Is there a standard solution for Gauss elimination in Python?

Question

Is there somewhere in the cosmos of scipy/numpy/... a standard method for Gauss-elimination of a matrix?

One finds many snippets via google, but I woul

Answer 1

One function that can be worth checking is _remove_redundancy, if you wish to remove repeated or redundant equations:

import numpy as np
import scipy.optimize

a = np.array([[1.,1.,1.,1.],
              [0.,0.,0.,1.],
              [0.,0.,0.,2.],
              [0.,0.,0.,3.]])
print(scipy.optimize._remove_redundancy._remove_redundancy(a, np.zeros_like(a[:, 0]))[0])

which gives:

[[1. 1. 1. 1.]
 [0. 0. 0. 3.]]

As a note to @flonk answer, using a LU decomposition might not always give the desired reduced row matrix. Example:

import numpy as np
import scipy.linalg

a = np.array([[1.,1.,1.,1.],
              [0.,0.,0.,1.],
              [0.,0.,0.,2.],
              [0.,0.,0.,3.]])

_,_, u = scipy.linalg.lu(a)
print(u)

gives the same matrix:

[[1. 1. 1. 1.]
 [0. 0. 0. 1.]
 [0. 0. 0. 2.]
 [0. 0. 0. 3.]]

even though the last 3 rows are linearly dependent.