8 citations · 15 across the 4 of their papers we have counts for
6 papers
Mirror Descent and Convex Optimization Problems With Non-Smooth Inequality Constraints
Anastasia Bayandina, Pavel Dvurechensky, Alexander Gasnikov +2
We consider the problem of minimization of a convex function on a simple set with convex non-smooth inequality constraint and describe first-order methods to solve such problems in…
Adaptive Stochastic Mirror Descent for Constrained Optimization
Anastasia Bayandina
Mirror Descent (MD) is a well-known method of solving non-smooth convex optimization problems. This paper analyzes the stochastic variant of MD with adaptive stepsizes. Its converg…
Adaptive Mirror Descent for Constrained Optimization
Anastasia Bayandina
This paper seeks to address how to solve non-smooth convex and strongly convex optimization problems with functional constraints. The introduced Mirror Descent (MD) method with ada…
Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression
Anastasia Bayandina, Elena Chernousova, Alexander Gasnikov +1
In this paper we propose a new approach to obtain mixing least square regression estimate by means of stochastic online mirror descent in non-euclidian set-up.
Gradient-free two-points optimal method for non smooth stochastic convex optimization problem with additional small noise
Anastasia Bayandina, Alexander Gasnikov, Fariman Guliev +1
Using double-smoothing technique and stochastic mirror descent with inexact oracle we built an optimal algorithm (up to a multiplicative factor) for two-points gradient-free non-sm…
Primal-dual mirror descent for the stochastic programming problems with functional constraints
Anastasia Bayandina, Alexander Gasnikov, Evgenia Gasnikova +1
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 larg…