1 citations · 1 across the 5 of their papers we have counts for
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Markovian Compression: Looking to the Past Helps Accelerate the Future
Andrey Veprikov, Vladimir Solodkin, Mikhail Rudakov +2
This paper deals with distributed optimization problems that use compressed communication to achieve efficient performance and mitigate communication bottleneck. We propose a famil…
Methods for Solving Variational Inequalities with Markovian Stochasticity
Vladimir Solodkin, Michael Ermoshin, Roman Gavrilenko +1
In this paper, we present a novel stochastic method for solving variational inequalities (VI) in the context of Markovian noise. By leveraging Extragradient technique, we can produ…
Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry
Vladimir Solodkin, Andrew Veprikov, Aleksandr Beznosikov
This paper examines a variety of classical optimization problems, including well-known minimization tasks and more general variational inequalities. We consider a stochastic formul…
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…