The difference of pseudo-inverse between SciPy and Numpy

Question

I found that there\'re two versions of pinv() function, which calculates the pseudo-inverse of a matrix in Scipy and numpy, the docume

Answer 1

I can't speak as to why there are implementations in both scipy and numpy, but I can explain why the behaviour is different.

numpy.linalg.pinv approximates the Moore-Penrose psuedo inverse using an SVD (the lapack method dgesdd to be precise), whereas scipy.linalg.pinv solves a model linear system in the least squares sense to approximate the pseudo inverse (using dgelss). This is why their performance is different. I would expect the overall accuracy of the resulting pseudo inverse estimates to be somewhat different as well.

You might find that scipy.linalg.pinv2 performs more similarly to numpy.linalg.pinv, as it too uses an SVD method, rather than a least sqaures approximation.