On iterative QR pre-processing in the parallel block-Jacobi SVD algorithm
Parallel Computing
Saving flops in LU based shift-and-invert strategy
Journal of Computational and Applied Mathematics
New progress in real and complex polynomial root-finding
Computers & Mathematics with Applications
An Implicit Multishift $QR$-Algorithm for Hermitian Plus Low Rank Matrices
SIAM Journal on Scientific Computing
A Novel Parallel QR Algorithm for Hybrid Distributed Memory HPC Systems
SIAM Journal on Scientific Computing
Chasing Bulges or Rotations? A Metamorphosis of the QR-Algorithm
SIAM Journal on Matrix Analysis and Applications
On aggressive early deflation in parallel variants of the QR algorithm
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume Part I
Journal of Computational and Applied Mathematics
Stable Computation of the CS Decomposition: Simultaneous Bidiagonalization
SIAM Journal on Matrix Analysis and Applications
Perturbations of invariant subspaces of unreduced Hessenberg matrices
Computers & Mathematics with Applications
Computing eigenvalues of normal matrices via complex symmetric matrices
Journal of Computational and Applied Mathematics
Optimally packed chains of bulges in multishift QR algorithms
ACM Transactions on Mathematical Software (TOMS)
Stability of rootfinding for barycentric Lagrange interpolants
Numerical Algorithms
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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 generic GR algorithm, including special cases (for example, QR, SR, HR), and the development of Krylov subspace methods. Also addressed are a generic Krylov process and the Arnoldi and various Lanczos algorithms, which are obtained as special cases. The chapter on product eigenvalue problems provides further unification, showing that the generalized eigenvalue problem, the singular value decomposition problem, and other product eigenvalue problems can all be viewed as standard eigenvalue problems. The author provides theoretical and computational exercises in which the student is guided, step by step, to the results. Some of the exercises refer to a collection of MATLAB programs compiled by the author that are available on a Web site that supplements the book. Audience: Readers of this book are expected to be familiar with the basic ideas of linear algebra and to have had some experience with matrix computations. This book is intended for graduate students in numerical linear algebra. It will also be useful as a reference for researchers in the area and for users of eigenvalue codes who seek a better understanding of the methods they are using. Contents: Preface; Chapter 1: Preliminary Material; Chapter 2: Basic Theory of Eigensystems; Chapter 3: Elimination; Chapter 4: Iteration; Chapter 5: Convergence; Chapter 6: The Generalized Eigenvalue Problem; Chapter 7: Inside the Bulge; Chapter 8: Product Eigenvalue Problems; Chapter 9: Krylov Subspace Methods; Bibliography; Index.