The convergence behaviour of a number of algorithms based on minimizing residual norms over Krylov subspaces is not well understood. Residual or error bounds currently available are either too loose or depend on unknown constants that can be very large. In this paper we take another look at traditio
Superlinear convergence in minimum residual iterations
✍ Scribed by I. Kaporin
- Publisher
- John Wiley and Sons
- Year
- 2005
- Tongue
- English
- Weight
- 142 KB
- Volume
- 12
- Category
- Article
- ISSN
- 1070-5325
- DOI
- 10.1002/nla.436
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✦ Synopsis
Abstract
The superlinear convergence of minimum residual‐type methods for solving systems of linear equations with diagonalizable non‐singular unsymmetric matrix is estimated using a special conditioning measure. For the construction of the latter, the distance from the spectrum of the matrix to the origin and the Frobenius distance between the matrix and the identity is used. Asymptotical exactness of the presented result is discussed theoretically and illustrated numerically. Copyright © 2005 John Wiley & Sons, Ltd.
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