In this paper, we provide a detailed analysis of the global convergence properties of an extensively studied and extremely effective fixed-point algorithm for the Kullback-Leibler approximation of spectral densities, proposed by Pavon and Ferrante in [Pavon and Ferrante, 2006]. Our main result states that the algorithm globally converges to one of its fixed points.
@article{arxiv.1612.03570,
title = {Further Results on the Convergence of the Pavon-Ferrante Algorithm for Spectral Estimation},
author = {Giacomo Baggio},
journal= {arXiv preprint arXiv:1612.03570},
year = {2018}
}
Comments
16 pages, no figures. Corrected a few typos and changed title. To appear in IEEE Transactions on Automatic Control