18 citations · 267 across the 76 of their papers we have counts for
5 papers · 1 filter
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
Aleksandr Beznosikov, Valentin Samokhin, Alexander Gasnikov
This paper focuses on the distributed optimization of stochastic saddle point problems. The first part of the paper is devoted to lower bounds for the centralized and decentralized…
Towards accelerated rates for distributed optimization over time-varying networks
Alexander Rogozin, Vladislav Lukoshkin, Alexander Gasnikov +2
We study the problem of decentralized optimization over time-varying networks with strongly convex smooth cost functions. In our approach, nodes run a multi-step gossip procedure a…
Zeroth-Order Algorithms for Smooth Saddle-Point Problems
Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel Dvurechensky +1
Saddle-point problems have recently gained increased attention from the machine learning community, mainly due to applications in training Generative Adversarial Networks using sto…
Linearly Convergent Gradient-Free Methods for Minimization of Parabolic Approximation
Aleksandra Bazarova, Aleksandr Beznosikov, Alexander Gasnikov
Finding the global minimum of non-convex functions is one of the main and most difficult problems in modern optimization. In the first part of the paper, we consider a certain clas…
Gradient-Free Methods for Saddle-Point Problem
Aleksandr Beznosikov, Abdurakhmon Sadiev, Alexander Gasnikov
In the paper, we generalize the approach Gasnikov et. al, 2017, which allows to solve (stochastic) convex optimization problems with an inexact gradient-free oracle, to the convex-…