Random points are optimal for the approximation of Sobolev functions
نویسندگان
چکیده
Abstract We show that independent and uniformly distributed sampling points are asymptotically as good optimal for the approximation of functions from Sobolev spaces $W_p^s(\varOmega )$ on bounded convex domains $\varOmega \subset{\mathbb{R}}^d$ in $L_q$-norm if $q<p$. More generally, we characterize quality arbitrary point sets $P\subset \varOmega $ via $L_\gamma (\varOmega )$-norm distance function dist$ (\cdot ,P)$, where $\gamma =s(1/q-1/p)^{-1}$ $q<p$ =\infty $q\ge p$. This improves upon previous characterizations based covering radius $P$.
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ژورنال
عنوان ژورنال: Ima Journal of Numerical Analysis
سال: 2023
ISSN: ['1464-3642', '0272-4979']
DOI: https://doi.org/10.1093/imanum/drad014