  The joint-sparse recovery problem aims to recover, from sets of compressed
measurements, unknown sparse matrices with nonzero entries restricted to a
subset of rows. This is an extension of the single-measurement-vector (SMV)
problem widely studied in compressed sensing. We analyze the recovery
properties for two types of recovery algorithms. First, we show that recovery
using sum-of-norm minimization cannot exceed the uniform recovery rate of
sequential SMV using $\ell_1$ minimization, and that there are problems that
can be solved with one approach but not with the other. Second, we analyze the
performance of the ReMBo algorithm [M. Mishali and Y. Eldar, IEEE Trans. Sig.
Proc., 56 (2008)] in combination with $\ell_1$ minimization, and show how
recovery improves as more measurements are taken. From this analysis it follows
that having more measurements than number of nonzero rows does not improve the
potential theoretical recovery rate.
