Asymptotic Performance

Asymptotic performance in machine learning and optimization focuses on analyzing the long-term behavior of algorithms, aiming to understand their ultimate capabilities as computational resources become unlimited. Current research investigates this through various lenses, including stochastic approximation methods, ensemble techniques like those used in reinforcement learning (e.g., REDQ, AQE), and the analysis of algorithms under different data regimes (e.g., adversarial vs. stochastic). Understanding asymptotic performance is crucial for designing efficient and robust algorithms, impacting fields ranging from adaptive control and smart grid management to distributed learning and optimization problems.