  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.
