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Optimizing Two-Level Preconditionings for the Conjugate Gradient Method

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Large-Scale Scientific Computing (LSSC 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2179))

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Abstract

The construction of efficient iterative linear equation solvers for ill-conditioned general symmetric positive definite systems is discussed. Certain known two-level conjugate gradient preconditioning techniques are presented in a uniform way and are further generalized and optimized with respect to the spectral or the K-condition numbers. The resulting constructions have shown to be useful for the solution of largescale ill-conditioned symmetric positive definite linear systems.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Nijmegen, Toernooiveld, 6525, ED Nijmegen, The Netherlands

    Owe Axelsson

  2. Center for Supercomputer and Massively Parallel Applications, Computing Center of Russian Academy of Sciences, Vavilova 40, Moscow, 117967, Russia

    Igor Kaporin

Authors
  1. Owe Axelsson
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  2. Igor Kaporin
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Editor information

Editors and Affiliations

  1. Central Laboratory for Parallel Processing, Bulgarian Academy of Sciences, Acad G. Bontchev, 1113, Sofia, Bulgaria

    Svetozar Margenov

  2. DTU, UNI•C, Danish IT Centre for Education and Research, Bldg. 304, 2800, Lyngby, Denmark

    Jerzy Waśniewski

  3. University of Rousse, Studentska 8, 7017, Rousse, Bulgaria

    Plamen Yalamov

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© 2001 Springer-Verlag Berlin Heidelberg

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Axelsson, O., Kaporin, I. (2001). Optimizing Two-Level Preconditionings for the Conjugate Gradient Method. In: Margenov, S., Waśniewski, J., Yalamov, P. (eds) Large-Scale Scientific Computing. LSSC 2001. Lecture Notes in Computer Science, vol 2179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45346-6_1

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  • DOI: https://doi.org/10.1007/3-540-45346-6_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43043-8

  • Online ISBN: 978-3-540-45346-8

  • eBook Packages: Springer Book Archive

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