Computing high precision Matrix Padé approximants

Authors:
Bernhard Beckermann;Daniel Bessis;Luca Perotti;Daniel Vrinceanu
Affiliations:
Laboratoire Paul Painlevé UMR CNRS 8524 (ANO-EDP), UFR Mathématiques --- M3, UST Lille, Villeneuve d'Ascq Cedex, France 59655;Department of Physics, Texas Southern University, Houston, USA;Department of Physics, Texas Southern University, Houston, USA;Department of Physics, Texas Southern University, Houston, USA
Venue:
Numerical Algorithms
Year:
2012

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Abstract

We describe a new method of computing matrix Padé approximants of series with integer data in an efficient and fraction-free way, by controlling the growth of the size of intermediate coefficients. This algorithm is applied to compute high precision Padé approximants of matrix-valued generating functions of time series. As an illustration we show that we can successfully recover from noisy equidistant sampling data a joint damped signal of four antenna, even in the presence of background signals.