  Let $X_0$ be an unknown $M$ by $N$ matrix. In matrix recovery, one takes $n <
MN$ linear measurements $y_1,..., y_n$ of $X_0$, where $y_i = \Tr(a_i^T X_0)$
and each $a_i$ is a $M$ by $N$ matrix. For measurement matrices with Gaussian
i.i.d entries, it known that if $X_0$ is of low rank, it is recoverable from
just a few measurements. A popular approach for matrix recovery is Nuclear Norm
Minimization (NNM). Empirical work reveals a \emph{phase transition} curve,
stated in terms of the undersampling fraction $\delta(n,M,N) = n/(MN)$, rank
fraction $\rho=r/N$ and aspect ratio $\beta=M/N$. Specifically, a curve
$\delta^* = \delta^*(\rho;\beta)$ exists such that, if $\delta >
\delta^*(\rho;\beta)$, NNM typically succeeds, while if $\delta <
\delta^*(\rho;\beta)$, it typically fails. An apparently quite different
problem is matrix denoising in Gaussian noise, where an unknown $M$ by $N$
matrix $X_0$ is to be estimated based on direct noisy measurements $Y = X_0 +
Z$, where the matrix $Z$ has iid Gaussian entries. It has been empirically
observed that, if $X_0$ has low rank, it may be recovered quite accurately from
the noisy measurement $Y$. A popular matrix denoising scheme solves the
unconstrained optimization problem $\text{min} \| Y - X \|_F^2/2 + \lambda
\|X\|_* $. When optimally tuned, this scheme achieves the asymptotic minimax
MSE $\cM(\rho) = \lim_{N \goto \infty} \inf_\lambda \sup_{\rank(X) \leq \rho
\cdot N} MSE(X,\hat{X}_\lambda)$. We report extensive experiments showing that
the phase transition $\delta^*(\rho)$ in the first problem coincides with the
minimax risk curve $\cM(\rho)$ in the second problem, for {\em any} rank
fraction $0 < \rho < 1$.
