Cost Constraint

Cost constraint optimization focuses on maximizing objectives (e.g., ad conversions, feature selection utility) while adhering to budgetary limitations. Current research emphasizes online learning algorithms, including primal-dual methods and reinforcement learning approaches, often incorporating techniques like shadow features or curriculum learning to improve efficiency and constraint satisfaction in dynamic environments. These advancements are crucial for diverse applications, from resource-constrained decision-making in medicine and advertising to efficient vehicle routing and dynamic pricing strategies, improving both theoretical understanding and practical performance.