2 citations · 2 across the 6 of their papers we have counts for
7 papers
Gradient-Free Methods for Stochastic Convex Optimization with Stochastic Functional Constraints
Vadim Abronin, Alexander Gasnikov, Darina Dvinskikh
We develop accelerated gradient-free methods for stochastic convex optimization with constraints defined by expectations. Our batched primal-dual sliding method uses two-point eval…
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…
Stochastic Optimization and Data Science
Arutyun Avetisyan, Darina Dvinskikh, Alexander Gasnikov +3
This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood…
Lower and upper bounds of the convergence rate of gradient methods with composite noise in gradient
Artem Vasin, Alexander Gasnikov
We introduce a detailed analysis of the convergence of first-order methods with composite noise (sum of relative and absolute) in gradient for convex and smooth function minimizati…
Stronger constraints for smooth min-max games
Valery Krivchenko, Alexander Gasnikov, Dmitry Kovalev
Saddle point problems with smooth convex-concave objective functions are often used to model min-max problems arising in machine learning. First-order methods are the standard para…
Accelerated and nonaccelerated stochastic gradient descent with model conception
Darina Dvinskikh, Alexander Tyurin, Alexander Gasnikov +1
In this paper, we describe a new way to get convergence rates for optimal methods in smooth (strongly) convex optimization tasks. Our approach is based on results for tasks where g…
Fast gradient descent method for convex optimization problems with an oracle that generates a -model of a function in a requested point
Alexander Tyurin, Alexander Gasnikov
In this article we propose a new concept of a -model of a function which generalizes the concept of the -oracle (Devolder-Glineur-Nesterov). Using this concept we des…