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Low-rank approximation of analytic kernels

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If you have a question about this talk, please contact Georg Maierhofer .

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. In this talk I will discuss a new 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. A preprint can be found at https://arxiv.org/abs/2509.14017.

This talk is part of the Applied and Computational Analysis series.

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