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.

arXiv comment9 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