numpy.linalg.lstsq gives empty residual

python version: 2.7.6 numpy version: 1.8.0

Here is a short example:

import numpy as np
from numpy.linalg import lstsq

a = np.array([[1, 0], [0, 1]]);
b = np.array([1,2]);

print(lstsq(a,b));

Since a is 2x2 matrix of rank 2, I expect the residual to be 0 instead of empty list according to the doc (http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html). But here is what I get:

(array([ 1.,  2.]), array([], dtype=float64), 2, array([ 1.,  1.]))

[eric-wieser]

Agreed, this behavior is ridiculous, and makes #8720 impossible (https://github.com/numpy/numpy/issues/8720#issuecomment-383198263)

[opensdh]

It may be far too late to change anything here (especially since it has now been documented as implemented for so long), but it’s not helpful to have the shape of a return value depend on the values of the input. If I provide a calculated a that might be rank-deficient (and might or might not be judged as such for numerical reasons), why should I have to examine the returned rank to find out whether I got residuals or not? Making them 0 (as was once documented) would make more sense there; anyone who “statically” knows that their system is never overdetermined would just ignore it anyway.