Adaptive Gradient Method

Adaptive gradient methods are optimization algorithms that dynamically adjust learning rates during training, aiming to accelerate convergence and improve performance compared to methods with fixed learning rates. Current research focuses on analyzing the convergence properties of algorithms like AdaGrad and Adam under various assumptions about the objective function (e.g., smoothness, convexity) and noise conditions, particularly in large-batch and federated learning settings. These analyses are crucial for understanding the strengths and limitations of adaptive methods, ultimately leading to more robust and efficient training of complex models in diverse applications such as deep learning and large language models.