4 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.