Low-rank approximation of analytic kernels

Fuente: arXiv
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Main Author: Webb, Marcus
Format: Preprint
Published: 2025
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author Webb, Marcus
author_facet Webb, Marcus
contents Many algorithms in scientific computing and data science take advantage of low-rank approximation of matrices and kernels, and understanding why nearly-low-rank structure occurs is essential for their analysis and further development. This paper provides a framework for bounding the best low-rank approximation error of matrices arising from samples of a kernel that is analytically continuable in one of its variables to an open region of the complex plane. Elegantly, the low-rank approximations used in the proof are computable by rational interpolation using the roots and poles of Zolotarev rational functions, leading to a fast algorithm for their construction.
format Preprint
id arxiv_https___arxiv_org_abs_2509_14017
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Low-rank approximation of analytic kernels
Webb, Marcus
Numerical Analysis
Many algorithms in scientific computing and data science take advantage of low-rank approximation of matrices and kernels, and understanding why nearly-low-rank structure occurs is essential for their analysis and further development. This paper provides a framework for bounding the best low-rank approximation error of matrices arising from samples of a kernel that is analytically continuable in one of its variables to an open region of the complex plane. Elegantly, the low-rank approximations used in the proof are computable by rational interpolation using the roots and poles of Zolotarev rational functions, leading to a fast algorithm for their construction.
title Low-rank approximation of analytic kernels
topic Numerical Analysis
url https://arxiv.org/abs/2509.14017