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Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Dmitry Kovalev, Alexander Gasnikov, Peter Richtárik
In this paper we study the convex-concave saddle-point problem , where and are smooth and convex functions. We propose a…
Error Compensated Loopless SVRG, Quartz, and SDCA for Distributed Optimization
Xun Qian, Hanze Dong, Peter Richtárik +1
The communication of gradients is a key bottleneck in distributed training of large scale machine learning models. In order to reduce the communication cost, gradient compression (…
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…
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
Laurent Condat, Peter Richtárik
We propose a generic variance-reduced algorithm, which we call MUltiple RANdomized Algorithm (MURANA), for minimizing a sum of several smooth functions plus a regularizer, in a seq…
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…
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Adil Salim, Laurent Condat, Dmitry Kovalev +1
Optimization problems under affine constraints appear in various areas of machine learning. We consider the task of minimizing a smooth strongly convex function F(x) under the affi…