Compare different SG-Schemes based on large least square problems
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929740287311872 |
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| author | Acharya, Ramkrishna |
| author_facet | Acharya, Ramkrishna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compare different SG-Schemes based on large least square problems Acharya, Ramkrishna Machine Learning Artificial Intelligence 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. |
| title | Compare different SG-Schemes based on large least square problems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01507 |