18 citations · 267 across the 76 of their papers we have counts for
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An Optimal Algorithm for Strongly Convex Min-min Optimization
Alexander Gasnikov, Dmitry Kovalev, Grigory Malinovsky
In this paper we study the smooth strongly convex minimization problem . The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{κ_x,κ_y\}}…
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities
Aleksandr Beznosikov, Alexander Gasnikov
Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of g…
Decentralized convex optimization over time-varying graphs: a survey
Alexander Rogozin, Alexander Gasnikov, Aleksander Beznosikov +1
Decentralized optimization over time-varying networks has a wide range of applications in distributed learning, signal processing and various distributed control problems. The agen…
The Mirror-Prox Sliding Method for Non-smooth decentralized saddle-point problems
Ilya Kuruzov, Alexander Rogozin, Demyan Yarmoshik +1
The saddle-point optimization problems have a lot of practical applications. This paper focuses on such non-smooth problems in decentralized case. This work contains generalization…
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey
Aleksandr Beznosikov, Boris Polyak, Eduard Gorbunov +2
This paper is a survey of methods for solving smooth (strongly) monotone stochastic variational inequalities. To begin with, we give the deterministic foundation from which the sto…
Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches
Alexander Rogozin, Demyan Yarmoshik, Ksenia Kopylova +1
Decentralized optimization is a common paradigm used in distributed signal processing and sensing as well as privacy-preserving and large-scale machine learning. It is assumed that…
Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities
Aleksandr Beznosikov, Alexander Gasnikov
Variational inequalities are an important tool, which includes minimization, saddles, games, fixed-point problems. Modern large-scale and computationally expensive practical applic…
On Scaled Methods for Saddle Point Problems
Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev +2
Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning pr…
Decentralized Saddle-Point Problems with Different Constants of Strong Convexity and Strong Concavity
Dmitriy Metelev, Alexander Rogozin, Alexander Gasnikov +1
Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear models with affine constraints. In this paper, we study distributed saddle-point problems…
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…
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
Dmitry Kovalev, Alexander Gasnikov
In this paper, we study the fundamental open question of finding the optimal high-order algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) establishe…
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
Dmitry Kovalev, Alexander Gasnikov
In this paper, we revisit the smooth and strongly-convex-strongly-concave minimax optimization problem. Zhang et al. (2021) and Ibrahim et al. (2020) established the lower bound $Ω…
Decentralized convex optimization under affine constraints for power systems control
Demyan Yarmoshik, Alexander Rogozin, Oleg. O. Khamisov +2
Modern power systems are now in continuous process of massive changes. Increased penetration of distributed generation, usage of energy storage and controllable demand require intr…
A General Framework for Distributed Partitioned Optimization
Savelii Chezhegov, Anton Novitskii, Alexander Rogozin +3
Decentralized optimization is widely used in large scale and privacy preserving machine learning and various distributed control and sensing systems. It is assumed that every agent…
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev +3
Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. Methods for variational inequalities are theref…
The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii +6
Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper,…