Online Primal Dual

Online primal-dual methods are optimization techniques addressing problems where data arrives sequentially, requiring immediate decisions under uncertainty and often subject to constraints. Current research focuses on extending these methods to handle dynamic systems, such as dynamic pricing and image processing, and distributed settings with communication constraints, employing algorithms that balance optimality with computational efficiency. These advancements are significant for various applications, including resource allocation, dynamic imaging, and online advertising, by providing efficient and robust solutions to complex real-time decision-making problems under uncertainty.