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- [31] COMPUTING LOW-RANK APPROXIMATIONS OF LARGE-SCALE MATRICES WITH THE TENSOR NETWORK RANDOMIZED SVDSIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2018, 39 (03) : 1221 - 1244Batselier, KimYu, WenjianDaniel, LucaWong, Ngai
Approximating the weighted pseudospectra of large matrices
被引：1
作者：
Astudillo, R.
[1
]
Castillo, Z.
[1
]
机构：
[1] Cent Univ Venezuela, Escuela Comp, Ctr Calculo Cient & Tecnol, Caracas, Venezuela
关键词：
Spectrum;
Pseudospectrum;
Weighted pseudospectrum;
Arnoldi method;
Weighted Arnoldi factorization;
COMPUTATION;
D O I：
10.1016/j.mcm.2011.03.029
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ;
0835 ;
摘要：
In different applications it is necessary to compute the pseudospectra of matrices in an energy or weighted norm. In this work we develop a practical method to convert an Arnoldi factorization based on the Euclidean inner product into another Arnoldi factorization in a weighted inner product. We also propose how to use this approach to find a portion of the weighted pseudospectra of large matrices. Numerical results on different test problems are presented. (c) 2011 Elsevier Ltd. All rights reserved.
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页码：2169 / 2176
页数：8
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