Kronecker Regression

Kronecker regression tackles the efficient solution of least squares problems where the design matrix is a Kronecker product of smaller matrices. Research focuses on developing subquadratic-time algorithms, often leveraging techniques like leverage score sampling and iterative methods, to overcome the computational challenges posed by the exponential growth of the design matrix with increasing factors. This improved efficiency is particularly impactful for applications such as tensor decomposition, accelerating algorithms like alternating least squares and enabling the analysis of larger datasets in fields like image processing and machine learning.