Spectral Sets
Document type :
Pré-publication ou Document de travail
Title :
Spectral Sets
Author(s) :
Badea, Catalin [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Université de Lille
Beckermann, Bernhard [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Université de Lille
Beckermann, Bernhard [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
HAL domain(s) :
Mathématiques [math]/Analyse fonctionnelle [math.FA]
English abstract : [en]
This is a chapter of the forthcoming Handbook of Linear Algebra, 2nd Edition (ed. L. Hogben). Spectral sets and K-spectral sets, introduced by John von Neumann in [vNe51], offer a possibility to estimate the norm of functions ...
Show more >This is a chapter of the forthcoming Handbook of Linear Algebra, 2nd Edition (ed. L. Hogben). Spectral sets and K-spectral sets, introduced by John von Neumann in [vNe51], offer a possibility to estimate the norm of functions of matrices in terms of the sup-norm of the function. Examples of such spectral sets include the numerical range or the pseudospectrum of a matrix, discussed in Chapters 16 and 18. Estimating the norm of functions of matrices is an essential task in numerous fields of pure and applied mathematics, such as (numerical) linear algebra [Gre97, Hig08], functional analysis [Pau02], and numerical analysis. More specific examples include probability [DD99], semi-groups and existence results for operator-valued differential equations, the study of numerical schemes for the time discretization of evolution equations [Cro08], or the convergence rate of GMRES (Section 41.7). The notion of spectral sets involves many deep connections between linear algebra, operator theory, approximation theory, and complex analysis.Show less >
Show more >This is a chapter of the forthcoming Handbook of Linear Algebra, 2nd Edition (ed. L. Hogben). Spectral sets and K-spectral sets, introduced by John von Neumann in [vNe51], offer a possibility to estimate the norm of functions of matrices in terms of the sup-norm of the function. Examples of such spectral sets include the numerical range or the pseudospectrum of a matrix, discussed in Chapters 16 and 18. Estimating the norm of functions of matrices is an essential task in numerous fields of pure and applied mathematics, such as (numerical) linear algebra [Gre97, Hig08], functional analysis [Pau02], and numerical analysis. More specific examples include probability [DD99], semi-groups and existence results for operator-valued differential equations, the study of numerical schemes for the time discretization of evolution equations [Cro08], or the convergence rate of GMRES (Section 41.7). The notion of spectral sets involves many deep connections between linear algebra, operator theory, approximation theory, and complex analysis.Show less >
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