15 citations · 58 across the 38 of their papers we have counts for
43 papers
Descending Everest with First-Order Oracles: Oracle Complexity of Convex Optimization under -Smoothness
Mikhail Rudomanov, Ivan Fomin, Alexander V. Gasnikov +1
For convex -smooth functions, the curvature may grow together with the gradient norm. Therefore, even when , the gradient and the optimality gap at the…
An Inexact Augmented Lagrangian Method for -Smooth Convex Optimization
Aleksandr Vyguzov, Fedor Stonyakin
Augmented Lagrangian methods are among the most effective approaches for solving constrained convex optimization problems. However, classical complexity analyses of first-order met…
Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients
Evgeniy Kovalev, Fedor Stonyakin
Generalized smoothness, such as (L0, L1)-smoothness, have recently attracted considerable attention due to their ability to model optimization problems arising in modern machine an…
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…
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…
On Some Versions of Subspace Optimization Methods with Inexact Gradient Information
Ilya Kuruzov, Fedor Stonyakin
It is well-known that accelerated gradient methods possess optimal complexity estimates for the class of convex smooth minimization problems. In many practical situations, it makes…
Higher Degree Inexact Model for Optimization problems
Mohammad Alkousa, Fedor Stonyakin, Alexander Gasnikov +2
In this paper, it was proposed a new concept of the inexact higher degree -model of a function that is a generalization of the inexact -model, -oracle an…
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…
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…
Subgradient methods with variants of Polyak step-size for quasi-convex optimization with inequality constraints for analogues of sharp minima
S. M. Puchinin, E. R. Korolkov, F. S. Stonyakin +2
In this paper, we consider two variants of the concept of sharp minimum for mathematical programming problems with quasiconvex objective function and inequality constraints. It inv…
About some works of Boris Polyak on convergence of gradient methods and their development
Seydamet Ablaev, Aleksandr Beznosikov, Alexander Gasnikov +4
The paper presents a review of the state-of-the-art of subgradient and accelerated methods of convex optimization, including in the presence of disturbances and access to various i…
Intermediate Gradient Methods with Relative Inexactness
Nikita Kornilov, Eduard Gorbunov, Mohammad Alkousa +3
This paper is devoted to first-order algorithms for smooth convex optimization with inexact gradients. Unlike the majority of the literature on this topic, we consider the setting…
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…
Adaptive Variant of the Frank-Wolfe Algorithm for Convex Optimization Problems
G. V. Aivazian, F. S. Stonyakin, D. A. Pasechnyuk +2
Some variant of the Frank-Wolfe method for convex optimization problems with adaptive selection of the step parameter corresponding to information about the smoothness of the objec…
Highly Smoothness Zero-Order Methods for Solving Optimization Problems under PL Condition
Aleksandr Lobanov, Alexander Gasnikov, Fedor Stonyakin
In this paper, we study the black box optimization problem under the Polyak--Lojasiewicz (PL) condition, assuming that the objective function is not just smooth, but has higher smo…
Online Optimization Problems with Functional Constraints under Relative Lipschitz Continuity and Relative Strong Convexity Conditions
Oleg Savchuk, Fedor Stonyakin, Mohammad Alkousa +3
Recently, there were introduced important classes of relatively smooth, relatively continuous, and relatively strongly convex optimization problems. These concepts have significant…
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…
Gradient-Type Methods for Optimization Problems with Polyak-Łojasiewicz Condition: Early Stopping and Adaptivity to Inexactness Parameter
Ilya A. Kuruzov, Fedor S. Stonyakin, Mohammad S. Alkousa
Due to its applications in many different places in machine learning and other connected engineering applications, the problem of minimization of a smooth function that satisfies t…
Gradient-Type Methods For Decentralized Optimization Problems With Polyak-Łojasiewicz Condition Over Time-Varying Networks
Ilya Kuruzov, Mohammad Alkousa, Fedor Stonyakin +1
This paper focuses on the decentralized optimization (minimization and saddle point) problems with objective functions that satisfy Polyak-Łojasiewicz condition (PL-condition). The…
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…
Some Methods for Relatively Strongly Monotone Variational Inequalities
F. S. Stonyakin, A. A. Titov, D. V. Makarenko +1
The article is devoted to the development of numerical methods for solving variational inequalities with relatively strongly monotone operators. We consider two classes of variatio…
Sequential Subspace Optimization for Quasar-Convex Optimization Problems with Inexact Gradient
Ilya Kuruzov, Fedor Stonyakin
It is well-known that accelerated gradient first-order methods possess optimal complexity estimates for the class of convex smooth minimization problems. In many practical situatio…
Adaptive Algorithms for Relatively Lipschitz Continuous Convex Optimization Problems
Fedor Stonyakin, Alexander Titov, Mohammad Alkousa +2
Recently there were proposed some innovative convex optimization concepts, namely, relative smoothness [1] and relative strong convexity [2,3]. These approaches have significantly…