Gradient and gradient-free methods for stochastic convex optimization with inexact oracle

Understand

In the paper we generalize universal gradient method (Yu.

• Nesterov) to strongly convex case and to Intermediate gradient method (Devolder-Glineur-Nesterov).
• We also consider possible generalizations to stochastic and online context.
• We show how these results can be generalized to gradient-free method and method of random direction search.