Incremental Gradient

Incremental gradient methods are optimization algorithms designed to efficiently solve large-scale machine learning problems by processing data in smaller, sequential batches. Current research focuses on improving the convergence guarantees of these methods, particularly for the last iterate (rather than the average iterate), and exploring optimal data ordering strategies to accelerate training. This work is significant because it addresses limitations in the theoretical understanding of widely used algorithms like stochastic gradient descent and its variants, leading to more efficient and robust machine learning models across various applications.