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Home/e-Resources/Artificial Intelligence & Machine Learning/Efficient local search for very large-scale satisfiability problems
PeriodicalsArtificial Intelligence & Machine Learning

Efficient local search for very large-scale satisfiability problems

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Abstract & Overview

The satisfiability problem (SAT) is a fundamental problem in mathematical logic, inference, automated reasoning, and computing theory. In this correspondence, we report the results of applying local search techniques to solve the satisfiability problem. While a traditional resolution-based algorithm is not able to handle even moderately sized inference problems, a local search algorithm, tested through years of real program execution, is capable of computing very large-scale inference problems with fast, robust convergence. On a workstation computer, for example, it is able to solve a satisfiability problem with 50,000 clauses and 5,000 variables in a few seconds.

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Jun Hua Gu (1992). Efficient local search for very large-scale satisfiability problems. ACM SIGART Bulletin, 3(1), 8–12. https://doi.org/10.1145/130836.130837

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Gu, J. H. (1992). Efficient local search for very large-scale satisfiability problems. ACM SIGART Bulletin, 3(1), 8–12. https://doi.org/10.1145/130836.130837
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