AAA interpolation of equispaced data

07/24/2022
by   Daan Huybrechs, et al.
0

We propose AAA rational approximation as a method for interpolating or approximating smooth functions from equispaced data samples. Although it is always better to approximate from large numbers of samples if they are available, whether equispaced or not, this method often performs impressively even when the sampling grid is fairly coarse. In most cases it gives more accurate approximations than other methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/26/2019

Bounding Zolotarev numbers using Faber rational functions

By closely following a construction by Ganelius, we construct Faber rati...
research
09/20/2022

Polynomial approximation of derivatives by the constrained mock-Chebyshev least squares operator

The constrained mock-Chebyshev least squares operator is a linear approx...
research
09/22/2021

Numerical Continued Fraction Interpolation

We show that highly accurate approximations can often be obtained from c...
research
01/14/2023

Nonlinear approximation of functions based on non-negative least squares solver

In computational practice, most attention is paid to rational approximat...
research
09/23/2021

Piecewise Padé-Chebyshev Reconstruction of Bivariate Piecewise Smooth Functions

We extend the idea of approximating piecewise smooth univariate function...
research
11/04/2022

Embracing Off-the-Grid Samples

Many empirical studies suggest that samples of continuous-time signals t...
research
01/12/2023

Practical challenges in data-driven interpolation: dealing with noise, enforcing stability, and computing realizations

In this contribution, we propose a detailed study of interpolation-based...

Please sign up or login with your details

Forgot password? Click here to reset