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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