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DATA MINING AND PREDICTION OF SAI TYPE MATRIX PRECONDITIONER  

Kim, Sang-Bae (Department of Mathematics, Hannam University)
Xu, Shuting (Department of Computer Information Systems, Virginia State University)
Zhang, Jun (Department of Computer Science, University of Kentucky)
Publication Information
Journal of applied mathematics & informatics / v.28, no.1_2, 2010 , pp. 351-361 More about this Journal
Abstract
The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods are considered the preferred methods. Selecting a suitable preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The prediction of ILU type preconditioners was considered in [27] where support vector machine(SVM), as a data mining technique, is used to classify large sparse linear systems and predict best preconditioners. In this paper, we apply the data mining approach to the sparse approximate inverse(SAI) type preconditioners to find some parameters with which the preconditioned Krylov subspace method on the linear systems shows best performance.
Keywords
Sparse matrix; preconditioning; sparse approximte inverse; support vector machine;
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