Single Loop

Single-loop algorithms represent a class of optimization methods aiming to solve complex problems, such as bilevel optimization and reinforcement learning, with increased efficiency by avoiding nested iterative structures. Current research focuses on developing and analyzing single-loop variants of established algorithms like actor-critic methods and interior-point methods, often incorporating techniques like stochastic gradient descent and variance reduction to improve convergence rates and sample complexity. These advancements are significant because they offer faster and more scalable solutions for a wide range of applications in machine learning, control systems, and other fields where computationally intensive nested optimization is common.