The deflated conjugate gradient method: Convergence, perturbation and accuracy

Deflation techniques for Krylov subspace methods have seen a lot of attention in recent years. They provide means to improve the convergence speed of these methods by enriching the Krylov subspace with a deflation subspace. The most common approach for the construction of deflation subspaces is to u...

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Vydané v:Linear algebra and its applications Ročník 515; s. 111 - 129
Hlavní autori: Kahl, K., Rittich, H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Inc 15.02.2017
American Elsevier Company, Inc
Predmet:
Conjugate gradient method
Conjugate gradients
Conjugates
Convergence
Deflation
Eigenvectors
Forecasting
Linear algebra
Multigrid
Multigrid methods
Perturbation
Subspace methods
Subspaces
65F10
Multigrid
Deflation
Conjugate gradients
Perturbation
65F08
Convergence
ISSN:0024-3795, 1873-1856
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Shrnutí:Deflation techniques for Krylov subspace methods have seen a lot of attention in recent years. They provide means to improve the convergence speed of these methods by enriching the Krylov subspace with a deflation subspace. The most common approach for the construction of deflation subspaces is to use (approximate) eigenvectors, but also more general subspaces are applicable. In this paper we discuss two results concerning the accuracy requirements within the deflated CG method. First we show that the effective condition number which bounds the convergence rate of the deflated conjugate gradient method depends asymptotically linearly on the size of the perturbations in the deflation subspace. Second, we discuss the accuracy required in calculating the deflating projection. This is crucial concerning the overall convergence of the method, and also allows to save some computational work. To show these results, we use the fact that as a projection approach deflation has many similarities to multigrid methods. In particular, recent results relate the spectra of the deflated matrix to the spectra of the error propagator of twogrid methods. In the spirit of these results we show that the effective condition number can be bounded by the constant of a weak approximation property.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0024-3795
1873-1856
DOI:10.1016/j.laa.2016.10.027