New logarithmic step size for stochastic gradient descent

• URL: http://arxiv.org/abs/2404.01257v1
• Date: Mon, 1 Apr 2024 17:25:27 GMT
• Title: New logarithmic step size for stochastic gradient descent
• Authors: M. Soheil Shamaee, S. Fathi Hafshejani, Z. Saeidian,
• Abstract summary: We propose a novel warm restart technique using a new logarithmic step size for the gradient descent (SGD) Our results show that the new logarithmic step improves test accuracy by 92% for the CIFAR100 dataset when we utilize a neuralal network (CNN) model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an $O(\frac{1}{\sqrt{T}})$ convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the ~FashionMinst,~ CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by $0.9\%$ for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.

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