test_LDLT.py

Go to the documentation of this file.

import numpy as np
 
import eigenpy
 
dim = 100
rng = np.random.default_rng()
 
A = rng.random((dim, dim))
A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
ldlt = eigenpy.LDLT(A)
 
L = ldlt.matrixL()
D = ldlt.vectorD()
P = ldlt.transpositionsP()
 
assert eigenpy.is_approx(
    np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))), A
)
 
X = rng.random((dim, 20))
B = A.dot(X)
X_est = ldlt.solve(B)
assert eigenpy.is_approx(X, X_est)
assert eigenpy.is_approx(A.dot(X_est), B)