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Krylov Subspace Iterations for Sparse Linear Systems

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Numerical Methods and Software Tools in Industrial Mathematics

Abstract

This chapter is concerned with efficient methods for iterative solution of large sparse systems of linear equations, typically derived from the discretization of an elliptic boundary value problem. In particular, attention is given to the family of Krylov subspace methods, as well as to several preconditioning strategies that are suitable for improving the convergence rates of such iterations.

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Author information

Authors and Affiliations

  1. SINTEF, P.O. Box 124, Blindern, Oslo, 0314, Norway

    Are Magnus Bruaset

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  1. Are Magnus Bruaset
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Editors and Affiliations

  1. SINTEF Applied Mathematics, Blindern, Oslo, N-0314, Norway

    Morten Dæhlen

  2. Department of Informatics, University of Oslo, N-0316, Oslo, Norway

    Aslak Tveito

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Bruaset, A.M. (1997). Krylov Subspace Iterations for Sparse Linear Systems. In: Dæhlen, M., Tveito, A. (eds) Numerical Methods and Software Tools in Industrial Mathematics. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-1984-2_12

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  • DOI: https://doi.org/10.1007/978-1-4612-1984-2_12

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-7367-7

  • Online ISBN: 978-1-4612-1984-2

  • eBook Packages: Springer Book Archive

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