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High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
During recent years the interest of optimization and machine learning communities in high- probability convergence of stochastic …
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik
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Communication acceleration of local gradient methods via an accelerated primal-dual algorithm with inexact prox
Inspired by a recent breakthrough of Mishchenko et al (2022), who for the first time showed that local gradient steps can lead to …
Abdurakhmon Sadiev, Dmitry Kovalev, Peter Richárik
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Optimal algorithms for decentralized stochastic variational inequalities
Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. …
Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev, Michael Persiianov, Peter Richtárik, Alexander Gasnikov
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Zeroth-order algorithms for smooth saddle-point problems
Saddle-point problems have recently gained an increased attention from the machine learning community, mainly due to applications in …
Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel Dvurechensky, Alexander Gasnikov
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Solving smooth min-min and min-max problems by mixed oracle algorithms
In this paper, we consider two types of problems that have some similarity in their structure, namely, min-min problems and min-max …
Egor Gladin, Abdurakhmon Sadiev, Alexander Gasnikov, Pavel Dvurechensky, Aleksandr Beznosikov, Mohammad Alkousa
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Gradient-free methods with inexact oracle for convex-concave stochastic saddle-point problem
In the paper, we generalize the approach Gasnikov et al. 2017, which allows to solve (stochastic) convex optimization problems with an …
Aleksandr Beznosikov, Abdurakhmon Sadiev, Alexander Gasnikov
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