and Satisfiability, 26
H. Hoos, T. Stützle (2000)
Local Search Algorithms for SAT: An Empirical EvaluationJournal of Automated Reasoning, 24
(1993)
A de ision pro edure for propositional logi
R. Schuler, U. Schöning, O. Watanabe (2001)
An Improved Randomized Algorithm for 3-SAT
J. Gu (1992)
Efficient local search for very large-scale satisfiability problemsIntelligence\/sigart Bulletin, 3
B. Selman (1992)
A New Method for Solving Hard Satis ability Problems
Joseph Culbersony, Ian Gentz, Holger Hoosx (2000)
On the Probabilistic Approximate Completeness of WalkSAT for 2-SAT
J. Gu (1992)
Efficient local search for very large-scale satisfiability problemsACM SIGART Bulletin, 3
E. Dantsin, E. Hirsch, S. Ivanov, M. Vsemirnov (2003)
Algorithms for Sat and Upper Bounds on Their ComplexityJournal of Mathematical Sciences, 118
R. Paturi, P. Pudlák, M. Saks, F. Zane (1998)
An improved exponential-time algorithm for k-SATProceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280)
J. Gu, P. Purdom, J. Franco, B. Wah (1996)
Algorithms for the satisfiability (SAT) problem: A survey
E. Dantsin, A. Goerdt, E.A. Hirsch, R. Kannan, J. Kleinberg, C. Papadimitriou, P. Raghavan, U. Schöning (2002)
A deterministic (2 − 2 (k + 1)) n algorithm for k-SAT based on local searchTheoretical Computer Science, 289
T. Larrabee (1992)
Test pattern generation using Boolean satisfiabilityIEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 11
In this paper we present a new randomized algorithm for SAT, i.e., the satisfiability problem for Boolean formulas in conjunctive normal form. Despite its simplicity, this algorithm performs well on many common benchmarks ranging from graph coloring problems to microprocessor verification. Our algorithm is inspired by two randomized algorithms having the best current worst-case upper bounds ([27,28] and [30,31]). We combine the main ideas of these algorithms in one algorithm. The two approaches we use are local search (which is used in many SAT algorithms, e.g., in GSAT [34] and WalkSAT [33]) and unit clause elimination (which is rarely used in local search algorithms). In this paper we do not prove any theoretical bounds. However, we present encouraging results of computational experiments comparing several implementations of our algorithm with other SAT solvers. We also prove that our algorithm is probabilistically approximately complete (PAC).
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Dec 31, 2004
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.