18 citations · 267 across the 76 of their papers we have counts for
11 papers · 2 filters
Method with Batching for Stochastic Finite-Sum Variational Inequalities in Non-Euclidean Setting
Alexander Pichugin, Maksim Pechin, Aleksandr Beznosikov +2
Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider s…
Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
Andrey Sadchikov, Savelii Chezhegov, Aleksandr Beznosikov +1
Distributed optimization plays an important role in modern large-scale machine learning and data processing systems by optimizing the utilization of computational resources. One of…
Average-case optimization analysis for distributed consensus algorithms on regular graphs
Nhat Trung Nguyen, Alexander Rogozin, Alexander Gasnikov
The consensus problem in distributed computing involves a network of agents aiming to compute the average of their initial vectors through local communication, represented by an un…
Decentralized Optimization with Coupled Constraints
Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev +3
We consider the decentralized minimization of a separable objective , where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\math…
Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
Vladimir Solodkin, Savelii Chezhegov, Ruslan Nazikov +2
Stochastic optimization is a vital field in the realm of mathematical optimization, finding applications in diverse areas ranging from operations research to machine learning. In t…
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin +3
The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known methods for solving constrained optimization problems appearing in various machine learning tasks. Th…
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…
Accelerated Methods with Compression for Horizontal and Vertical Federated Learning
Sergey Stanko, Timur Karimullin, Aleksandr Beznosikov +1
Distributed optimization algorithms have emerged as a superior approaches for solving machine learning problems. To accommodate the diverse ways in which data can be stored across…
Extragradient Sliding for Composite Non-Monotone Variational Inequalities
Roman Emelyanov, Andrey Tikhomirov, Aleksandr Beznosikov +1
Variational inequalities offer a versatile and straightforward approach to analyzing a broad range of equilibrium problems in both theoretical and practical fields. In this paper,…
Decentralized Finite-Sum Optimization over Time-Varying Networks
Dmitry Metelev, Savelii Chezhegov, Alexander Rogozin +4
We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently s…
Optimal Data Splitting in Distributed Optimization for Machine Learning
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov +1
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-di…