4 citations · 5 across the 16 of their papers we have counts for
3 papers · 1 filter
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…
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.…
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…