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The Algebraic Eigenvalue Problem (Numerical Mathematics and Scientific Computation), by J. H. Wilkinson
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This volume, which became a classic on first publication, is perhaps the most important and widely read book in the field of numerical analysis. It presents a distillation of the author's pioneering discoveries concerning the computation of matrix eigenvalues. The emphasis is on the transmission of knowledge rather than elaborate proofs. The book will be valued by all practicing numerical analysts, students and researchers in the field, engineers, and scientists.
- Sales Rank: #2145722 in Books
- Published on: 1988-04-21
- Original language: English
- Number of items: 1
- Dimensions: 6.19" h x 1.35" w x 9.19" l, 2.09 pounds
- Binding: Paperback
- 680 pages
Review
" . . . a classic . . . Questions of sensitivity, and the economy and stability of many numerical methods are treated in rigorous detail, but always in a manner accessible to engineers and scientists." --Quarterly of Applied Mathematics
Most helpful customer reviews
12 of 13 people found the following review helpful.
Does Not Get Any Better Than This...
By Good_Authors_Are_Retired
This book is really something else.
I found it in my boss' collection, and my appreciation to his taste of books doubled instantly as I started going over the pages. I was aware of the book through Golub and Van Loan's endless citations to it (as AEP) whenever they reach a hard to prove theorem on eigenvalues.
Wilkinson presents hardcore material with clarity, I really appreciate the authorship. For those who share my background (electrical engineering), the authorship is similar to Gallager's "Information Theory and Reliable Communication", or Wozencraft's "Principles of Communication Engineering".
Wilkinson is surprising in this book however. Immediately, after hardcore numerical stability bound derivations, he starts giving practical examples, does not appear to talk down to the reader.
This book is a treasure. Wilkinson is my hero!
Very likely, the book by Parlett "Symmetric Eigenvalue Problem" will be a good companion. When someone borrows your AEP, you can continue learning from SEP.
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