Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Boosting complete techniques thanks to local search methods
Annals of Mathematics and Artificial Intelligence
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Applying SAT Methods in Unbounded Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the conference on Design, automation and test in Europe
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
GASAT: a genetic local search algorithm for the satisfiability problem
Evolutionary Computation
A new hybrid solution to boost SAT solver performance
Proceedings of the conference on Design, automation and test in Europe
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Combining adaptive noise and look-ahead in local search for SAT
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Structural abstraction of software verification conditions
CAV'07 Proceedings of the 19th international conference on Computer aided verification
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Benchmarking SAT solvers for bounded model checking
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
A parameterized benchmark suite of hard pipelined-machine-verification problems
CHARME'05 Proceedings of the 13 IFIP WG 10.5 international conference on Correct Hardware Design and Verification Methods
Local search for unsatisfiability
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
A Novel Approach to Combine a SLS- and a DPLL-Solver for the Satisfiability Problem
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Boosting local search thanks to CDCL
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
International Journal of Advanced Intelligence Paradigms
Using cross-entropy for satisfiability
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A survey of the satisfiability-problems solving algorithms
International Journal of Advanced Intelligence Paradigms
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Boolean Satisfiability (SAT) solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. SAT solvers are also the building block of more sophisticated decision procedures, including Satisfiability Modulo Theory (SMT) solvers. The large number of applications of SAT yields ever more challenging problem instances, and motivate the development of more efficient algorithms. Recent work studied hybrid approaches for SAT, which involves integrating incomplete and complete SAT solvers. This paper proposes a number of improvements to hybrid SAT solvers. Experimental results demonstrate that the proposed optimizations are effective. The resulting algorithms in general perform better and, more importantly, are significantly more robust.