  In the paper we consider an application of mirror descent (dual averaging) to
the stochastic online convex optimization problems. We compare classical mirror
descent (Nemirovski-Yudin, 1979) with dual averaging (Nesterov, 2005) and
Grigoriadis-Khachiyan algorithm (1995). Grigoriadis-Khachiyan algorithm has
just proved to be a randomized mirror descent (dual averaging) with
randomization in KL-projection of (sub)gradient to a unit simplex. We found out
that this randomization is an optimal way of solving sparse matrix games and
some other problems arising in convex optimization and experts weighting.
