Sampling numbers of smoothness classes via ${\ell }^1$-minimization

Abstract

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 $S_p^{r}W({\mathbb{T}}^d)$ 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.