  An unknown $m$ by $n$ matrix $X_0$ is to be estimated from noisy measurements
$Y=X_0+Z$, where the noise matrix $Z$ has i.i.d. Gaussian entries. A popular
matrix denoising scheme solves the nuclear norm penalization problem
$\operatorname {min}_X\|Y-X\|_F^2/2+\lambda\|X\|_*$, where $\|X\|_*$ denotes
the nuclear norm (sum of singular values). This is the analog, for matrices, of
$\ell_1$ penalization in the vector case. It has been empirically observed that
if $X_0$ has low rank, it may be recovered quite accurately from the noisy
measurement $Y$. In a proportional growth framework where the rank $r_n$,
number of rows $m_n$ and number of columns $n$ all tend to $\infty$
proportionally to each other ($r_n/m_n\rightarrow \rho$, $m_n/n\rightarrow
\beta$), we evaluate the asymptotic minimax MSE $\mathcal
{M}(\rho,\beta)=\lim_{m_n,n\rightarrow \infty}\inf_{\lambda}\sup_{\operatorname
{rank}(X)\leq r_n}\operatorname {MSE}(X_0,\hat{X}_{\lambda})$. Our formulas
involve incomplete moments of the quarter- and semi-circle laws ($\beta=1$,
square case) and the Mar\v{c}enko-Pastur law ($\beta<1$, nonsquare case). For
finite $m$ and $n$, we show that MSE increases as the nonzero singular values
of $X_0$ grow larger. As a result, the finite-$n$ worst-case MSE, a quantity
which can be evaluated numerically, is achieved when the signal $X_0$ is
"infinitely strong." The nuclear norm penalization problem is solved by
applying soft thresholding to the singular values of $Y$. We also derive the
minimax threshold, namely the value $\lambda^*(\rho)$, which is the optimal
place to threshold the singular values. All these results are obtained for
general (nonsquare, nonsymmetric) real matrices. Comparable results are
obtained for square symmetric nonnegative-definite matrices.
