Accelerated Gradient Descent

Accelerated gradient descent (AGD) methods aim to improve the speed and efficiency of finding optimal solutions in various optimization problems, particularly prevalent in machine learning and related fields. Current research focuses on enhancing AGD's performance through techniques like preconditioning, adaptive learning rates, and momentum, as well as exploring its application in diverse contexts such as constrained optimization, distributed systems, and deep neural network training. These advancements are significant because they lead to faster training of complex models, improved generalization performance, and more efficient solutions for a wide range of scientific and engineering applications.