Randomized least-squares with minimal oversampling and interpolation in general spaces

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Hauptverfasser: Chkifa, Abdellah, Dolbeault, Matthieu
Format: Preprint
Veröffentlicht: 2023
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author Chkifa, Abdellah
Dolbeault, Matthieu
author_facet Chkifa, Abdellah
Dolbeault, Matthieu
contents In approximation of functions based on point values, least-squares methods provide more stability than interpolation, at the expense of increasing the sampling budget. We show that near-optimal approximation error can nevertheless be achieved, in an expected $L^2$ sense, as soon as the sample size $m$ is larger than the dimension $n$ of the approximation space by a constant ratio. On the other hand, for $m=n$, we obtain an interpolation strategy with a stability factor of order $n$. The proposed sampling algorithms are greedy procedures based on arXiv:0808.0163 and arXiv:1508.03261, with polynomial computational complexity.
format Preprint
id arxiv_https___arxiv_org_abs_2306_07435
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Randomized least-squares with minimal oversampling and interpolation in general spaces
Chkifa, Abdellah
Dolbeault, Matthieu
Numerical Analysis
65D15, 41A65, 65D05, 41A81, 41A05
In approximation of functions based on point values, least-squares methods provide more stability than interpolation, at the expense of increasing the sampling budget. We show that near-optimal approximation error can nevertheless be achieved, in an expected $L^2$ sense, as soon as the sample size $m$ is larger than the dimension $n$ of the approximation space by a constant ratio. On the other hand, for $m=n$, we obtain an interpolation strategy with a stability factor of order $n$. The proposed sampling algorithms are greedy procedures based on arXiv:0808.0163 and arXiv:1508.03261, with polynomial computational complexity.
title Randomized least-squares with minimal oversampling and interpolation in general spaces
topic Numerical Analysis
65D15, 41A65, 65D05, 41A81, 41A05
url https://arxiv.org/abs/2306.07435