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Lennart Aljoscha Hübner

Publications and source records attributed to Lennart Aljoscha Hübner.

2 recordsLinked to original sources

Computing Chebyshev polynomials using the complex Remez algorithm

We employ the generalized Remez algorithm, initially suggested by P. T. P. Tang, to perform an experimental study of Chebyshev polynomials in the complex plane. Our focus lies particularly on the examination of their norms and zeros. What sets our study apart is the breadth of examples considered, coupled with the fact that the degrees under investigation are substantially higher than those in previous studies where other methods have been applied. These computations of Chebyshev polynomials of high degrees reveal discernible, repeating patterns, which indicate a typical behavior of Chebyshev polynomials in a general setting. The use of Tang's algorithm allows for computations executed with precision, maintaining accuracy within quantifiable margins of error. Additionally, as a result of our experimental study, we propose what we believe to be a fundamental relationship between Chebyshev and Faber polynomials associated with a compact set.

math.CV↗

Parameter estimation for multivariate exponential sums via iterative rational approximation

We present two new methods for multivariate exponential analysis. In [7], we developed a new algorithm for reconstruction of univariate exponential sums by exploiting the rational structure of their Fourier coefficients and reconstructing this rational structure with the AAA (adaptive Antoulas-Anderson) method for rational approximation [15]. In this paper, we extend these ideas to the multivariate setting. Similarly as in univariate case, the Fourier coefficients of multivariate exponential sums have a rational structure and the multivariate exponential recovery problem can be reformulated as multivariate rational interpolation problem. We develop two approaches to solve this special multivariate rational interpolation problem by reducing it to the several univariate ones, which are then solved again via the univariate AAA method. Our first approach is based on using indices of the Fourier coefficients chosen from some sparse grid, which ensures efficient reconstruction using a respectively small amount of input data. The second approach is based on using the full grid of indices of the Fourier coefficients and relies on the idea of recursive dimension reduction. We demonstrate performance of our methods with several numerical examples.

math.NA↗