| Review: |
This book has its origins in a course taught by the author on eigensystems computation at the Technical University of Chemnitz Germany in 1999. The book provides a unified development of the two most important classes of algorithms for solving matrix eigenvalue problems. These are the QR-like ones for dense problems and Krylov subspace methods for sparse problems. The author states he is not aiming for completeness but follows his own interest and to not make the book too lengthy. Chapters 1 and 2 contain background material, although the author spent considerable time on the latter covering basic theoretical material and eigensystems and believe most will find this of interest. Chapter 3 covers tools for zeros in matrices, chapter 4 covers GR algorithms, chapter 5 convergence, chapter 6 is on GZ algorithms, chapter 7 inside the bulge, chapter 8 is on product eigenvalue problems and chapter 9 introduces Krylov subspace. A substantial effort has gone into the exercises, which contain many proofs of theorems. |