Asymptotic Optimality

Asymptotic optimality in various fields focuses on designing algorithms and procedures that achieve the best possible performance as the problem size grows infinitely large. Current research emphasizes developing algorithms with provable optimality guarantees, often focusing on models like restless bandits, and employing techniques such as simulation-based approaches, elimination methods, and information-theoretic metrics to analyze and improve efficiency. These advancements have significant implications for diverse applications, including online learning, best-arm identification in multi-armed bandits, and efficient optimization in large-scale systems. The pursuit of asymptotic optimality drives the development of more efficient and effective algorithms across numerous scientific disciplines.