Computing Matrix SVD Decomposition Using Different Methods

MATLAB simulation for computing matrix SVD decomposition through various approaches, utilizing SVD for solving least squares problems and implementing image compression

Low-Rank Representation Code Implementation with IADM-NNLS Algorithm

Application Context: IADM_NNLS (Inexact Alternating Direction Method for Nuclear Norm Regularized Least Squares) solves optimization problems where the regularization term uses the nuclear norm, such as low-rank representation algorithms. Key Techniques: Matrix rank is always less than or equal to the number of rows. From a definition perspective, for a set of vectors A, the rank represents the size of the maximum linearly independent subset. This implementation enhances rank computation accuracy through nuclear norm minimization.