Eigenvalues of Matrices

Häftad, Engelska, 2012

Del i serien Classics in Applied Mathematics

1 284 kr

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Beskrivning

This classic textbook provides a modern and complete guide to the calculation of eigenvalues of matrices, written at an accessible level that presents in matrix notation the fundamental aspects of the spectral theory of linear operators in finite dimension. Unique features of this book are:The convergence of eigensolvers serving as the basis of the notion of the gap between invariant subspaces.Its coverage of the impact of the high nonnormality of the matrix on its eigenvalues.The comprehensive nature of the material that moves beyond mathematical technicalities to the essential mean carried out by matrix eigenvalues.

Produktinformation

Utgivningsdatum:2012-11-30
Mått:154 x 226 x 22 mm
Vikt:598 g
Format:Häftad
Språk:Engelska
Serie:Classics in Applied Mathematics
Antal sidor:440
Upplaga:2
Förlag:Society for Industrial & Applied Mathematics,U.S.
ISBN:9781611972450

Utforska kategorier

Beräkning och matematisk analys inom Naturvetenskap och teknik

Mer om författaren

Françoise Chatelin is Professor of Mathematics at the University of Toulouse and head of the Qualitative Computing Group at CERFACS. Before moving to CERFACS, she was a professor at the universities of Grenoble and Paris IX Dauphine. She also worked for a decade in the industrial research laboratories of IBM France and Thales, where she was in charge of intensive computing activities. Her areas of expertise include spectral theory for linear operators in Banach spaces and finite precision computation of very large eigenproblems. She currently explores the uncharted domain of mathematical computation that lies beyond real or complex analysis.

Innehållsförteckning

Chapter 1: Supplements from Linear AlgebraChapter 2: Elements of Spectral TheoryChapter 3: Why Compute Eigenvalues?Chapter 4: Error AnalysisChapter 5: Foundations of Methods for Computing EigenvaluesChapter 6: Numerical Methods for Large MatricesChapter 7: Chebyshev’s Iterative MethodsChapter 8: Polymorphic Information Processing with MatricesAppendix A: Solution to ExercisesAppendix B: References for ExercisesAppendix C: References