16 citations · 36 across the 20 of their papers we have counts for
7 papers · 1 filter
Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under -Smoothness
Aleksandr Lobanov, Alexander Gasnikov, Eduard Gorbunov +1
The gradient descent (GD) method -- is a fundamental and likely the most popular optimization algorithm in machine learning (ML), with a history traced back to a paper in 1847 (Cau…
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
Georgii Bychkov, Darina Dvinskikh, Anastasia Antsiferova +2
We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversaria…
Nesterov's method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square
Boris Chervonenkis, Andrei Krasnov, Alexander Gasnikov +1
The challenges of black box optimization arise due to imprecise responses and limited output information. This article describes new results on optimizing multivariable functions u…
Improved Iteration Complexity in Black-Box Optimization Problems under Higher Order Smoothness Function Condition
Aleksandr Lobanov
This paper is devoted to the study (common in many applications) of the black-box optimization problem, where the black-box represents a gradient-free oracle …
Gradient-free algorithm for saddle point problems under overparametrization
Ekaterina Statkevich, Sofiya Bondar, Darina Dvinskikh +2
This paper focuses on solving a stochastic saddle point problem (SPP) under an overparameterized regime for the case, when the gradient computation is impractical. As an intermedia…
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
Aleksandr Lobanov, Alexander Gasnikov, Andrei Krasnov
Frequently, the burgeoning field of black-box optimization encounters challenges due to a limited understanding of the mechanisms of the objective function. To address such problem…
Median Clipping for Zeroth-order Non-Smooth Convex Optimization and Multi-Armed Bandit Problem with Heavy-tailed Symmetric Noise
Nikita Kornilov, Yuriy Dorn, Aleksandr Lobanov +5
In this paper, we consider non-smooth convex optimization with a zeroth-order oracle corrupted by symmetric stochastic noise. Unlike the existing high-probability results requiring…