500 citations · 1.3k across the 52 of their papers we have counts for
22 papers · 1 filter
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…
Gradient Descent with Compressed Iterates
Ahmed Khaled, Peter Richtárik
We propose and analyze a new type of stochastic first order method: gradient descent with compressed iterates (GDCI). GDCI in each iteration first compresses the current iterate us…
First Analysis of Local GD on Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise arbitrary functions. Problems of this form and loc…
Stochastic Convolutional Sparse Coding
Jinhui Xiong, Peter Richtárik, Wolfgang Heidrich
State-of-the-art methods for Convolutional Sparse Coding usually employ Fourier-domain solvers in order to speed up the convolution operators. However, this approach is not without…
L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik
We develop and analyze a new family of {\em nonaccelerated and accelerated loopless variance-reduced methods} for finite sum optimization problems. Our convergence analysis relies…