Sat Apr  2 20:35:54 2022

svd_truncated_test():
  Python version: 3.6.9
  Demonstrate the use of the truncated
  Singular Value Decomposition (SVD) for cases where
  the sizes of M and N are very different.

svd_truncated_u_test():
  M = 4
  N = 3

  Original matrix A:
[[0.64800996 0.05023028 0.57805973]
 [0.64642209 0.73844741 0.50911219]
 [0.76699332 0.93959538 0.92820986]
 [0.21087148 0.16653028 0.0285024 ]]

  Maximum error |A - U*S*V| =  4.440892098500626e-16

  Recomputed A = U * S * V:
[[0.64800996 0.05023028 0.57805973]
 [0.64642209 0.73844741 0.50911219]
 [0.76699332 0.93959538 0.92820986]
 [0.21087148 0.16653028 0.0285024 ]]

svd_truncated_v_test():
  M = 3
  N = 4

  Original matrix A:
[[0.98955246 0.54796986 0.49630102 0.50025594]
 [0.6823696  0.38034993 0.01107941 0.42481303]
 [0.10418632 0.2139493  0.9636949  0.51096435]]

  Maximum error |A - U*S*V| =  6.661338147750939e-16

  Recomputed A = U * S * V:
[[0.98955246 0.54796986 0.49630102 0.50025594]
 [0.6823696  0.38034993 0.01107941 0.42481303]
 [0.10418632 0.2139493  0.9636949  0.51096435]]

svd_truncated_test():
  Normal end of execution.
Sat Apr  2 20:35:54 2022