  Stochastic non-smooth convex optimization constitutes a class of problems in
machine learning and operations research. This paper considers minimization of
a non-smooth function based on stochastic subgradients. When the function has a
locally polyhedral structure, a staggered time average algorithm is proven to
have O(1/T) convergence rate. A more general convergence result is proven when
the locally polyhedral assumption is removed. In that case, the convergence
bound depends on the curvature of the function near the minimum. Finally, the
locally polyhedral assumption is shown to improve convergence beyond O(1/T) for
a special case of deterministic problems.
