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40 papers · 1 filter
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…
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,…
About some works of Boris Polyak on convergence of gradient methods and their development
Seydamet Ablaev, Aleksandr Beznosikov, Alexander Gasnikov +4
The paper presents a review of the state-of-the-art of subgradient and accelerated methods of convex optimization, including in the presence of disturbances and access to various i…
Real Acceleration of Communication Process in Distributed Algorithms with Compression
Svetlana Tkachenko, Artem Andreev, Aleksandr Beznosikov +1
Modern applied optimization problems become more and more complex every day. Due to this fact, distributed algorithms that can speed up the process of solving an optimization probl…
Decentralized Optimization Over Slowly Time-Varying Graphs: Algorithms and Lower Bounds
Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin +2
We consider a decentralized convex unconstrained optimization problem, where the cost function can be decomposed into a sum of strongly convex and smooth functions, associated with…
Non-Smooth Setting of Stochastic Decentralized Convex Optimization Problem Over Time-Varying Graphs
Aleksandr Lobanov, Andrew Veprikov, Georgiy Konin +3
Distributed optimization has a rich history. It has demonstrated its effectiveness in many machine learning applications, etc. In this paper we study a subclass of distributed opti…