Momentum Acceleration

Momentum acceleration techniques, primarily inspired by Nesterov's Accelerated Gradient and Polyak's Heavy Ball methods, aim to improve the convergence speed of optimization algorithms, particularly in challenging scenarios like reinforcement learning and solving inverse problems. Current research focuses on adapting these methods to various contexts, including constrained optimization problems, Lie group optimization, and decentralized learning settings, often employing deep unrolling networks or analyzing their convergence rates under different policy parameterizations. These advancements hold significant promise for accelerating training in deep learning models and enhancing the efficiency of solving complex optimization problems across diverse scientific and engineering applications.