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Fast Parallelizable Methods for Computing Invariant Subspaces of Hermitian Matrices
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@Article{JCM-25-583,
author = {},
title = {Fast Parallelizable Methods for Computing Invariant Subspaces of Hermitian Matrices},
journal = {Journal of Computational Mathematics},
year = {2007},
volume = {25},
number = {5},
pages = {583--594},
abstract = { We propose a {\it quadratically} convergent algorithm for computing the invariant subspaces of an Hermitian matrix. Each iteration of the algorithm consists of {\it one} matrix-matrix multiplication and {\it one} QR decomposition. We present an accurate convergence analysis of the algorithm without using the big $O$ notation. We also propose a general framework based on implicit rational transformations which allows us to make connections with several existing algorithms and to derive classes of extensions to our basic algorithm with faster convergence rates. Several numerical examples are given which compare some aspects of the existing algorithms and the new algorithms.},
issn = {1991-7139},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jcm/8715.html}
}
TY - JOUR
T1 - Fast Parallelizable Methods for Computing Invariant Subspaces of Hermitian Matrices
JO - Journal of Computational Mathematics
VL - 5
SP - 583
EP - 594
PY - 2007
DA - 2007/10
SN - 25
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jcm/8715.html
KW - Eigenvalue
KW - Invariant subspace
KW - Hermitian matrix
KW - QR method
KW - Parallelizable method
AB - We propose a {\it quadratically} convergent algorithm for computing the invariant subspaces of an Hermitian matrix. Each iteration of the algorithm consists of {\it one} matrix-matrix multiplication and {\it one} QR decomposition. We present an accurate convergence analysis of the algorithm without using the big $O$ notation. We also propose a general framework based on implicit rational transformations which allows us to make connections with several existing algorithms and to derive classes of extensions to our basic algorithm with faster convergence rates. Several numerical examples are given which compare some aspects of the existing algorithms and the new algorithms.
Zhenyue Zhang, Hongyuan Zha & Wenlong Ying. (1970). Fast Parallelizable Methods for Computing Invariant Subspaces of Hermitian Matrices.
Journal of Computational Mathematics. 25 (5).
583-594.
doi:
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