Year: 2025
Type: preprint
Abstract: This study reviews some of the popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning play a crucial role in finding better parameters of a model. Hence this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.
Source: arXiv (Cornell University)
Author: Acharya, Ramkrishna
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415339035
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