Online Optimization

Online optimization focuses on developing algorithms that efficiently learn optimal strategies in dynamic environments where data arrives sequentially and the underlying problem may change over time. Current research emphasizes distributed and robust algorithms, often employing gradient-based methods, Reinforcement Learning, and evolutionary optimization techniques to handle non-convex losses, delayed feedback, and time-correlated data, with a focus on minimizing regret (the difference between the online algorithm's performance and the optimal offline solution). These advancements have significant implications for various fields, including communication systems, robotics, and resource allocation, by enabling adaptive and efficient control in complex, real-world scenarios.