Matrix Sketching

Matrix sketching is a technique for approximating large matrices with smaller, computationally tractable representations, primarily aiming to accelerate computations and reduce memory requirements in various applications. Current research focuses on optimizing sketching algorithms, such as Frequent Directions and CountSketch, to achieve optimal space bounds and minimize error in tasks like least squares regression, linear system solving, and semidefinite programming, often exploring both deterministic and randomized approaches. These advancements have significant implications for large-scale data analysis, machine learning, and scientific computing, enabling faster and more efficient solutions for problems previously intractable due to data size.