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Multivariate exponential analysis from the minimal number of samples

Journal Contribution - Journal Article

The problem of multivariate exponential analysis or sparse interpolation has received a lot of attention, especially with respect to the number of samples required to solve it unambiguously. In this paper we show how to bring the number of samples down to the absolute minimum of (d + 1)n where d is the dimension of the problem and n is the number of exponential terms. To this end we present a fundamentally different approach for the multivariate problem statement. We combine a one-dimensional exponential analysis method such as ESPRIT, MUSIC, the matrix pencil or any Prony-like method, with some linear systems of equations because the multivariate exponents are inner products and thus linear expressions in the parameters.
Journal: Advances in computational mathematics
ISSN: 1019-7168
Volume: 44
Pages: 987 - 1002
Publication year:2018
Keywords:A1 Journal article
BOF-keylabel:yes
BOF-publication weight:1
CSS-citation score:2
Authors from:Higher Education
Accessibility:Open