Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions

In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tucker format. We develop novel randomized techniques to accomplish the tensor compression, provide a detailed analysis of t...

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Vydáno v:	Advances in computational mathematics Ročník 48; číslo 5
Hlavní autoři:	Saibaba, Arvind K., Minster, Rachel, Kilmer, Misha E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.10.2022 Springer Nature B.V
Témata:	Algorithms Approximation Chebyshev approximation Computational mathematics Computational Mathematics and Numerical Analysis Computational Science and Engineering Cost analysis Gaussian process Kernels Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Multivariate analysis Polynomials Tensors Visualization Multivariate function approximation Kernel methods 65F55 41A63 42A10 41A10 Low-rank approximations Randomized algorithms Tucker format
ISSN:	1019-7168, 1572-9044
On-line přístup:	Získat plný text
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