16 citations · 36 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov +5
In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has…
The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
Aleksandr Lobanov, Nail Bashirov, Alexander Gasnikov
In this paper, we study the standard formulation of an optimization problem when the computation of gradient is not available. Such a problem can be classified as a "black box" opt…
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
Aleksandr Lobanov, Alexander Gasnikov
This paper is devoted to solving a convex stochastic optimization problem in a overparameterization setup for the case where the original gradient computation is not available, but…
Non-Smooth Setting of Stochastic Decentralized Convex Optimization Problem Over Time-Varying Graphs
Aleksandr Lobanov, Andrew Veprikov, Georgiy Konin +3
Distributed optimization has a rich history. It has demonstrated its effectiveness in many machine learning applications, etc. In this paper we study a subclass of distributed opti…
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…
Stochastic Adversarial Noise in the "Black Box" Optimization Problem
Aleksandr Lobanov
This paper is devoted to the study of the solution of a stochastic convex black box optimization problem. Where the black box problem means that the gradient-free oracle only retur…
Zero-Order Stochastic Conditional Gradient Sliding Method for Non-smooth Convex Optimization
Aleksandr Lobanov, Anton Anikin, Alexander Gasnikov +2
The conditional gradient idea proposed by Marguerite Frank and Philip Wolfe in 1956 was so well received by the community that new algorithms (also called Frank--Wolfe type algorit…