The Impact of Logarithmic Step Size on Stochastic Gradient Descent Optimization

Optimizing the efficiency of the stochastic gradient descent (SGD) algorithm is crucial for achieving superior performance in machine learning tasks. The choice of step size, also known as the learning rate, significantly influences the convergence of SGD. Recently, various step size strategies have been introduced to enhance the performance of SGD, but they often encounter challenges related to their probability distribution.

One of the key challenges in optimizing step size strategies is the distribution of probabilities assigned to the iterations. The conventional cosine step size method, although effective in practice, tends to assign very low probability distribution values to the final iterations. This limitation can hinder the algorithm’s convergence and impact its overall performance.

Addressing this challenge, a research team led by M. Soheil Shamaee presented a novel logarithmic step size approach in a study published in Frontiers of Computer Science. The new step size method demonstrates significant advantages, particularly in the final iterations, compared to the traditional cosine step size. By ensuring a higher probability of selection during these critical phases, the logarithmic step size outperforms its counterpart in terms of convergence and performance.

The numerical results obtained from experiments conducted on popular datasets like FashionMinst, CIFAR10, and CIFAR100 highlight the superiority of the logarithmic step size in optimizing SGD. Notably, when combined with a convolutional neural network (CNN) model, the new step size method showed a remarkable 0.9% increase in test accuracy for the CIFAR100 dataset. These findings underscore the efficiency and effectiveness of the logarithmic step size in challenging optimization scenarios.

The introduction of the logarithmic step size represents a significant advancement in optimizing the stochastic gradient descent algorithm. By addressing the distribution challenges faced by traditional step size strategies, the new approach offers improved convergence and performance, particularly in the critical final iterations. Future research and applications in machine learning are poised to benefit greatly from this innovative step size method.


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