  In approximation of functions based on point values, least-squares methods
provide more stability than interpolation, at the expense of increasing the
sampling budget. We show that near-optimal approximation error can nevertheless
be achieved, in an expected $L^2$ sense, as soon as the sample size $m$ is
larger than the dimension $n$ of the approximation space by a constant ratio.
On the other hand, for $m=n$, we obtain an interpolation strategy with a
stability factor of order $n$. The proposed sampling algorithms are greedy
procedures based on arXiv:0808.0163 and arXiv:1508.03261, with polynomial
computational complexity.
