18 citations · 267 across the 76 of their papers we have counts for
16 papers · 1 filter
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Dmitry Kovalev, Alexander Gasnikov, Peter Richtárik
In this paper we study the convex-concave saddle-point problem , where and are smooth and convex functions. We propose a…
Acceleration in Distributed Optimization under Similarity
Ye Tian, Gesualdo Scutari, Tianyu Cao +1
We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes. The loss functions of the agents are assumed to be \textit{similar…
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Aleksandr Beznosikov, Peter Richtárik, Michael Diskin +2
Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transp…
Distributed Saddle-Point Problems Under Similarity
Aleksandr Beznosikov, Gesualdo Scutari, Alexander Rogozin +1
We study solution methods for (strongly-)convex-(strongly)-concave Saddle-Point Problems (SPPs) over networks of two type - master/workers (thus centralized) architectures and mesh…
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…
Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks
Aleksandr Beznosikov, Alexander Rogozin, Dmitry Kovalev +1
Decentralized optimization methods have been in the focus of optimization community due to their scalability, increasing popularity of parallel algorithms and many applications. In…
One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
Aleksandr Beznosikov, Ivan Stepanov, Artyom Voronov +1
This work is devoted to solving the composite optimization problem with the mixture oracle: for the smooth part of the problem, we have access to the gradient, and for the non-smoo…
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
Dmitry Kovalev, Elnur Gasanov, Peter Richtárik +1
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network whose links are allo…
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Aleksandr Beznosikov, Pavel Dvurechensky, Anastasia Koloskova +3
We consider distributed stochastic variational inequalities (VIs) on unbounded domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We…
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…
Solving smooth min-min and min-max problems by mixed oracle algorithms
Egor Gladin, Abdurakhmon Sadiev, Alexander Gasnikov +3
In this paper, we consider two types of problems that have some similarity in their structure, namely, min-min problems and min-max saddle-point problems. Our approach is based on…
One-Point Gradient-Free Methods for Smooth and Non-Smooth Saddle-Point Problems
Aleksandr Beznosikov, Vasilii Novitskii, Alexander Gasnikov
In this paper, we analyze gradient-free methods with one-point feedback for stochastic saddle point problems . For non-smooth and smooth cases, we present…
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
Dmitry Kovalev, Egor Shulgin, Peter Richtárik +2
We propose ADOM - an accelerated method for smooth and strongly convex decentralized optimization over time-varying networks. ADOM uses a dual oracle, i.e., we assume access to the…
Decentralized Distributed Optimization for Saddle Point Problems
Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh +3
We consider distributed convex-concave saddle point problems over arbitrary connected undirected networks and propose a decentralized distributed algorithm for their solution. The…
Newton Method over Networks is Fast up to the Statistical Precision
Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky +1
We propose a distributed cubic regularization of the Newton method for solving (constrained) empirical risk minimization problems over a network of agents, modeled as undirected gr…