Least Square

Least squares is a fundamental statistical method aiming to find the best-fitting line or curve to a dataset by minimizing the sum of squared errors. Current research focuses on improving the efficiency and robustness of least squares methods, particularly for high-dimensional data, through techniques like sketching, regularization (including ridge and lasso regression), and iterative reweighting. These advancements are crucial for addressing challenges in diverse fields such as machine learning, signal processing, and system identification, enabling more efficient and accurate solutions to large-scale problems.