Least Squares with Examples in Signal Processing 1
Ivan Selesnick · 2013
Ivan Selesnick March 7, 2013 NYU-Poly These notes address (approximate) solutions to linear equations by least squares. We deal with the ‘easy’ case wherein the system matrix is full rank. If the system matrix is rank deficient, then other methods are needed, e.g., QR decomposition, singular value decomposition, or the pseudo-inverse, [2, 3]. In these notes, least squares is illustrated by applying it to several basic problems in signal processing: