Gradient Based Algorithm

Gradient-based algorithms are fundamental to optimizing complex functions in machine learning and other fields, aiming to efficiently find minima or saddle points. Current research focuses on extending these methods to handle challenging scenarios like bilevel optimization (nested optimization problems) and non-convex objectives, often employing techniques such as Moreau envelopes and adaptive subspace searches to improve efficiency and convergence guarantees. These advancements are crucial for tackling increasingly complex machine learning tasks, including hyperparameter optimization, neural architecture search, and robust model training, leading to improved performance and efficiency in various applications.