Online Algorithm

Online algorithms address the challenge of making optimal decisions sequentially, without complete knowledge of future inputs. Current research focuses on improving algorithm performance through the integration of machine-learned predictions, developing robust algorithms for various settings (e.g., expanding graphs, correlated rewards, limited data retention), and analyzing the trade-off between worst-case and average-case performance. These advancements are significant for diverse applications, including resource allocation, network management, and online learning systems, by enabling more efficient and adaptable decision-making in dynamic environments.