28 citations · 184 across the 34 of their papers we have counts for
9 papers · 1 filter
Recent theoretical advances in decentralized distributed convex optimization
Eduard Gorbunov, Alexander Rogozin, Aleksandr Beznosikov +2
In the last few years, the theory of decentralized distributed convex optimization has made significant progress. The lower bounds on communications rounds and oracle calls have ap…
Parallel and Distributed algorithms for ML problems
Darina Dvinskikh, Alexander Gasnikov, Alexander Rogozin +1
In this paper we make a survey of modern parallel and distributed approaches to solve sum-type convex minimization problems come from ML applications.
Improved Complexity Bounds in Wasserstein Barycenter Problem
Darina Dvinskikh, Daniil Tiapkin
In this paper, we focus on computational aspects of the Wasserstein barycenter problem. We propose two algorithms to compute Wasserstein barycenters of discrete measures of siz…
Accelerated and nonaccelerated stochastic gradient descent with inexact model
Darina Dvinskikh, Alexander Tyurin, Alexander Gasnikov +1
In this paper, we propose a new way to obtain optimal convergence rates for smooth stochastic (strong) convex optimization tasks. Our approach is based on results for optimization…
Accelerated meta-algorithm for convex optimization
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +5
We propose an accelerated meta-algorithm, which allows to obtain accelerated methods for convex unconstrained minimization in different settings. As an application of the general s…
Oracle Complexity Separation in Convex Optimization
Anastasiya Ivanova, Evgeniya Vorontsova, Dmitry Pasechnyuk +4
Many convex optimization problems have structured objective function written as a sum of functions with different types of oracles (full gradient, coordinate derivative, stochastic…
Inexact Relative Smoothness and Strong Convexity for Optimization and Variational Inequalities by Inexact Model
Fedor Stonyakin, Alexander Tyurin, Alexander Gasnikov +7
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 vari…
Stochastic Approximation versus Sample Average Approximation for population Wasserstein barycenters
Darina Dvinskikh
In the machine learning and optimization community, there are two main approaches for the convex risk minimization problem, namely, the Stochastic Approximation (SA) and the Sample…
Accelerated and nonaccelerated stochastic gradient descent with model conception
Darina Dvinskikh, Alexander Tyurin, Alexander Gasnikov +1
In this paper, we describe a new way to get convergence rates for optimal methods in smooth (strongly) convex optimization tasks. Our approach is based on results for tasks where g…