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Vaidya’s Method for Convex Stochastic Optimization Problems in Small Dimension

Mathematical Notes, 2022-08, Vol.112 (1-2), p.183-190

2022

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Min-Max Optimization over Slowly Time-Varying Graphs

Doklady. Mathematics, 2023-12, Vol.108 (Suppl 2), p.S300-S309

2023

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Optimal Data Splitting in Distributed Optimization for Machine Learning

Doklady. Mathematics, 2023-12, Vol.108 (Suppl 2), p.S465-S475

2023

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Accelerated Methods for Saddle-Point Problem

Computational mathematics and mathematical physics, 2020-11, Vol.60 (11), p.1787-1809

2020

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Universal Method for Stochastic Composite Optimization Problems

Computational mathematics and mathematical physics, 2018, Vol.58 (1), p.48-64

2018

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Accelerated Meta-Algorithm for Convex Optimization Problems

Computational mathematics and mathematical physics, 2021, Vol.61 (1), p.17-28

2021

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Highly Smooth Zeroth-Order Methods for Solving Optimization Problems under the PL Condition

Computational mathematics and mathematical physics, 2024, Vol.64 (4), p.739-770

2024

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Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities

Doklady. Mathematics, 2023-12, Vol.108 (Suppl 2), p.S348-S359

2023

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Fast Gradient Descent for Convex Minimization Problems with an Oracle Producing a (δ, L)-Model of Function at the Requested Point

Computational mathematics and mathematical physics, 2019-07, Vol.59 (7), p.1085-1097

2019

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Solving Convex Min-Min Problems with Smoothness and Strong Convexity in One Group of Variables and Low Dimension in the Other

Automation and remote control, 2021-10, Vol.82 (10), p.1679-1691

2021

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Decentralized Conditional Gradient Method on Time-Varying Graphs

Programming and computer software, 2023-12, Vol.49 (6), p.505-512

2023

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Activations and Gradients Compression for Model-Parallel Training

Doklady. Mathematics, 2023-12, Vol.108 (Suppl 2), p.S272-S281

2023

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Gradient-free Federated Learning Methods with l1 and l2-randomization for Non-smooth Convex Stochastic Optimization Problems

Computational mathematics and mathematical physics, 2023-09, Vol.63 (9), p.1600-1653

2023

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Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality

Mathematical Notes, 2020-10, Vol.108 (3-4), p.511-522

2020

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Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions

Proceedings of the Steklov Institute of Mathematics, 2023-12, Vol.323 (Suppl 1), p.S1-S18

2023

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Numerical Methods for the Resource Allocation Problem in a Computer Network

Computational mathematics and mathematical physics, 2021-02, Vol.61 (2), p.297-328

2021

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Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints

Computational mathematics and mathematical physics, 2017-08, Vol.57 (8), p.1262-1276

2017

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An Adaptive Proximal Method for Variational Inequalities

Computational mathematics and mathematical physics, 2019-05, Vol.59 (5), p.836-841

2019

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Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator

Automation and remote control, 2019-08, Vol.80 (8), p.1487-1501

2019

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Accelerated Primal-Dual Gradient Descent with Linesearch for Convex, Nonconvex, and Nonsmooth Optimization Problems

Doklady. Mathematics, 2019-03, Vol.99 (2), p.125-128

2019

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A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization, and Coordinate Descent

Computational mathematics and mathematical physics, 2023-02, Vol.63 (2), p.147-174

2023

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A hypothesis about the rate of global convergence for optimal methods (Newtons type) in smooth convex optimization

Kompʹûternye issledovaniâ i modelirovanie (Online), 2018-06, Vol.10 (3), p.305-314

2018

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Accelerated Directional Search with Non-Euclidean Prox-Structure

Automation and remote control, 2019-04, Vol.80 (4), p.693-707

2019

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On Some Works of Boris Teodorovich Polyak on the Convergence of Gradient Methods and Their Development

Computational mathematics and mathematical physics, 2024, Vol.64 (4), p.635-675

2024

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Accelerated Gradient Sliding for Minimizing a Sum of Functions

Doklady. Mathematics, 2020-05, Vol.101 (3), p.244-246

2020

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Vaidya’s Method for Convex Stochastic Optimization Problems in Small Dimension

Min-Max Optimization over Slowly Time-Varying Graphs

Optimal Data Splitting in Distributed Optimization for Machine Learning

Accelerated Methods for Saddle-Point Problem

Universal Method for Stochastic Composite Optimization Problems

Accelerated Meta-Algorithm for Convex Optimization Problems

Highly Smooth Zeroth-Order Methods for Solving Optimization Problems under the PL Condition

Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities

Fast Gradient Descent for Convex Minimization Problems with an Oracle Producing a (δ, L)-Model of Function at the Requested Point

Solving Convex Min-Min Problems with Smoothness and Strong Convexity in One Group of Variables and Low Dimension in the Other

Decentralized Conditional Gradient Method on Time-Varying Graphs

Activations and Gradients Compression for Model-Parallel Training

Gradient-free Federated Learning Methods with l1 and l2-randomization for Non-smooth Convex Stochastic Optimization Problems

Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality

Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions

Numerical Methods for the Resource Allocation Problem in a Computer Network

Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints

An Adaptive Proximal Method for Variational Inequalities

Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator

Accelerated Primal-Dual Gradient Descent with Linesearch for Convex, Nonconvex, and Nonsmooth Optimization Problems

A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization, and Coordinate Descent

A hypothesis about the rate of global convergence for optimal methods (Newtons type) in smooth convex optimization

Accelerated Directional Search with Non-Euclidean Prox-Structure

On Some Works of Boris Teodorovich Polyak on the Convergence of Gradient Methods and Their Development

Accelerated Gradient Sliding for Minimizing a Sum of Functions

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