Stochastic Gradient Descent Revisited
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910864742809600 |
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| author | Louzi, Azar |
| author_facet | Louzi, Azar |
| contents | Stochastic gradient descent (SGD) has been a go-to algorithm for nonconvex stochastic optimization problems arising in machine learning. Its theory however often requires a strong framework to guarantee convergence properties. We hereby present a full scope convergence study of biased nonconvex SGD, including weak convergence, function-value convergence and global convergence, and also provide subsequent convergence rates and complexities, all under relatively mild conditions in comparison with literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06070 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stochastic Gradient Descent Revisited Louzi, Azar Optimization and Control Probability Machine Learning 90C15, 90C26, 90C60 Stochastic gradient descent (SGD) has been a go-to algorithm for nonconvex stochastic optimization problems arising in machine learning. Its theory however often requires a strong framework to guarantee convergence properties. We hereby present a full scope convergence study of biased nonconvex SGD, including weak convergence, function-value convergence and global convergence, and also provide subsequent convergence rates and complexities, all under relatively mild conditions in comparison with literature. |
| title | Stochastic Gradient Descent Revisited |
| topic | Optimization and Control Probability Machine Learning 90C15, 90C26, 90C60 |
| url | https://arxiv.org/abs/2412.06070 |