Adaptive Step Size

Adaptive step size methods aim to automatically adjust the learning rate in optimization algorithms, improving efficiency and reducing the need for manual hyperparameter tuning. Current research focuses on extending these methods to complex settings like federated learning, bi-level optimization, and compressed stochastic gradient descent, often employing variations of Polyak step size or other adaptive strategies within algorithms such as Adam and SGD. This research is significant because it enhances the performance and robustness of optimization algorithms across various machine learning tasks, particularly in large-scale and distributed environments, leading to faster convergence and improved model accuracy.