Sparsity and $\ell_p$-Restricted Isometry

A matrix $A$ is said to have the $\ell_p$-Restricted Isometry Property ($\ell_p$-RIP) if for all vectors $x$ of up to some sparsity $k$, $\|Ax\|_p$ is roughly proportional to $\|x\|_p$. It is known that with high probability, random dense $m\times n$ matrices (e.g., with i.i.d.\ $\pm 1$ entries) are $\ell_2$-RIP with $k \approx m/\log n$, and sparse random matrices are $\ell_p$-RIP for $p \in [1,2)$ when $k, m = \Theta(n)$. However, when $m = \Theta(n)$, sparse random matrices are known to \emph{not} be $\ell_2$-RIP with high probability. With this backdrop, we show that there are no sparse matrices with $\pm 1$ entries that are $\ell_2$-RIP. On the other hand, for $p \neq 2$, we show that any $\ell_p$-RIP matrix \emph{must} be sparse. Thus, sparsity is incompatible with $\ell_2$-RIP, but necessary for $\ell_p$-RIP for $p \neq 2$.