def test_solve_cholesky():
size = np.random.randint(5, 50)
A = np.random.randn(size, size)
b = np.random.randn(size)
# square values of A
A = A * A
# and solve
xexp, resid, rank, s = np.linalg.lstsq(A, b, rcond=None)
# chol solve
L = np.linalg.cholesky(A.T @ A)
w = solve_tri(L, A.T @ b)
x = solve_tri(L, w, transpose=True)
assert x == approx(xexp, abs=1.0e-3)
def test_solve_utri_transpose():
size = np.random.randint(5, 50)
Af = np.random.randn(size, size)
b = np.random.randn(size)
A = np.triu(Af)
x = solve_tri(A, b, True, lower=False)
assert len(x) == size
xexp = sla.solve_triangular(A.T, b, lower=True)
assert x == approx(xexp, rel=1.0e-6)
def test_solve_utri():
for i in range(10):
size = np.random.randint(5, 50)
Af = np.random.randn(size, size)
b = np.random.randn(size)
A = np.triu(Af)
x = solve_tri(A, b, lower=False)
assert len(x) == size
xexp = sla.solve_triangular(A, b, lower=False)
assert x == approx(xexp, rel=1.0e-6)
def test_solve_ltri():
size = np.random.randint(5, 50)
Af = np.random.randn(size, size)
b = np.random.randn(size)
A = np.tril(Af)
x = solve_tri(A, b)
assert len(x) == size
xexp = sla.solve_triangular(A, b, lower=True)
assert x == approx(xexp, rel=1.0e-6)
