Momentum Method

Momentum methods are optimization algorithms that accelerate the convergence of gradient descent by incorporating information from previous iterations, improving efficiency in training machine learning models. Current research focuses on analyzing the convergence rates and stability of momentum methods under various conditions, including biased gradient estimations, non-convex optimization landscapes, and distributed computing environments, with specific attention given to algorithms like Heavy Ball, Nesterov's Accelerated Gradient, and their stochastic variants. These investigations are crucial for understanding and improving the performance of machine learning models across diverse applications, particularly in deep learning where large-scale optimization is paramount. The resulting theoretical insights and improved algorithms directly impact the speed and reliability of training complex models.