Budget Constraint

Budget constraint optimization focuses on making optimal decisions under limited resources, aiming to maximize utility or performance while adhering to predefined resource limits. Current research emphasizes developing efficient algorithms, such as Thompson Sampling variations, end-to-end models incorporating integer linear programming, and reinforcement learning approaches, to solve diverse problems ranging from online advertising and sensor placement to multi-robot coordination and federated learning. These advancements have significant implications for resource allocation in various fields, improving efficiency and decision-making in applications with limited budgets, from online platforms to resource-constrained robotics.