  Using double-smoothing technique and stochastic mirror descent with inexact
oracle we built an optimal algorithm (up to a multiplicative factor) for
two-points gradient-free non-smooth stochastic convex programming. We
investigate how much can be the level of noise (the nature of this noise isn't
necessary stochastic) for the rate of convergence to be maintained (up to a
multiplicative factor).
