Fedor S. Stonyakin

According to our database, Fedor S. Stonyakin authored at least 15 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints.

Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar, Seydamet S. Ablaev

CoRR, May, 2026

2025

Intermediate Gradient Methods with Relative Inexactness.

Nikita Kornilov, Mohammad S. Alkousa, Eduard Gorbunov, Fedor S. Stonyakin, Pavel E. Dvurechensky, Alexander V. Gasnikov

J. Optim. Theory Appl., December, 2025

Gradient-type methods for decentralized optimization problems with Polyak-Łojasiewicz condition over time-varying networks.

Ilya A. Kuruzov, Mohammad S. Alkousa, Fedor S. Stonyakin, Alexander V. Gasnikov

Optim. Methods Softw., September, 2025

A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization.

A. A. Vyguzov, Fedor S. Stonyakin

Proceedings of the Optimization and Applications - 16th International Conference, 2025

On the Convergence of First-Order Methods for Quasar Convex Functions.

O. Santiago Valdivia Viscarra, Fedor S. Stonyakin

Proceedings of the Optimization and Applications - 16th International Conference, 2025

2023

Adaptive Variant of the Frank-Wolfe Algorithm for Convex Optimization Problems.

G. V. Aivazian, Fedor S. Stonyakin, D. A. Pasechnyk, Mohammad S. Alkousa, A. M. Raigorodsky, I. V. Baran

Program. Comput. Softw., December, 2023

Adaptive Methods for Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators.

Seydamet S. Ablaev, Fedor S. Stonyakin, Mohammad S. Alkousa, D. A. Pasechnyk

Program. Comput. Softw., December, 2023

Stopping Rules for Gradient Methods for Non-convex Problems with Additive Noise in Gradient.

Fedor S. Stonyakin, Ilya A. Kuruzov, Boris T. Polyak

J. Optim. Theory Appl., August, 2023

2022

Generalized Mirror Prox Algorithm for Monotone Variational Inequalities: Universality and Inexact Oracle.

Fedor S. Stonyakin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Alexander A. Titov, Mohammad S. Alkousa

J. Optim. Theory Appl., 2022

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

Proceedings of the Advances in Optimization and Applications, 2022

Some Adaptive First-Order Methods for Variational Inequalities with Relatively Strongly Monotone Operators and Generalized Smoothness.

Seydamet S. Ablaev, Alexander A. Titov, Fedor S. Stonyakin, Mohammad S. Alkousa, Alexander V. Gasnikov

Proceedings of the Optimization and Applications - 13th International Conference, 2022

2021

Inexact model: a framework for optimization and variational inequalities.

Fedor S. Stonyakin, Alexander Tyurin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Artem Agafonov, Darina Dvinskikh, Mohammad S. Alkousa, Dmitry Pasechnyuk, Sergei Artamonov, Victoria V. Piskunova

Optim. Methods Softw., 2021

Convex optimization.

Evgeniya A. Vorontsova, Roland Hildebrand, Alexander V. Gasnikov, Fedor S. Stonyakin

CoRR, 2021

2019

Gradient Methods for Problems with Inexact Model of the Objective.

Fedor S. Stonyakin, Darina Dvinskikh, Pavel E. Dvurechensky, Alexey Kroshnin, Olesya Kuznetsova, Artem Agafonov, Alexander V. Gasnikov, Alexander Tyurin, César A. Uribe, Dmitry Pasechnyuk, Sergei Artamonov

Proceedings of the Mathematical Optimization Theory and Operations Research, 2019

On Some Methods for Strongly Convex Optimization Problems with One Functional Constraint.

Fedor S. Stonyakin, Mohammad S. Alkousa, Alexander A. Titov, Victoria V. Piskunova

Proceedings of the Mathematical Optimization Theory and Operations Research, 2019