test_CholmodSupernodalLLT.py

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import numpy as np
from scipy.sparse import csc_matrix
 
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))
 
A = csc_matrix(A)
 
llt = eigenpy.CholmodSupernodalLLT(A)
 
assert llt.info() == eigenpy.ComputationInfo.Success
 
X = rng.random((dim, 20))
B = A.dot(X)
X_est = llt.solve(B)
assert eigenpy.is_approx(X, X_est)
assert eigenpy.is_approx(A.dot(X_est), B)
 
llt.analyzePattern(A)
llt.factorize(A)