Stochastic Gradient Descent Revisited

Azar Louzi

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arXiv · 2412.06070

Stochastic Gradient Descent Revisited

Azar Louzi

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Abstract

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.

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Azar Louzi. 2024-12-08. Stochastic Gradient Descent Revisited. https://arxiv.org/abs/2412.06070

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