10 citations · 26 across the 11 of their papers we have counts for
5 papers · 1 filter
The Stochastic Multi-Proximal Method for Nonsmooth Optimization
Laurent Condat, Elnur Gasanov, Peter Richtárik
Stochastic gradient descent type methods are ubiquitous in machine learning, but they are only applicable to the optimization of differentiable functions. Proximal algorithms are m…
Speeding up Stochastic Proximal Optimization in the High Hessian Dissimilarity Setting
Elnur Gasanov, Peter Richtárik
Stochastic proximal point methods have recently garnered renewed attention within the optimization community, primarily due to their desirable theoretical properties. Notably, thes…
Error Feedback Shines when Features are Rare
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
We provide the first proof that gradient descent with greedy sparsification and error feedback $\left({\…
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
Dmitry Kovalev, Elnur Gasanov, Peter Richtárik +1
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network whose links are allo…
Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev, Eduard Gorbunov, Elnur Gasanov +1
The state-of-the-art methods for solving optimization problems in big dimensions are variants of randomized coordinate descent (RCD). In this paper we introduce a fundamentally new…