9 citations · 17 across the 8 of their papers we have counts for
8 papers
Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates
Dmitry Kovalev, Ekaterina Borodich
Recently, several instances of non-Euclidean SGD, including SignSGD, Lion, and Muon, have attracted significant interest from the optimization community due to their practical succ…
Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization
Ekaterina Borodich, Dmitry Kovalev
In this paper, we focus on the problem of minimizing a continuously differentiable convex objective function, . Recently, Malitsky (2020); Alacaoglu et al.(2023) devel…
On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms
Dmitry Kovalev, Ekaterina Borodich
We revisit the smooth convex-concave bilinearly-coupled saddle-point problem of the form . In the highly specific case whe…
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
Dmitry Kovalev, Ekaterina Borodich, Alexander Gasnikov +1
We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network. This problem is relatively well-studied…
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity
Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich +2
We study structured convex optimization problems, with additive objective , where is (-strongly) convex, is -smooth and convex, and is -smooth, p…
Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes
Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov +5
This paper considers the problem of decentralized, personalized federated learning. For centralized personalized federated learning, a penalty that measures the deviation from the…
Decentralized Personalized Federated Learning for Min-Max Problems
Ekaterina Borodich, Aleksandr Beznosikov, Abdurakhmon Sadiev +4
Personalized Federated Learning (PFL) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of train…
On Accelerated Methods for Saddle-Point Problems with Composite Structure
Vladislav Tominin, Yaroslav Tominin, Ekaterina Borodich +3
We consider strongly-convex-strongly-concave saddle-point problems with general non-bilinear objective and different condition numbers with respect to the primal and the dual varia…