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Function recovery on manifolds using scattered data.
- Source :
-
Journal of Approximation Theory . Jan2025, Vol. 305, pN.PAG-N.PAG. 1p. - Publication Year :
- 2025
-
Abstract
- We consider the task of recovering a Sobolev function on a connected compact Riemannian manifold M when given a sample on a finite point set. We prove that the quality of the sample is given by the L γ (M) -average of the geodesic distance to the point set and determine the value of γ ∈ (0 , ∞ ]. This extends our findings on bounded convex domains [IMA J. Numer. Anal., 2024]. As a byproduct, we prove the optimal rate of convergence of the n th minimal worst case error for L q (M) -approximation for all 1 ≤ q ≤ ∞. Further, a limit theorem for moments of the average distance to a set consisting of i.i.d. uniform points is proven. This yields that a random sample is asymptotically as good as an optimal sample in precisely those cases with γ < ∞. In particular, we obtain that cubature formulas with random nodes are asymptotically as good as optimal cubature formulas if the weights are chosen correctly. This closes a logarithmic gap left open by Ehler, Gräf and Oates [Stat. Comput., 29:1203-1214, 2019]. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00219045
- Volume :
- 305
- Database :
- Academic Search Index
- Journal :
- Journal of Approximation Theory
- Publication Type :
- Academic Journal
- Accession number :
- 181221260
- Full Text :
- https://doi.org/10.1016/j.jat.2024.106098