Fixed Budget

Fixed-budget problems address the challenge of optimizing performance within a predetermined resource limit, typically time or computational budget. Current research focuses on developing efficient algorithms for various tasks, including best arm identification in multi-armed bandit problems and optimal resource allocation in complex systems like robot task assignment, often employing techniques like successive rejection, reinforcement learning, and dynamic allocation strategies. These advancements are crucial for improving the efficiency of deep learning, optimizing computationally expensive black-box functions, and enabling real-time decision-making in resource-constrained environments. The resulting algorithms and theoretical frameworks have broad implications across diverse fields, from AI and machine learning to operations research and robotics.