Estimating the largest eigenvalue of a positive definite matrix

Author:

O’Leary Dianne P.,Stewart G. W.,Vandergraft James S.

Abstract

The power method for computing the dominant eigenvector of a positive definite matrix will converge slowly when the dominant eigenvalue is poorly separated from the next largest eigenvalue. In this note it is shown that in spite of this slow convergence, the Rayleigh quotient will often give a good approximation to the dominant eigenvalue after a very few iterations-even when the order of the matrix is large.

Publisher

American Mathematical Society (AMS)

Subject

Applied Mathematics,Computational Mathematics,Algebra and Number Theory

Reference4 articles.

1. The Lanczos algorithm with selective orthogonalization;Parlett, B. N.;Math. Comp.,1979

2. Handbook Series Linear Algebra: Simultaneous iteration method for symmetric matrices;Rutishauser, H.;Numer. Math.,1970

3. Accelerating the orthogonal iteration for the eigenvectors of a Hermitian matrix;Stewart, G. W.;Numer. Math.,1969

4. Computer Science and Applied Mathematics;Stewart, G. W.,1973

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