Inaccurate result when a vector is divided by a matrix

[scidam]

Ok,
if you try to execute the following code

import numpy as np
a = np.array([10, 10, 10])
b = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
a @ np.linalg.pinv(b)

you get

Output:
array([0.71428571, 1.42857143, 2.14285714])

np.linalg.pinv(x) returns Moore-Penrose inversion of a matrix x. x @ y denotes matrix product between x and y. Moore-Penrose inversion is used to obtain least squares solution for over-determined (or under-determined) linear systems. Octave knows: if you are trying to divide one matrix onto another, you are likely trying to solve system of linear equations. E.g.: Ax=b, then x = b / A; in Python you should do this: x = np.linalg.pinv(A) @ b.