Faster Convergence Speed

Faster convergence speed in optimization algorithms is a crucial research area aiming to reduce the computational time and resources needed for training machine learning models. Current efforts focus on improving existing algorithms like stochastic quasi-Newton methods and gradient descent, often incorporating techniques such as gradient clipping, variance reduction, and adaptive momentum strategies, as well as exploring novel approaches like deep unfolding and multi-agent reinforcement learning. These advancements are significant for various applications, including federated learning and congestion control, where efficient training is critical for scalability and real-time performance. The resulting improvements in training efficiency translate to reduced energy consumption and faster deployment of machine learning solutions.