A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Boosting complete techniques thanks to local search methods
Annals of Mathematics and Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
UnitWalk: A New SAT Solver that Uses Local Search Guided by Unit Clause Elimination
Annals of Mathematics and Artificial Intelligence
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Boosting a complete technique to find MSS and MUS thanks to a local search oracle
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scaling and probabilistic smoothing: dynamic local search for unweighted MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Variable dependency in local search: prevention Is better than cure
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
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
MUST: provide a finer-grained explanation of unsatisfiability
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Towards more effective unsatisfiability-based maximum satisfiability algorithms
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Relaxed DPLL Search for MaxSAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Automated design debugging with maximum satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Boosting local search thanks to CDCL
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Improving parallel local search for SAT
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Lower bounds and upper bounds for MaxSAT
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
From sequential to parallel local search for SAT
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Hyperplane initialized local search for MAXSAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A survey of the satisfiability-problems solving algorithms
International Journal of Advanced Intelligence Paradigms
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Systematic search and local search paradigms for combinatorial problems are generally believed to have complementary strengths. Nevertheless, attempts to combine the power of the two paradigms have had limited success, due in part to the expensive information communication overhead involved. We propose a hybrid strategy based on shared memory, ideally suited for multi-core processor architectures. This method enables continuous information exchange between two solvers without slowing down either of the two. Such a hybrid search strategy is surprisingly effective, leading to substantially better quality solutions to many challenging Maximum Satisfiability (MaxSAT) instances than what the current best exact or heuristic methods yield, and it often achieves this within seconds. This hybrid approach is naturally best suited to MaxSAT instances for which proving unsatisfiability is already hard; otherwise the method falls back to pure local search.