Non Adaptive

Non-adaptive algorithms are computational methods that make decisions without adjusting their strategy based on previously observed data. Current research focuses on understanding their limitations compared to adaptive counterparts in various contexts, including optimization (e.g., analyzing the performance of Adam optimizer relative to SGD), function approximation, and group testing. These studies aim to characterize the inherent trade-offs between adaptive and non-adaptive approaches, revealing fundamental limits on performance and efficiency in specific problem domains. This work has implications for algorithm design and resource allocation in diverse fields, from machine learning to data analysis.