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

• URL: http://arxiv.org/abs/2411.15795v1
• Date: Sun, 24 Nov 2024 11:46:47 GMT
• Title: Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization
• Authors: Corrado Coppola, Lorenzo Papa, Irene Amerini, Laura Palagi,
• Abstract summary: 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. Tests show significant improvements, including a decrease in the overall training time by 68%, an increase in per-epoch efficiency by up to 20%, and in model accuracy by up to 5%.
• Score: 1.6749379740049926
• License:
• 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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