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Journal of Computational and Applied MathematicsVolume 386, April 2021, Article number 113265

From pseudospectra of diagonal blocks to pseudospectrum of a full matrix(Article)

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  • aComputational Statistics and Machine Learning, Istituto Italiano di Teconologia, Via Enrico Melen 83, Genova 16152, Italy
  • bDepartment of Mathematics and Informatics, Faculty of Science, University of Novi Sad, Trg D. Obradovića 4, Novi Sad, Serbia

Abstract

Since the concept of block matrices stems from a variety of practical applications (discretization of partial differential equations with the finite element method or finite difference methods, structured networks, intermediate transformations in numerical computations etc.) an important task is to analyse their pseudospectra. It is well known that for a fixed perturbation size, the union of pseudospectra of diagonal blocks underestimates the pseudospectrum of a full matrix. So, one is interested to provide an upper estimate by adequately changing the size of the perturbation for the pseudospectra of diagonal blocks. For the case of 2-by-2 block triangular matrix, this was optimally done in Trefethen and Embree (2005). Here we extend this result to general block matrices, and, in turn, obtain some estimates for the distance to instability of a block matrix. © 2020

Author keywords

Block matricesDistance to instabilityLocalizationsM-matricesPseudospectra

Indexed keywords

Engineering controlled terms:Finite difference methodNumerical methods
Engineering uncontrolled termsBlock matricesBlock triangular matrixDiscretizationsFull matrixesNumerical computationsPerturbation sizePseudospectrumStructured networks
Engineering main heading:Linear transformations

Funding details

Funding sponsor Funding number Acronym
Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja174019MPNTR
Ministarstvo Prosvete, Nauke i Tehnološkog RazvojaMPNTR
  • 1

    The work of all three authors has been partially supported by the Ministry of Education, Science and Technological Development of Serbia , Research Grant 174019 .

  • ISSN: 03770427
  • Source Type: Journal
  • Original language: English
  • DOI: 10.1016/j.cam.2020.113265
  • Document Type: Article
  • Publisher: Elsevier B.V.

  Kostić, V.R.; Computational Statistics and Machine Learning, Istituto Italiano di Teconologia, Via Enrico Melen 83, Genova 16152, Italy;
© Copyright 2020 Elsevier B.V., All rights reserved.

Cited by 1 document

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