Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Two Proof Procedures for a Cardinality Based Language in Propositional Calculus
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Inference methods for a pseudo-boolean satisfiability solver
Eighteenth national conference on Artificial intelligence
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Solving linear pseudo-Boolean constraint problems with local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Some search problems are most directly specified by boolean combinations of pseudo-boolean constraints. We study a logic PL(PB) whose formulas are of this form, and design local-search methods to compute models of PL(PB)- theories. In our approach we view a PL(PB)-theory T as a data structure -- a concise representation of a certain propositional CNF theory cl(T) logically equivalent to T. We show that parameters needed by local-search algorithms for CNF theories, such as walksat, can be estimated on the basis of T, without the need to compute cl(T) explicitly. Since cl(T) is often much larger than T, running search based on T promises performance gains. Our experimental results confirm this expectation.