Numerical Methods for General and Structured Eigenvalue Problems / by Daniel Kressner.

Por:

Kressner, Daniel [autor]

Colaborador(es):

SpringerLink (Servicio en línea)

Tipo de material: Texto

TextoSeries Lecture Notes in Computational Science and Engineering ; 46Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Descripción: xiv, 258 páginas 32 ilustraciones recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9783540285021

Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:

QA71-90

Recursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificaciÃ³n en Biblioteca Digital UANL)

Contenidos:

The QR Algorithm -- The QZ Algorithm -- The Krylov-Schur Algorithm -- Structured Eigenvalue Problems -- Background in Control Theory Structured Eigenvalue Problems -- Software.

Resumen: This book is about computing eigenvalues, eigenvectors and invariant subspaces of matrices. The treatment includes generalized and structured eigenvalue problems, such as Hamiltonian or product eigenvalue problems. All vital aspects of eigenvalue computations are covered: theory, perturbation analysis, algorithms, high performance methodologies and software. The reader will learn about recently developed techniques which substantially improve the performance of some of the most widely numerical methods, the QR and the QZ algorithm as well as Krylov subspace methods. A unique feature of this book 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.

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Springer eBooks

The QR Algorithm -- The QZ Algorithm -- The Krylov-Schur Algorithm -- Structured Eigenvalue Problems -- Background in Control Theory Structured Eigenvalue Problems -- Software.

This book is about computing eigenvalues, eigenvectors and invariant subspaces of matrices. The treatment includes generalized and structured eigenvalue problems, such as Hamiltonian or product eigenvalue problems. All vital aspects of eigenvalue computations are covered: theory, perturbation analysis, algorithms, high performance methodologies and software. The reader will learn about recently developed techniques which substantially improve the performance of some of the most widely numerical methods, the QR and the QZ algorithm as well as Krylov subspace methods. A unique feature of this book 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.

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