  Mirror Descent (MD) is a well-known method of solving non-smooth convex
optimization problems. This paper analyzes the stochastic variant of MD with
adaptive stepsizes. Its convergence on average is shown to be faster than with
the fixed stepsizes and optimal in terms of lower bounds.
