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arXiv 1302.0546math.FAcs.NAmath.NA
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谱集

Spectral Sets

Université Lille 1(里尔第一大学)

Catalin Badea, Bernhard Beckermann

AI总结:

本文综述了谱集与K-谱集的概念,这些概念由John von Neumann引入,用于通过函数的上确界范数估计矩阵函数的范数,并探讨了其在数值线性代数、泛函分析等领域的应用。

摘要:

这是一篇关于谱集的综述,将发表于《线性代数手册》第二版(L. Hogben 编)。由 John von Neumann 引入的谱集和 K-谱集提供了一种通过函数的 sup-范数来估计矩阵函数范数的可能性。这类谱集的例子包括矩阵的数值域或伪谱,这些内容在第16章和第18章中讨论。估计矩阵函数的范数是纯数学和应用数学众多领域中的基本任务,例如(数值)线性代数、泛函分析和数值分析。更具体的例子包括概率论、半群和算子值微分方程的存在性结果、演化方程时间离散化的数值格式研究,或 GMRES 的收敛速度(第41.7节)。谱集的概念涉及线性代数、算子理论、逼近论和复分析之间的许多深层联系。

英文摘要:

This is a survey about spectral sets, to appear in the second edition of Handbook of Linear Algebra (L. Hogben, ed.). Spectral sets and K-spectral sets, introduced by John von Neumann, 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, functional analysis, and numerical analysis. More specific examples include probability, semi-groups and existence results for operator-valued differential equations, the study of numerical schemes for the time discretization of evolution equations, 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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