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Rational Krylov approximation of matrix functions: Numerical methods and optimal pole selection

Abstract:

Matrix functions are a central topic of linear algebra, and problems of their numerical approximation appear increasingly often in scientific computing. We review various rational Krylov methods for the computation of large-scale matrix functions. Emphasis is put on the rational Arnoldi method and variants thereof, namely, the extended Krylov subspace method and the shift-and-invert Arnoldi method, but we also discuss the nonorthogonal generalized Leja point (or PAIN) method. The issue of opt...

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Stefan Guettel More by this author
Publication date:
2012-03-05
URN:
uuid:cd6a7402-1d50-4055-ac28-9b53eace04e1
Local pid:
oai:eprints.maths.ox.ac.uk:1502

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