Randomized Algorithm

Randomized algorithms leverage stochasticity to solve computational problems, often achieving superior performance or efficiency compared to deterministic counterparts, particularly in adversarial or high-dimensional settings. Current research focuses on developing and analyzing novel randomized algorithms for optimization problems (including those with constraints), machine learning tasks (like feature selection and matrix factorization), and active learning scenarios, often employing techniques like sketching, variance reduction, and Bayesian optimization to improve efficiency and accuracy. These advancements are significant because they enable the solution of complex problems previously intractable due to computational limitations, impacting fields ranging from data analysis and machine learning to robotics and scientific computing.