101 citations · 414 across the 51 of their papers we have counts for
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Derivative-Free Method For Composite Optimization With Applications To Decentralized Distributed Optimization
Aleksandr Beznosikov, Eduard Gorbunov, Alexander Gasnikov
In this paper, we propose a new method based on the Sliding Algorithm from Lan(2016, 2019) for the convex composite optimization problem that includes two terms: smooth one and non…
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…
Lecture Notes on Stochastic Processes
Alexander Gasnikov, Eduard Gorbunov, Sergey Guz +3
This is lecture notes on the course "Stochastic Processes". In this format, the course was taught in the spring semesters 2017 and 2018 for third-year bachelor students of the Depa…
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
Eduard Gorbunov, Filip Hanzely, Peter Richtárik
In this paper we introduce a unified analysis of a large family of variants of proximal stochastic gradient descent ({\tt SGD}) which so far have required different intuitions, con…
A Stochastic Derivative Free Optimization Method with Momentum
Eduard Gorbunov, Adel Bibi, Ozan Sener +2
We consider the problem of unconstrained minimization of a smooth objective function in in setting where only function evaluations are possible. We propose and analy…
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…
Stochastic Three Points Method for Unconstrained Smooth Minimization
El Houcine Bergou, Eduard Gorbunov, Peter Richtárik
In this paper we consider the unconstrained minimization problem of a smooth function in in a setting where only function evaluations are possible. We design a nov…
Distributed Learning with Compressed Gradient Differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč +1
Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods bas…