Coverage for ase / _4 / symopt / test_cholesky_deriv.py: 100.00%

1import numpy as np 

2import pytest 

3 

4from ase._4.symopt.relax import chol_derivative 

5 

6pytestmark = pytest.mark.calculator_lite 

7 

8 

9def numerical_chol_derivative(A, dA, eps=1e-10): 

10 dA = (dA + dA.T) / 2 

11 L = np.linalg.cholesky(A - eps * dA) 

12 Lp = np.linalg.cholesky(A + eps * dA) 

13 return (Lp - L) / (2 * eps) 

14 

15 

16def test_chol_derivative(): 

17 for i in range(10): 

18 # Create a random positive definite matrix 

19 A = np.random.rand(3, 3) 

20 A = A @ A.T 

21 

22 # Create a random perturbation 

23 dA = np.random.rand(3, 3) 

24 dA = dA - dA.T 

25 numdL = numerical_chol_derivative(A, dA) 

26 print('numdL', numdL) 

27 analdL = chol_derivative(A, dA) 

28 print('analdL', analdL) 

29 

30 assert np.allclose(numdL, analdL, atol=1e-4)