def test_cholesky():
random.seed()
for i in range(10):
print(".", end="", flush= True)
size = random.randint(100, 200)
M = matgen.symmetricPositiveDefinite(size)
T = cholesky.completeCholesky(M)
if not numpy.allclose(M, numpy.dot(T, T.transpose())):
return False
return True
def testIncompleteCholeskyPrecision():
size = 100
nbOfPoints = 100
x, y = [], []
for i in numpy.linspace(0, size*(size-1), nbOfPoints):
x.append(i/(size*size))
M = matgen.symmetricSparsePositiveDefinite(size, i)
complete = cholesky.completeCholesky(M)
incomplete = cholesky.incompleteCholesky(M)
y.append(numpy.linalg.norm(complete-incomplete)/numpy.linalg.norm(complete))
plt.plot(x, y, marker='o')
plt.title("Relative error made by Cholesky incomplete factorization, in function of matrix density\n")
plt.ylabel("relative error on the norm of the matrix obtained")
plt.xlabel("density of the initial 100*100 matrix")
plt.show()
