81 citations · 116 across the 5 of their papers we have counts for
10 papers
From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov +2
Most algorithms for solving optimization problems or finding saddle points of convex-concave functions are fixed-point algorithms. In this work we consider the generic problem of f…
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely, Dmitry Kovalev, Peter Richtarik
We propose an accelerated version of stochastic variance reduced coordinate descent -- ASVRCD. As other variance reduced coordinate descent methods such as SEGA or SVRCD, our metho…
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li, Dmitry Kovalev, Xun Qian +1
Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While…
Distributed Fixed Point Methods with Compressed Iterates
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev +3
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated…
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates
Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik
We present two new remarkably simple stochastic second-order methods for minimizing the average of a very large number of sufficiently smooth and strongly convex functions. The fir…
Accelerated methods for composite non-bilinear saddle point problem
Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin +2
Based on G. Lan's accelerated gradient sliding and general relation between the smoothness and strong convexity parameters of function under Legendre transformation we show that un…