Primal-dual mirror descent for the stochastic programming problems with functional constraints

Abstract

We propose primal-dual stochastic mirror descent for the convex optimization problems with functional constraints. We obtain the rate of convergence in terms of probability of large deviations.

9 pages, in Russian. Comp. Math. & Mat. Phys. 2018. V. 58

References in corpus (1)

• Decomposition Techniques for Bilinear Saddle Point Problems and Variational Inequalities with Affine Monotone Operators on Domains Given by Linear Minimization Oracles

Cited by in corpus (1)

• One Mirror Descent Algorithm for Convex Constrained Optimization Problems with non-standard growth properties