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Paper II.1

Effect of Ordering on the Performance of Sparse Approximate Inverse Preconditioners

E. Flórez, M.D. Garcia, L. González and G. Montero

Department of Mathematics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

doi:10.4203/ccp.65.2.1

, "Effect of Ordering on the Performance of Sparse Approximate Inverse Preconditioners", in B.H.V. Topping, (Editor), "Finite Elements: Techniques and Developments", Civil-Comp Press, Edinburgh, UK, pp 25-34, 2000. doi:10.4203/ccp.65.2.1

Abstract

We experimentally study how the reordering techniques affect the rate of convergence of preconditioned Krylov subspace methods for nonsymmetric sparse linear systems, where the preconditioner is a sparse approximate inverse. In addition, we show how the reordering reduces the number of entries in the approximate inverse and thus, the amount of storage requirement and computations for a given accuracy. These properties are illustrated with several numerical experiments taken from the discretization of PDE by a finite element method and from a standard matrix collection.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 65 FINITE ELEMENTS: TECHNIQUES AND DEVELOPMENTS Edited by: B.H.V. Topping Paper II.1 Effect of Ordering on the Performance of Sparse Approximate Inverse Preconditioners E. Flórez, M.D. Garcia, L. González and G. Montero Department of Mathematics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain doi:10.4203/ccp.65.2.1 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Effect of Ordering on the Performance of Sparse Approximate Inverse Preconditioners", in B.H.V. Topping, (Editor), "Finite Elements: Techniques and Developments", Civil-Comp Press, Edinburgh, UK, pp 25-34, 2000. doi:10.4203/ccp.65.2.1 Abstract We experimentally study how the reordering techniques affect the rate of convergence of preconditioned Krylov subspace methods for nonsymmetric sparse linear systems, where the preconditioner is a sparse approximate inverse. In addition, we show how the reordering reduces the number of entries in the approximate inverse and thus, the amount of storage requirement and computations for a given accuracy. These properties are illustrated with several numerical experiments taken from the discretization of PDE by a finite element method and from a standard matrix collection. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £80 +P&P)
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