Utilize the low-rank approximation method for a matrix and calculate the relative residual norm between each iteration of Ak and the given matrix A. \

low-rank approximation rule:

was gotten and how TO DO IN PYTHON)

Also, Calculate the relative residual norm R1. (PLEASE SHOW HOW TO DO IN PYTHON )

Now, we perform the rank-2 approximation (we cannot do more than that since this is a 2x2 matrix) and we obtain the new relative residual norm of r2. ( PLEASESHOW HOW IT WOUDLVE BEEN IN PYTHON )

A) Give the sigma vector, as calculated using the numpy.linalg.svd() function

B) Give the list of relative residual norms as described above (e.g. in the simple example I gave above, that would be reported as [r1, r2]).

C) EXplain what you found and if you expected the results based on what you know about SVD and matrix characteristics.

D) Show the Python code you used to solve this problem.

IMPORTANT INFO that may not be applicable but helpful.