  We use a rank one Gaussian perturbation to derive a smooth stochastic
approximation of the maximum eigenvalue function. We then combine this
smoothing result with an optimal smooth stochastic optimization algorithm to
produce an efficient method for solving maximum eigenvalue minimization
problems. We show that the complexity of this new method is lower than that of
deterministic smoothing algorithms in certain precision/dimension regimes.
