The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods

✍ Scribed by David S. Watkins

Publisher: Society for Industrial Mathematics
Year: 2007
Tongue: English
Leaves: 451
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

After working on the reduction of IC interconnect networks for months, I am eager to find a book about matrix eigenvalue computations. Although there are several famous bibles in this field, like "The Algebraic Eigenvalue Problem" by James Wilkinson, they are not so up-to-date that some important topics like product eigenvalue problems are not covered. Also, matrices with special structures require dedicated algortihms to compute their eigenvalues efficiently. This book might be the best book which covers all the latest eigenvalue problems in a very traceable way.

The other major advantage of this book is its well-designed exercises. Many theorems are proved in the exercises with step-by-step guidance. Actually, the exercises demonsrtate how the autohr proves/solves a theorem/problem. I indeed gain a lot of insight from the exercises! I recommend this book to anyone who wants to implement matrix eigenvalue computation algorithms by yourslef.

📜 SIMILAR VOLUMES

The Matrix Eigenvalue Problem: GR and Kr

📁 The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods

✍ David S. Watkins 📂 Library 📅 2008 🏛 Society for Industrial Mathematics 🌐 English

This book presents the first in-depth, complete, and unified theoretical discussion of the two most important classes of algorithms for solving matrix eigenvalue problems: QR-like algorithms for dense problems and Krylov subspace methods for sparse problems. The author discusses the theory of the ge

The Matrix Eigenvalue Problem: GR and Kr

📁 The Matrix Eigenvalue Problem: GR and Krylov Subspace Methods

✍ David S. Watkins 📂 Library 📅 2007 🏛 Society for Industrial Mathematics 🌐 English

The first in-depth, complete, and unified theoretical discussion of the two most important classes of algorithms for solving matrix eigenvalue problems: QR-like algorithms for dense problems and Krylov subspace methods for sparse problems. The author discusses the theory of the generic GR algorithm,

The Matrix Eigenvalue Problem. GR and Kr

📁 The Matrix Eigenvalue Problem. GR and Krylov Subspace Methods

✍ David S. Watkins 📂 Library 📅 2007 🏛 Society for Industrial and Applied Mathematics 🌐 English

The matrix eigenvalue problem: GR and Kr

📁 The matrix eigenvalue problem: GR and Krylov subspace methods

✍ David S. Watkins 📂 Library 📅 2007 🏛 Society for Industrial and Applied Mathematics 🌐 English

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📁 Matrix Computations and Semiseparable Matrices - Eigenvalue and Singular Value Methods

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<P>The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and Semiseparable Matrices. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value computations

Matrix Computations and Semiseparable Ma

📁 Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods

✍ Raf Vandebril, Marc Van Barel, Nicola Mastronardi 📂 Library 📅 2008 🏛 Johns Hopkins University Press 🌐 English

<P>The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of <I>Matrix Computations and Semiseparable Matrices</I>. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value comp