scispace - formally typeset
Journal ArticleDOI

A geometric theory for the QR, LU and power iterations.

TLDR
In this paper, it was shown that the first four iterations produce exactly the same sequence of subspaces as do direct and inverse iterations, starting from appropriate sub-spaces, and that Hessenberg matrices are associated with ideal starting spaces.
Abstract
We are concerned with the task of computing the invariant subspaces of a given matrix. For this purpose the $LU$, $QR$, treppen and bi-iterations have been presented, used, and studied more or less independently of the old-fashioned power method. Each of these methods generates implicitly a sequence of subspaces which determines the convergence properties of the method. The iterations differ in the way in which a basis is constructed to represent each subspace. This aspect largely determines the usefulness of the method.We show that the first four iterations produce exactly the same sequence of subspaces as do direct and inverse iteration started from appropriate subspaces. Their convergence properties are therefore the same and we present a complete geometric convergence theory in terms of the power method. Most previous studies have been algebraic in character. We show that Hessenberg matrices are associated with ideal starting spaces.The theory rests naturally in the setting of an n-dimensional space $...

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Citations
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Book

Optimization and Dynamical Systems

Uwe Helmke, +1 more
TL;DR: Details of Matrix Eigenvalue Methods, including Double Bracket Isospectral Flows, and Singular Value Decomposition are revealed.
Journal ArticleDOI

Computational methods of linear algebra

TL;DR: A survey of computational methods in linear algebra can be found in this article, where the authors discuss the means and methods of estimating the quality of numerical solution of computational problems, the generalized inverse of a matrix, the solution of systems with rectangular and poorly conditioned matrices, and more traditional questions such as algebraic eigenvalue problems and systems with a square matrix.
Book

Numerical Methods Of Statistics

TL;DR: This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis and treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods.
Journal ArticleDOI

Some perspectives on the eigenvalue problem

David S. Watkins
- 01 Sep 1993 - 
TL;DR: This expository paper explores the relationships among a number of algorithms for solving eigenvalue problems, including the power method, subspace iteration, the $QR$ algorithm, and the Arnoldi and symmetric Lanczos algorithms.
Journal ArticleDOI

Computing an Eigenvector with Inverse Iteration

Ilse C. F. Ipsen
- 01 Jun 1997 - 
TL;DR: It is concluded that the behavior of the residuals in inverse iteration is governed by the departure of the matrix from normality rather than by the conditioning of a Jordan basis or the defectiveness of eigenvalues.
References
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Book

The algebraic eigenvalue problem

TL;DR: Theoretical background Perturbation theory Error analysis Solution of linear algebraic equations Hermitian matrices Reduction of a general matrix to condensed form Eigenvalues of matrices of condensed forms The LR and QR algorithms Iterative methods Bibliography.
Journal ArticleDOI

On some algorithms for the solution of the complete eigenvalue problem

TL;DR: The basis and details of algorithms used in the solution of the complete problem of the eigenvalues of a matrix, already briefly explained in a note of the author's, are given and their extension to provide the Solution of the partial problem is considered.