We provide a new upper bound for sampling numbers (gn)n∈N\documentclass[12pt]{minimal}
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\begin{document}$$(g_n)_{n\in \mathbb {N}}$$\end{document} associated with the compact embedding of a separable reproducing kernel Hilbert space into the space of square integrable functions. There are universal constants C,c>0\documentclass[12pt]{minimal}
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\begin{document}$$C,c>0$$\end{document} (which are specified in the paper) such that gn2≤Clog(n)n∑k≥⌊cn⌋σk2,n≥2,\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} g^2_n \le \frac{C\log (n)}{n}\sum \limits _{k\ge \lfloor cn \rfloor } \sigma _k^2,\quad n\ge 2, \end{aligned}$$\end{document}where (σk)k∈N\documentclass[12pt]{minimal}
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\begin{document}$$(\sigma _k)_{k\in \mathbb {N}}$$\end{document} is the sequence of singular numbers (approximation numbers) of the Hilbert–Schmidt embedding Id:H(K)→L2(D,ϱD)\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm {Id}:H(K) \rightarrow L_2(D,\varrho _D)$$\end{document}. 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 Hmixs(Td)\documentclass[12pt]{minimal}
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\begin{document}$$H^s_{\text {mix}}(\mathbb {T}^d)$$\end{document} in L2(Td)\documentclass[12pt]{minimal}
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\begin{document}$$L_2(\mathbb {T}^d)$$\end{document} with s>1/2\documentclass[12pt]{minimal}
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\begin{document}$$s>1/2$$\end{document}. We obtain the asymptotic bound gn≤Cs,dn-slog(n)(d-1)s+1/2,\documentclass[12pt]{minimal}
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\begin{document}$$\begin{aligned} g_n \le C_{s,d}n^{-s}\log (n)^{(d-1)s+1/2}, \end{aligned}$$\end{document}which improves on very recent results by shortening the gap between upper and lower bound to log(n)\documentclass[12pt]{minimal}
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\begin{document}$$\sqrt{\log (n)}$$\end{document}. The result implies that for dimensions d>2\documentclass[12pt]{minimal}
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\begin{document}$$d>2$$\end{document} any sparse grid sampling recovery method does not perform asymptotically optimal.
