Sparse Interpolation and Rational Approximation

Abstract

Sparse interpolation or exponential analysis, is widely used and in quite different applications and areas of science and engineering. Therefore researchers are often not aware of similar studies going on in another field. The current text is written as a concise tutorial, from an approximation theorist point of view. In Section 2 we summarize the mathematics involved in exponential analysis: structured matrices, generalized eigenvalue problems, singular value decomposition. The section is written with the numerical computation of the sparse interpolant in mind. In Section 3 we outline several connections of sparse interpolation with other mostly non-numeric subjects: computer algebra, number theory, linear recurrences. Some problems are only solved using exact arithmetic. In Section 4 we connect sparse interpolation to rational approximation theory. One of the major hurdles in sparse interpolation is still the correct detection of the number of components in the model. Here we show how to reliably obtain the number of terms in a numeric and noisy environment. The new insight allows to improve on existing state-of-the-art algorithms.

Cite this paper

@misc{cuyt2016sparse,
  title = {{Sparse Interpolation and Rational Approximation}},
  author = {Cuyt, Annie and Lee, Wen-shin},
  journal = {Contemporary mathematics - American Mathematical Society},
  pages = {229--242},
  year = {2016},
  doi = {10.1090/conm/661/13284}
}

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