  A learning rate scheduler is a predefined set of instructions for varying
search stepsizes during model training processes. This paper introduces a new
logarithmic method using harsh restarting of step sizes through stochastic
gradient descent. Cyclical log annealing implements the restart pattern more
aggressively to maybe allow the usage of more greedy algorithms on the online
convex optimization framework. The algorithm was tested on the CIFAR-10 image
datasets, and seemed to perform analogously with cosine annealing on large
transformer-enhanced residual neural networks. Future experiments would involve
testing the scheduler in generative adversarial networks and finding the best
parameters for the scheduler with more experiments.
