  We investigate the convergence rate of the recently proposed subgradient-push
method for distributed optimization over time-varying directed graphs. The
subgradient-push method can be implemented in a distributed way without
requiring knowledge of either the number of agents or the graph sequence; each
node is only required to know its out-degree at each time. Our main result is a
convergence rate of $O \left((\ln t)/t \right)$ for strongly convex functions
with Lipschitz gradients even if only stochastic gradient samples are
available; this is asymptotically faster than the $O \left((\ln t)/\sqrt{t}
\right)$ rate previously known for (general) convex functions.
