1. A New Upper Bound for Sampling Numbers
- Author
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Nagel, Nicolas, Schäfer, Martin, and Ullrich, Tino
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Algorithms -- Statistics ,Algorithm ,Mathematics - Abstract
We provide a new upper bound for sampling numbers [Formula omitted] associated with the compact embedding of a separable reproducing kernel Hilbert space into the space of square integrable functions. There are universal constants [Formula omitted] (which are specified in the paper) such that gn2[less than or equal to]Clog(n)n[summation]k[greater than or equal to]âcn[right floor][sigma]k2,n[greater than or equal to]2,where [Formula omitted] is the sequence of singular numbers (approximation numbers) of the Hilbert-Schmidt embedding [Formula omitted]. The algorithm which realizes the bound is a least squares algorithm based on a specific set of sampling nodes. These are constructed out of a random draw in combination with a down-sampling procedure coming from the celebrated proof of Weaver's conjecture, which was shown to be equivalent to the Kadison-Singer problem. Our result is non-constructive since we only show the existence of a linear sampling operator realizing the above bound. The general result can for instance be applied to the well-known situation of [Formula omitted] in [Formula omitted] with [Formula omitted]. We obtain the asymptotic bound gn[less than or equal to]Cs,dn-slog(n)(d-1)s+1/2,which improves on very recent results by shortening the gap between upper and lower bound to [Formula omitted]. The result implies that for dimensions [Formula omitted] any sparse grid sampling recovery method does not perform asymptotically optimal., Author(s): Nicolas Nagel [sup.1], Martin Schäfer [sup.1], Tino Ullrich [sup.1] Author Affiliations: (1) grid.6810.f, 0000 0001 2294 5505, Faculty of Mathematics, TU Chemnitz, , 09107, Chemnitz, Germany Introduction > In [...]
- Published
- 2022
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