Online Allocation

Online allocation addresses the problem of dynamically assigning resources to arriving agents, aiming to optimize overall utility or revenue under various constraints. Current research focuses on developing algorithms that are competitive even with limited information about agent preferences or resource costs, often employing techniques like primal-dual methods, learning-augmented approaches, and adaptive re-solving strategies to handle uncertainty and achieve near-optimal performance. These advancements are crucial for applications ranging from efficient resource management in data centers and digital health to fair allocation of societal resources and improved performance in online advertising and financial markets. The field is also actively exploring the incorporation of fairness constraints and the impact of strategic agents on allocation outcomes.