Wed Oct  8 08:57:49 2025

svd_truncated_test():
  python version: 3.10.12
  numpy version:  1.26.4
  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:
[[0.90101438 0.28563376 0.07889994]
 [0.43304785 0.53244186 0.36680798]
 [0.30872057 0.03247288 0.45752286]
 [0.53225302 0.04594687 0.85742615]]

  Maximum error |A - U*S*V| =  7.494005416219807e-16

  Recomputed A = U * S * V:
[[0.90101438 0.28563376 0.07889994]
 [0.43304785 0.53244186 0.36680798]
 [0.30872057 0.03247288 0.45752286]
 [0.53225302 0.04594687 0.85742615]]

svd_truncated_v_test():
  M = 3
  N = 4

  Original matrix A:
[[0.48483093 0.29702868 0.98311609 0.89788261]
 [0.131214   0.04697484 0.41442448 0.86654716]
 [0.12805106 0.68321105 0.86515709 0.74725149]]

  Maximum error |A - U*S*V| =  5.551115123125783e-16

  Recomputed A = U * S * V:
[[0.48483093 0.29702868 0.98311609 0.89788261]
 [0.131214   0.04697484 0.41442448 0.86654716]
 [0.12805106 0.68321105 0.86515709 0.74725149]]

svd_truncated_test():
  Normal end of execution.
Wed Oct  8 08:57:49 2025