Armijo Line Search

Armijo line search is an optimization technique that automatically adjusts the learning rate in gradient descent methods, eliminating the need for manual tuning and potentially improving performance. Recent research focuses on enhancing Armijo line search for large-scale neural network training, particularly with Transformer and CNN architectures, by incorporating momentum and addressing issues related to sharpness and stability. These improvements demonstrate superior performance compared to traditional methods with fixed learning rates across various datasets and significantly reduce the computational cost, particularly for large batch sizes. This work contributes to more efficient and robust training of deep learning models, impacting both theoretical understanding of optimization and practical applications.