1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.OC2024
On quasi-convex smooth optimization problems by a comparison oracle
A. V. Gasnikov, M. S. Alkousa, A. V. Lobanov +4
Frequently, when dealing with many machine learning models, optimization problems appear to be challenging due to a limited understanding of the constructions and characterizations…
math.OC2022★ 1 cited
Stopping Rules for Gradient Methods for Non-Convex Problems with Additive Noise in Gradient
Boris T. Polyak, Ilia A. Kuruzov, Fedor S. Stonyakin
We study the gradient method under the assumption that an additively inexact gradient is available for, generally speaking, non-convex problems. The non-convexity of the objective…