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arXiv · 2411.15795

Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization

Corrado Coppola·Lorenzo Papa·Irene Amerini·Laura Palagi
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Abstract

Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these methods come with limitations, such as increased memory requirements for elements like moving averages and a poorly understood convergence theory. To overcome these challenges, we introduce F-CMA, a Fast-Controlled Mini-batch Algorithm with a random reshuffling method featuring a sufficient decrease condition and a line-search procedure to ensure loss reduction per epoch, along with its deterministic proof of global convergence to a stationary point. To evaluate the F-CMA, we integrate it into conventional training protocols for classification tasks involving both convolutional neural networks and vision transformer models, allowing for a direct comparison with popular optimizers. Computational tests show significant improvements, including a decrease in the overall training time by up to 68%, an increase in per-epoch efficiency by up to 20%, and in model accuracy by up to 5%.

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Corrado Coppola, Lorenzo Papa, Irene Amerini, Laura Palagi. 2024-12-16. Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization. https://arxiv.org/abs/2411.15795

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