28 citations · 184 across the 34 of their papers we have counts for
9 papers · 1 filter
Accelerated gradient sliding and variance reduction
Darina Dvinskikh, Sergey Omelchenko, Alexander Tyurin +1
We consider sum-type strongly convex optimization problem (first term) with smooth convex not proximal friendly composite (second term). We show that the complexity of this problem…
Adaptive Gradient Descent for Convex and Non-Convex Stochastic Optimization
Darina Dvinskikh, Aleksandr Ogaltsov, Alexander Gasnikov +3
In this paper we propose several adaptive gradient methods for stochastic optimization. Unlike AdaGrad-type of methods, our algorithms are based on Armijo-type line search and they…
Optimal Decentralized Distributed Algorithms for Stochastic Convex Optimization
Eduard Gorbunov, Darina Dvinskikh, Alexander Gasnikov
We consider stochastic convex optimization problems with affine constraints and develop several methods using either primal or dual approach to solve it. In the primal case, we use…
Accelerated methods for composite non-bilinear saddle point problem
Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin +2
Based on G. Lan's accelerated gradient sliding and general relation between the smoothness and strong convexity parameters of function under Legendre transformation we show that un…
Decentralized and Parallel Primal and Dual Accelerated Methods for Stochastic Convex Programming Problems
Darina Dvinskikh, Alexander Gasnikov
We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles, the proposed methods are optimal i…
Gradient Methods for Problems with Inexact Model of the Objective
Fedor Stonyakin, Darina Dvinskikh, Pavel Dvurechensky +8
We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a parti…
On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks
Darina Dvinskikh, Eduard Gorbunov, Alexander Gasnikov +2
We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of com…
Inexact Model: A Framework for Optimization and Variational Inequalities
Fedor Stonyakin, Alexander Gasnikov, Alexander Tyurin +6
In this paper we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variat…
On the Complexity of Approximating Wasserstein Barycenter
Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky +3
We study the complexity of approximating Wassertein barycenter of discrete measures, or histograms of size by contrasting two alternative approaches, both using entropic re…