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math.OC2026
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…
math.OC2025
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…
math.OC2024
New Aspects of Black Box Conditional Gradient: Variance Reduction and One Point Feedback
Andrey Veprikov, Aleksandr Bogdanov, Vladislav Minashkin +1
This paper deals with the black-box optimization problem. In this setup, we do not have access to the gradient of the objective function, therefore, we need to estimate it somehow.…