Low-rank matrix recovery addresses the problem of recovering an unknown
low-rank matrix from few linear measurements. Nuclear-norm minimization is a
tractible approach with a recent surge of strong theoretical backing. Analagous
to the theory of compressed sensing, these results have required random
measurements. For example, m >= Cnr Gaussian measurements are sufficient to
recover any rank-r n x n matrix with high probability. In this paper we address
the theoretical question of how many measurements are needed via any method
whatsoever --- tractible or not. We show that for a family of random
measurement ensembles, m >= 4nr - 4r^2 measurements are sufficient to guarantee
that no rank-2r matrix lies in the null space of the measurement operator with
probability one. This is a necessary and sufficient condition to ensure uniform
recovery of all rank-r matrices by rank minimization. Furthermore, this value
of m precisely matches the dimension of the manifold of all rank-2r matrices.
We also prove that for a fixed rank-r matrix, m >= 2nr - r^2 + 1 random
measurements are enough to guarantee recovery using rank minimization. These
results give a benchmark to which we may compare the efficacy of nuclear-norm
minimization
