文章基本信息

标题：On the Convergence of a Matricial Fixed-Point Iteration Connected with Spectral Estimation
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作者：Giacomo Baggio
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：7415-7420
DOI：10.1016/j.ifacol.2017.08.1499
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, an in-depth analysis of a nonlinear matricial fixed-point iteration and its convergence properties is provided. Remarkably, the analyzed iteration can be regarded as a “frequency-sampled” version of a thoroughly investigated and very efficient fixed-point iteration for Kullback–Leibler approximation of spectral densities, proposed in Pavon and Ferrante (2006). A proof of global convergence to a prescribed set of fixed points of the latter iteration seems to be elusive, though conjectured and corroborated by an extensive campaign of numerical simulations (Ferrante et al., 2011). The present work represents a first step towards the solution of this conjecture.
关键词：KeywordsStability of nonlinear systemsLyapunov methodsApplication of nonlinear analysisdesignConvergence analysisFixed-point iterationSpectral density estimation