Prony like methods for noisy data
(09/2015 - 02/2017)
Individual research grant of Chinese Scholarship Council
We investigate the stability of Prony's method in case of noisy input data. For this purpose we consider a maximum likelihood modification of the Prony method and compare it to other approaches for stabilization of Prony's method. We further aim at generalizing the approach to sparse expansions of eigenfunctions of linear operators.