The Algebraic Eigenvalue Problem
About: This article is published in Mathematics of Computation.The article was published on 1966-10-01. It has received 2408 citations till now. The article focuses on the topics: Algebraic number & Eigenvalues and eigenvectors.
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TL;DR: In this article, the Orr-Sommerfeld equation is solved numerically using expansions in Chebyshev polynomials and the QR matrix eigenvalue algorithm.
Abstract: The Orr-Sommerfeld equation is solved numerically using expansions in Chebyshev polynomials and the QR matrix eigenvalue algorithm. It is shown that results of great accuracy are obtained very economically. The method is applied to the stability of plane Poiseuille flow; it is found that the critical Reynolds number is 5772·22. It is explained why expansions in Chebyshev polynomials are better suited to the solution of hydrodynamic stability problems than expansions in other, seemingly more relevant, sets of orthogonal functions.
1,365 citations
01 May 1998
TL;DR: It is proved that under certain conditions LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance.
Abstract: Latent semantic indexing LSI is an information retrieval technique based on the spectral analysis of the term document matrix whose empirical success had heretofore been without rigorous prediction and explanation We prove that under certain conditions LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance We also propose the technique of random projection as a way of speeding up LSI We complement our theorems with encouraging experimental results We also argue that our results may be viewed in a more general framework as a theoretical basis for the use of spectral methods in a wider class of applications such as collaborative ltering
1,235 citations
TL;DR: This paper presents an unusual numbering of the mesh (unknowns) and shows that if the authors avoid operating on zeros, the $LDL^T $ factorization of A can be computed using the same standard algorithm in $O(n^3 )$ arithmetic operations.
Abstract: Let M be a mesh consisting of $n^2 $ squares called elements, formed by subdividing the unit square $(0,1) \times (0,1)$ into $n^2 $ small squares of side ${1 / h}$, and having a node at each of the $(n + 1)^2 $ grid points. With M we associate the $N \times N$ symmetric positive definite system $Ax = b$, where $N = (n + 1)^2 $, each $x_i $ is associated with a node of M, and $A_{ij}
e 0$ if and only if $x_i $ and $x_j $ are associated with nodes of the same element. If we solve the equations via the standard symmetric factorization of A, then $O(n^4 )$ arithmetic operations are required if the usual row by row (banded) numbering scheme is used, and the storage required is $O(n^3 )$. In this paper we present an unusual numbering of the mesh (unknowns) and show that if we avoid operating on zeros, the $LDL^T $ factorization of A can be computed using the same standard algorithm in $O(n^3 )$ arithmetic operations. Furthermore, the storage required is only $O(n^2 \log _2 n)$. Finally, we prove that all ord...
1,043 citations
TL;DR: A new method, called the QZ algorithm, is presented for the solution of the matrix eigenvalue problem $Ax = \lambda Bx$ with general square matrices A and B with particular attention to the degeneracies which result when B is singular.
Abstract: A new method, called the $QZ$ algorithm, is presented for the solution of the matrix eigenvalue problem $Ax = \lambda Bx$ with general square matrices A and B. Particular attention is paid to the degeneracies which result when B is singular. No inversions of B or its submatrices are used. The algorithm is a generalization of the $QR$ algorithm, and reduces to it when $B = I$. Problems involving higher powers of $\lambda $ are also mentioned.
1,038 citations
TL;DR: A novel BCG-like approach, the quasi-minimal residual (QMR) method, which overcomes the problems of BCG is presented and how BCG iterates can be recovered stably from the QMR process is shown.
Abstract: The biconjugate gradient (BCG) method is the "natural" generalization of the classical conjugate gradient algorithm for Hermitian positive definite matrices to general non-Hermitian linear systems. Unfortunately, the original BCG algorithm is susceptible to possible breakdowns and numerical instabilities. In this paper, we present a novel BCG-like approach, the quasi-minimal residual (QMR) method, which overcomes the problems of BCG. An implementation of QMR based on a look-ahead version of the nonsymmetric Lanczos algorithm is proposed. It is shown how BCG iterates can be recovered stably from the QMR process. Some further properties of the QMR approach are given and an error bound is presented. Finally, numerical experiments are reported.
985 citations