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 their paper 'On the Georgiou-Lindquist approach to constrained Kullback-Leibler approximation of spectral densities.' Our main result states that the algorithm globally converges to one of its fixed points.
Further Results on the Convergence of the Pavon-Ferrante Algorithm for Spectral Estimation
Baggio G.
2018
Abstract
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 their paper 'On the Georgiou-Lindquist approach to constrained Kullback-Leibler approximation of spectral densities.' Our main result states that the algorithm globally converges to one of its fixed points.File in questo prodotto:
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