1 citations · 2 across the 7 of their papers we have counts for
7 papers
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar +1
Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models.…
Mirror Descent Methods with Weighting Scheme for Outputs for Constrained Variational Inequality Problems
Mohammad S. Alkousa, Belal A. Alashqar, Fedor S. Stonyakin +2
This paper is devoted to the variational inequality problems. We consider two classes of problems, the first is classical constrained variational inequality and the second is the s…
Adaptive Methods or Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators
S. S. Ablaev, F. S. Stonyakin, M. S. Alkousa +1
The article is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Starting from the recently proposed…
Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum
S. S. Ablaev, D. V. Makarenko, F. S. Stonyakin +2
In this paper we propose a generalized condition for a sharp minimum, somewhat similar to the inexact oracle proposed recently by Devolder-Glineur-Nesterov. The proposed approach m…
Some Adaptive First-order Methods for Variational Inequalities with Relatively Strongly Monotone Operators and Generalized Smoothness
A. A. Titov, S. S. Ablaev, M. S. Alkousa +2
In this paper, we introduce some adaptive methods for solving variational inequalities with relatively strongly monotone operators. Firstly, we focus on the modification of the rec…
Adaptive Gradient-type Methods for Convex Optimization Problems with Relative Accuracy and Sharp Minimum
Fedor S. Stonyakin, Seydamet S. Ablaev, Inna V. Baran
In this paper, we consider gradient-type methods for convex positively homogeneous optimization problems with relative accuracy. An analogue of the accelerated universal gradient-t…
Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Problems
Alexander Titov, Fedor Stonyakin, Mohammad Alkousa +2
Recently some specific classes of non-smooth and non-Lipschitz convex optimization problems were selected by Yu.~Nesterov along with H.~Lu. We consider convex programming problems…