Numerical Methods for General and Structured Eigenvalue Problems

Numerical Methods for General and Structured Eigenvalue Problems
Author: Daniel Kressner
Publisher: Springer Science & Business Media
Total Pages: 272
Release: 2006-01-20
Genre: Mathematics
ISBN: 3540285024


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This book is about computing eigenvalues, eigenvectors, and invariant subspaces of matrices. Treatment includes generalized and structured eigenvalue problems and all vital aspects of eigenvalue computations. A unique feature is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.


Numerical Methods for General and Structured Eigenvalue Problems
Language: en
Pages: 272
Authors: Daniel Kressner
Categories: Mathematics
Type: BOOK - Published: 2006-01-20 - Publisher: Springer Science & Business Media

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This book is about computing eigenvalues, eigenvectors, and invariant subspaces of matrices. Treatment includes generalized and structured eigenvalue problems a
Numerical Methods and Software for General and Structured Eigenvalue Problems
Language: en
Pages: 255
Authors: Daniel Kreßner
Categories:
Type: BOOK - Published: 2004 - Publisher:

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Numerical Methods for Eigenvalue Problems
Language: en
Pages: 216
Authors: Steffen Börm
Categories: Mathematics
Type: BOOK - Published: 2012-05-29 - Publisher: Walter de Gruyter

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Eigenvalues and eigenvectors of matrices and linear operators play an important role when solving problems from structural mechanics and electrodynamics, e.g.,
Numerical Methods and Software for General and Structured Eigenvalue Problems
Language: en
Pages:
Authors: Daniel Kressner (Mathématicien)
Categories:
Type: BOOK - Published: 2004 - Publisher:

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Numerical Methods for Large Eigenvalue Problems
Language: en
Pages: 292
Authors: Yousef Saad
Categories: Mathematics
Type: BOOK - Published: 2011-01-01 - Publisher: SIAM

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This revised edition discusses numerical methods for computing eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numeri