On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Discrete Applied Mathematics
Justification-Based Local Search with Adaptive Noise Strategies
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Justification-Based Non-Clausal Local Search for SAT
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Improving Variable Selection Process in Stochastic Local Search for Propositional Satisfiability
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Applying GSAT to non-clausal formulas
Journal of Artificial Intelligence Research
Building structure into local search for SAT
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A stochastic non-CNF SAT solver
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Speeding-up non-clausal local search for propositional satisfiability with clause learning
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Improved local search for circuit satisfiability
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Stochastic local search for non-clausal and circuit satisfiability
Stochastic local search for non-clausal and circuit satisfiability
Hi-index | 0.00 |
We develop a novel circuit-level stochastic local search (SLS) method D-CRSat for Boolean satisfiability by integrating a structure-based heuristic into the recent CRSat algorithm. D-CRSat significantly improves on CRSat on real-world application benchmarks on which other current CNF and circuit-level SLS methods tend to perform weakly. We also give an intricate proof of probabilistically approximate completeness for D-CRSat, highlighting key features of the method.