A novel procedure is given here for constructing non-negative functions with zero-valued global minima coinciding with eigenvectors of a general real matrix A. Some of these functions are distinct because all their local minima are also global, offering a new way of determining eigenpairs by local optimization. Apart from describing the framework of the method, the error bounds given separately for the approximation of eigenvectors and eigenvalues provide a deeper insight into the fundamentally different nature of their approximations.
@article{bwmeta1.element.bwnjournal-article-amcv12i4p533bwm,
author = {Kocsor, Andr\'as and Dombi, J\'ozsef and B\'alint, Imre},
title = {Inequality-based approximation of matrix eigenvectors},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {12},
year = {2002},
pages = {533-538},
zbl = {1037.93041},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv12i4p533bwm}
}
Kocsor, András; Dombi, József; Bálint, Imre. Inequality-based approximation of matrix eigenvectors. International Journal of Applied Mathematics and Computer Science, Tome 12 (2002) pp. 533-538. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv12i4p533bwm/
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