Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions

1 April 2020

Papers citing "Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions"

8 / 8 papers shown

Title
Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm Jinwei Zhao Marco Gori Alessandro Betti S. Melacci Hongtao Zhang Jiedong Liu Xinhong Hei 28 0 0 10 Sep 2024
High Probability Guarantees for Random Reshuffling Hengxu Yu Xiao Li 37 2 0 20 Nov 2023
A Novel Gradient Methodology with Economical Objective Function Evaluations for Data Science Applications Christian Varner Vivak Patel 16 2 0 19 Sep 2023
Distributed Stochastic Optimization under a General Variance Condition Kun-Yen Huang Xiao Li Shin-Yi Pu FedML 37 5 0 30 Jan 2023
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 34 6 0 13 Dec 2021
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 23 50 0 04 May 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 32 13 0 01 Apr 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 133 1,198 0 16 Aug 2016