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Fractional differential equations (FDEs) describe subdiffusion behavior of dynamical systems. Their nonlocal structure requires taking into account the whole evolution history during the time integration, which then possibly causes additional memory use to store the history, growing in time. An alternative to a quadrature for the history integral is to approximate the fractional kernel with a sum of exponentials, which is equivalent to considering the FDE solution as a sum of solutions to a system of ordinary differential equations. One possibility to construct this system is to approximate the Laplace spectrum of the fractional kernel with a rational function. In this paper we use the adaptive Antoulas–Anderson algorithm for the rational approximation of the kernel spectrum, which yields only a small number of real-valued poles. We propose a numerical scheme based on this idea and study its stability and convergence properties. In addition, we apply the algorithm to a time-fractional Cahn–Hilliard problem. [ABSTRACT FROM AUTHOR]

We introduce a new method for Estimation of Signal Parameters based on Iterative Rational Approximation (ESPIRA) for sparse exponential sums. Our algorithm uses the AAA algorithm for rational approximation of the discrete Fourier transform of the given equidistant signal values. We show that ESPIRA can be interpreted as a matrix pencil method (MPM) applied to Loewner matrices. These Loewner matrices are closely connected with the Hankel matrices that are usually employed for signal recovery. Due to the construction of the Loewner matrices via an adaptive selection of index sets, the MPM is stabilized. ESPIRA achieves similar recovery results for exact data as ESPRIT and the MPM, but with less computational effort. Moreover, ESPIRA strongly outperforms ESPRIT and the MPM for noisy data and for signal approximation by short exponential sums. [ABSTRACT FROM AUTHOR]

In this paper we introduce two algorithms for stable approximation with and recovery of short cosine sums. The used signal model contains cosine terms with arbitrary real positive frequency parameters and therefore strongly generalizes usual Fourier sums. The proposed methods both employ a set of equidistant signal values as input data. The ESPRIT method for cosine sums is a Prony-like method and applies matrix pencils of Toeplitz + Hankel matrices while the ESPIRA method is based on rational approximation of DCT data and can be understood as a matrix pencil method for special Loewner matrices. Compared to known numerical methods for recovery of exponential sums, the design of the considered new algorithms directly exploits the special real structure of the signal model and therefore usually provides real parameter estimates for noisy input data, while the known general recovery algorithms for complex exponential sums tend to yield complex parameters in this case. [ABSTRACT FROM AUTHOR]

In this paper, we derive a new recovery procedure for the reconstruction of extended exponential sums of the form y (t) = ∑ j = 1 M (∑ m = 0 n j γ j , m t m) e 2 π λ j t , where the frequency parameters λ j ∈ ℂ are pairwise distinct. In order to reconstruct y(t) we employ a finite set of classical Fourier coefficients of y with regard to a finite interval (0 , P) ⊂ ℝ with P > 0. For our method, 2N + 2 Fourier coefficients c k (y) are sufficient to recover all parameters of y, where N : = ∑ j = 1 M (1 + n j) denotes the order of y(t). The recovery is based on the observation that for λ j ∉ i P ℤ the terms of y(t) possess Fourier coefficients with rational structure. We employ a recently proposed stable iterative rational approximation algorithm in [Y. Nakatsukasa, O. Sète and L. N. Trefethen, The AAA Algorithm for rational approximation, SIAM J. Sci. Comput.40(3) (2018) A1494A1522]. If a sufficiently large set of L Fourier coefficients of y is available (i.e. L ≥ 2 N + 2), then our recovery method automatically detects the number M of terms of y, the multiplicities n j for j = 1 , ... , M , as well as all parameters λ j , j = 1 , ... , M , and γ j , m , j = 1 , ... , M , m = 0 , ... , n j , determining y(t). Therefore, our method provides a new stable alternative to the known numerical approaches for the recovery of exponential sums that are based on Prony's method. [ABSTRACT FROM AUTHOR]

We present an extension of the AAA (adaptive Antoulas--Anderson) algorithm for periodic functions, called "AAAtrig."" The algorithm uses the key steps of AAA approximation by (i) representing the approximant in (trigonometric) barycentric form and (ii) selecting the support points greedily. Accordingly, AAAtrig inherits all the favorable characteristics of AAA and is thus extremely flexible and robust, being able to consider quite general sets of sample points in the complex plane. We consider a range of applications with particular emphasis on solving Laplace's equation in periodic domains and compressing periodic conformal maps. These results reproduce the tapered exponential clustering effect observed in other recent studies. The algorithm is implemented in Chebfun. [ABSTRACT FROM AUTHOR]