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.

翻译：学习率调度器是在模型训练过程中用于调整搜索步长的一组预定义指令。本文提出了一种新的对数方法，通过随机梯度下降实现步长的剧烈重启。循环对数退火以更具侵略性的方式实施重启模式，可能允许在在线凸优化框架中使用更贪婪的算法。该算法在CIFAR-10图像数据集上进行了测试，结果表明其在大型Transformer增强残差神经网络上的表现与余弦退火方法类似。未来的实验将包括在生成对抗网络中测试该调度器，并通过更多实验寻找其最佳参数。