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arXiv · 2404.01714

Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning

Jiawu Tian·Liwei Xu·Xiaowei Zhang·Yongqi Li
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Abstract

Training deep neural networks is a challenging task. In order to speed up training and enhance the performance of deep neural networks, we rectify the vanilla conjugate gradient as conjugate-gradient-like and incorporate it into the generic Adam, and thus propose a new optimization algorithm named CG-like-Adam for deep learning. Specifically, both the first-order and the second-order moment estimation of generic Adam are replaced by the conjugate-gradient-like. Convergence analysis handles the cases where the exponential moving average coefficient of the first-order moment estimation is constant and the first-order moment estimation is unbiased. Numerical experiments show the superiority of the proposed algorithm based on the CIFAR10/100 dataset.

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Jiawu Tian, Liwei Xu, Xiaowei Zhang, Yongqi Li. 2024-04-02. Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning. https://arxiv.org/abs/2404.01714

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