Compare different SG-Schemes based on large least square problems

Ramkrishna Acharya

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arXiv · 2503.01507

Compare different SG-Schemes based on large least square problems

Ramkrishna Acharya

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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.

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Ramkrishna Acharya. 2025-03-03. Compare different SG-Schemes based on large least square problems. https://arxiv.org/abs/2503.01507

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