Compare different SG-Schemes based on large least square problems
Machine Learning
2025-03-05 v2 Artificial Intelligence
Abstract
This study reviews 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 model parameters. 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.
Cite
@article{arxiv.2503.01507,
title = {Compare different SG-Schemes based on large least square problems},
author = {Ramkrishna Acharya},
journal= {arXiv preprint arXiv:2503.01507},
year = {2025}
}