Mathematics > Numerical Analysis
[Submitted on 1 Dec 2022 (v1), last revised 31 Jul 2023 (this version, v3)]
Title:Sampling numbers of smoothness classes via $\ell^1$-minimization
View PDFAbstract:Using techniques developed recently in the field of compressed sensing we prove new upper bounds for general (nonlinear) sampling numbers of (quasi-)Banach smoothness spaces in $L^2$. In particular, we show that in relevant cases such as mixed and isotropic weighted Wiener classes or Sobolev spaces with mixed smoothness, sampling numbers in $L^2$ can be upper bounded by best $n$-term trigonometric widths in $L^\infty$. We describe a recovery procedure from $m$ function values based on $\ell^1$-minimization (basis pursuit denoising). With this method, a significant gain in the rate of convergence compared to recently developed linear recovery methods is achieved. In this deterministic worst-case setting we see an additional speed-up of $m^{-1/2}$ (up to log factors) compared to linear methods in case of weighted Wiener spaces. For their quasi-Banach counterparts even arbitrary polynomial speed-up is possible. Surprisingly, our approach allows to recover mixed smoothness Sobolev functions belonging to $S^r_pW(\mathbb{T}^d)$ on the $d$-torus with a logarithmically better rate of convergence than any linear method can achieve when $1 < p < 2$ and $d$ is large. This effect is not present for isotropic Sobolev spaces.
Submission history
From: Thomas Jahn [view email][v1] Thu, 1 Dec 2022 11:31:32 UTC (23 KB)
[v2] Tue, 31 Jan 2023 09:24:11 UTC (35 KB)
[v3] Mon, 31 Jul 2023 19:01:08 UTC (43 KB)
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