  In this paper, we propose a new way to obtain optimal convergence rates for
smooth stochastic (strong) convex optimization tasks. Our approach is based on
results for optimization tasks where gradients have nonrandom noise. In
contrast to previously known results, we extend our idea to the inexact model
conception.
