def test_eigh_Z():
## Test the Cholesky decomposition of a double precision complex matrix (use the
## non-default, lower half)
ns = 342
gA = np.random.standard_normal((ns, ns)).astype(np.float64)
gA = gA + 1.0J * np.random.standard_normal((ns, ns)).astype(np.float64)
gA = np.dot(gA, gA.T.conj()) # Make positive definite
gA = np.asfortranarray(gA)
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
dU = rt.cholesky(dA, lower=True)
gUd = dU.to_global_array(rank=0)
if rank == 0:
gUn = la.cholesky(gA, lower=True)
assert allclose(gUn, gUd)
def test_cholesky_D():
## Test the Cholesky decomposition of a double precision matrix (use the
## default, upper half)
ns = 317
gA = np.random.standard_normal((ns, ns)).astype(np.float64)
gA = np.dot(gA, gA.T) # Make positive definite
gA = np.asfortranarray(gA)
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
dU = rt.cholesky(dA)
gUd = dU.to_global_array(rank=0)
if rank == 0:
gUn = la.cholesky(gA)
print(gUn)
print(gUd)
assert allclose(gUn, gUd)
