3 citations · 10 across the 19 of their papers we have counts for
6 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…
OPTAMI: Global Superlinear Convergence of High-order Methods
Dmitry Kamzolov, Dmitry Pasechnyuk, Artem Agafonov +2
Second-order methods for convex optimization outperform first-order methods in terms of theoretical iteration convergence, achieving rates up to for highly-smooth funct…
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…
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…