4 papers
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
Aleksandr Lobanov, Alexander Gasnikov
This paper is devoted to the study of stochastic optimization problems under the generalized smoothness assumption. By considering the unbiased gradient oracle in Stochastic Gradie…
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…