13 citations · 13 across the 1 of their papers we have counts for
5 papers
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
Dmitry Kovalev, Egor Shulgin, Peter Richtárik +2
We propose ADOM - an accelerated method for smooth and strongly convex decentralized optimization over time-varying networks. ADOM uses a dual oracle, i.e., we assume access to the…
Adaptive Catalyst for Smooth Convex Optimization
Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko +3
In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization probl…
Lecture Notes on Stochastic Processes
Alexander Gasnikov, Eduard Gorbunov, Sergey Guz +3
This is lecture notes on the course "Stochastic Processes". In this format, the course was taught in the spring semesters 2017 and 2018 for third-year bachelor students of the Depa…
Revisiting Stochastic Extragradient
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin +2
We fix a fundamental issue in the stochastic extragradient method by providing a new sampling strategy that is motivated by approximating implicit updates. Since the existing stoch…
SGD: General Analysis and Improved Rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian +3
We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of varia…