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I'm currently using the modified Gram-Schmidt algorithm to compute the QR decomposition of a matrix A (m x n). My current problem is that I need the full decomposition Q (m x m) instead of the thin one Q (m x n). Can somebody help me, what do I have to add to the algorithm to compute the full QR decomposition?.

import numpy as np

def gs_m(A):

    m,n= A.shape
    A= A.copy()
    Q= np.zeros((m,n))
    R= np.zeros((n,n))

    for k in range(n):

        R[k,k]= np.linalg.norm(A[:,k:k+1].reshape(-1),2)
        Q[:,k:k+1]= A[:,k:k+1]/R[k,k]
        R[k:k+1,k+1:n+1]= np.dot( Q[:,k:k+1].T, A[:,k+1:n+1] )
        A[:,k+1:n+1]= A[:, k+1:n+1] - np.dot( Q[:,k:k+1], R[k:k+1,k+1:n+1])


     return Q, R
share|improve this question
    
Are you asking how to extend $Q$ so it is orthogonal? One possibility is after the above, run the modified GS algorithm on the basis vectors $e_1,...e_n$ and discard those that are already in the span. –  copper.hat Oct 3 '12 at 18:09
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