From the 1 of 10 linked papers with an AI index.
10 papers
Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients
Evgeniy Kovalev, Fedor Stonyakin
The paper proposes and analyzes convex optimization algorithms that work with a comparison oracle providing inexact normalized gradients for (L0, L1)-smooth problems, establishing…
Adaptive Variant of Frank-Wolfe Method for Relative Smooth Convex Optimization Problems
Alexander Vyguzov, Fedor Stonyakin
The paper introduces a new adaptive version of the Frank-Wolfe algorithm for relatively smooth convex functions. It is proposed to use the Bregman divergence other than half the sq…
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…
Optimal Convergence Rate for Mirror Descent Methods with special Time-Varying Step Sizes Rules
Mohammad Alkousa, Fedor Stonyakin, Asmaa Abdo +1
In this paper, the optimal convergence rate (where is the total number of iterations performed by the algorithm), without the presence of a logarithmic…
Universal methods for variational inequalities: deterministic and stochastic cases
Anton Klimza, Alexander Gasnikov, Fedor Stonyakin +1
In this paper, we propose universal proximal mirror methods to solve the variational inequality problem with Holder continuous operators in both deterministic and stochastic settin…