HUANG Shaobin, LI Ya, LI Yanmei, “An SVM-Based Prediction Method for Solving SAT Problems,” Chinese Journal of Electronics, vol. 28, no. 2, pp. 246-252, 2019, doi: 10.1049/cje.2019.01.004
Citation: HUANG Shaobin, LI Ya, LI Yanmei, “An SVM-Based Prediction Method for Solving SAT Problems,” Chinese Journal of Electronics, vol. 28, no. 2, pp. 246-252, 2019, doi: 10.1049/cje.2019.01.004

An SVM-Based Prediction Method for Solving SAT Problems

doi: 10.1049/cje.2019.01.004
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  • Corresponding author: LI Ya (corresponding author) was born in 1985. She is a Ph.D. candidate in College of Computer Science and Technology of Harbin Engineering University. Her research area covers software engineering, fault locations and model checking. (
  • Received Date: 2016-06-12
  • Rev Recd Date: 2017-02-13
  • Publish Date: 2019-03-10
  • We show how Support vector machines (SVM) can be applied to the Satisfiability (SAT) problem and how their prediction results can be naturally applied to both incomplete and complete SAT solvers. SVM is used for the classification of the variables in the SAT problem and the classification results are the assignment of the variables. And we also present empirical results of applying SVM to instances of the SAT problem from the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) archive and compare them against the results of other incomplete and complete algorithms for the SAT problem.
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