Optimal Stopping

Optimal stopping addresses the problem of determining the best time to halt a stochastic process to maximize reward. Current research focuses on developing efficient algorithms, including those based on Gaussian processes, reinforcement learning, and adaptive optimization methods like AdaGrad, to solve optimal stopping problems in various contexts, often involving the analysis of convergence rates and the development of near-optimal solutions. These advancements are improving the ability to tackle complex problems across diverse fields, such as finance, operations management, and cybersecurity, where optimal decision-making under uncertainty is crucial. The development of theoretically sound and practically efficient algorithms for high-dimensional problems remains a key area of ongoing investigation.