  We discuss non-Euclidean deterministic and stochastic algorithms for
optimization problems with strongly and uniformly convex objectives. We provide
accuracy bounds for the performance of these algorithms and design methods
which are adaptive with respect to the parameters of strong or uniform
convexity of the objective: in the case when the total number of iterations $N$
is fixed, their accuracy coincides, up to a logarithmic in $N$ factor with the
accuracy of optimal algorithms.
