Sublinear Time

Sublinear time algorithms aim to solve computational problems significantly faster than linear time, focusing on achieving approximate solutions with drastically reduced computational complexity. Current research emphasizes developing such algorithms for diverse applications, including machine learning (e.g., graph neural networks, clustering, online kernel regression), large language models (e.g., efficient attention mechanisms), and online optimization problems (e.g., constrained Markov decision processes). This focus stems from the need to handle massive datasets and complex models that are intractable with traditional linear-time approaches, impacting fields ranging from AI and data mining to theoretical computer science.