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A variable metric proximal stochastic gradient method: An application to classification problems
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DOI：10.1016/j.ejco.2024.100088.png)
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Abstract
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• Supervised classification problems are ubiquitous in several scientific fields. • Proximal stochastic gradient algorithms are the gold standard to solve classification problems. • Variable metric strategies help to control the variance of the stochastic gradients. • Non-monotone line search procedures allow to automatically adjust the learning rate.
Keywords:
Variable metric
Stochastic optimization
Classification problem
Deep learning
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