def test_eigsh_valid_init_operator_with_shape(dtype):
np.random.seed(100)
backend = symmetric_backend.SymmetricBackend()
np_backend = numpy_backend.NumPyBackend()
D = 16
index = Index(U1Charge.random(D, -1, 1), True)
indices = [index, index.copy().flip_flow()]
a = BlockSparseTensor.random(indices, dtype=dtype)
H = a + a.T.conj()
def mv(vec, mat):
return mat @ vec
init = BlockSparseTensor.random([index], dtype=dtype)
# note: this will only find eigenvalues in the charge (0,0)
# block of H because `init` only has non-zero values there.
# To find eigen values in other sectors we need to support non-zero
# divergence for block-sparse tensors
eta1, U1 = backend.eigsh_lanczos(mv, [H], init)
eta2, U2 = np_backend.eigsh_lanczos(mv, [H.todense()], init.todense())
v1 = np.reshape(U1[0].todense(), (D))
v1 = v1 / sum(v1)
v1 /= np.linalg.norm(v1)
v2 = np.reshape(U2[0], (D))
v2 = v2 / sum(v2)
v2[np.abs(v2) < 1E-12] = 0.0
v2 /= np.linalg.norm(v2)
np.testing.assert_allclose(eta1[0], min(eta2))
np.testing.assert_allclose(v1, v2)
def test_eigsh_lanczos_sanity_check_2(dtype):
np.random.seed(10)
D = 16
backend = symmetric_backend.SymmetricBackend()
index = Index(U1Charge.random(D, 0, 0), True)
indices = [index, index.copy().flip_flow()]
H = BlockSparseTensor.random(indices, dtype=dtype)
H = H + H.conj().T
def mv(x, mat):
return mat @ x
eta1, U1 = backend.eigsh_lanczos(mv, [H],
shape=(H.sparse_shape[1].flip_flow(), ),
dtype=dtype)
eta2, U2 = np.linalg.eigh(H.todense())
v1 = np.reshape(U1[0].todense(), (D))
v1 = v1 / sum(v1)
v2 = U2[:, 0]
v2 = v2 / sum(v2)
np.testing.assert_allclose(eta1[0], min(eta2))
np.testing.assert_allclose(v1, v2)
def test_eigsh_lanczos_reorthogonalize_sanity_check(dtype, numeig):
np.random.seed(10)
D = 24
backend = symmetric_backend.SymmetricBackend()
index = Index(U1Charge.random(D, 0, 0), True)
indices = [index, index.copy().flip_flow()]
H = BlockSparseTensor.random(indices, dtype=dtype)
H = H + H.conj().T
def mv(x, mat):
return mat @ x
eta1, U1 = backend.eigsh_lanczos(mv, [H],
shape=(H.sparse_shape[1].flip_flow(), ),
dtype=dtype,
numeig=numeig,
num_krylov_vecs=D,
reorthogonalize=True,
ndiag=1,
tol=10**(-12),
delta=10**(-12))
eta2, U2 = np.linalg.eigh(H.todense())
np.testing.assert_allclose(eta1[0:numeig], eta2[0:numeig])
for n in range(numeig):
v2 = U2[:, n]
v2 /= np.sum(v2) #fix phases
v1 = np.reshape(U1[n].todense(), (D))
v1 /= np.sum(v1)
np.testing.assert_allclose(v1, v2, rtol=10**(-5), atol=10**(-5))
def test_eigs_raises():
np.random.seed(10)
dtype = np.float64
backend = symmetric_backend.SymmetricBackend()
D = 16
index = Index(U1Charge.random(D, 0, 0), True)
indices = [index, index.copy().flip_flow()]
H = BlockSparseTensor.random(indices, dtype=dtype)
init = BlockSparseTensor.random([index], dtype=dtype)
with pytest.raises(ValueError,
match='which = SI is currently not supported.'):
backend.eigs(lambda x: x, [H], initial_state=init, which='SI')
with pytest.raises(ValueError,
match='which = LI is currently not supported.'):
backend.eigs(lambda x: x, [H], initial_state=init, which='LI')
with pytest.raises(
ValueError,
match="if no `initial_state` is passed, then `shape` and"
"`dtype` have to be provided"):
backend.eigs(lambda x: x, [H])
with pytest.raises(ValueError, match="`num_krylov_vecs`"):
backend.eigs(lambda x: x, [H], numeig=3, num_krylov_vecs=3)
with pytest.raises(TypeError, match="Expected a"):
backend.eigs(lambda x: x, [H], initial_state=[])
def test_index_copy():
D = 10
B = 4
dtype = np.int16
np.random.seed(10)
q1 = U1Charge(np.random.randint(-B // 2, B // 2 + 1, D).astype(dtype))
q2 = U1Charge(np.random.randint(-B // 2, B // 2 + 1, D).astype(dtype))
i = Index(charges=[q1, q2], flow=[False, True])
icopy = i.copy()
assert not np.any([a is b for a, b in zip(i._charges, icopy._charges)])
assert i.flow is not icopy.flow
def test_eigsh_small_number_krylov_vectors_sanity_check():
np.random.seed(10)
dtype = np.float64
backend = symmetric_backend.SymmetricBackend()
index = Index(U1Charge.random(2, 0, 0), True)
indices = [index, index.copy().flip_flow()]
H = BlockSparseTensor.random(indices, dtype=dtype)
H.data = np.array([1, 2, 3, 4], dtype=np.float64)
init = BlockSparseTensor.random([index], dtype=dtype)
init.data = np.array([1, 1], dtype=np.float64)
def mv(x, mat):
return mat @ x
eta, _ = backend.eigsh_lanczos(mv, [H], init, num_krylov_vecs=1)
np.testing.assert_allclose(eta[0], 5)
def test_eigs_valid_init_operator_with_shape_sanity_check(dtype):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
D = 16
index = Index(U1Charge.random(D, 0, 0), True)
indices = [index, index.copy().flip_flow()]
H = BlockSparseTensor.random(indices, dtype=dtype)
def mv(vec, mat):
return mat @ vec
init = BlockSparseTensor.random([index], dtype=dtype)
eta1, U1 = backend.eigs(mv, [H], init)
eta2, U2 = np.linalg.eig(H.todense())
compare_eigvals_and_eigvecs(np.stack([u.todense() for u in U1], axis=1),
eta1,
U2,
eta2,
thresh=1E-8)
def eye(column_index: Index,
row_index: Optional[Index] = None,
dtype: Optional[Type[np.number]] = None) -> BlockSparseTensor:
"""
Return an identity matrix.
Args:
column_index: The column index of the matrix.
row_index: The row index of the matrix.
dtype: The dtype of the matrix.
Returns:
BlockSparseTensor
"""
if row_index is None:
row_index = column_index.copy().flip_flow()
if dtype is None:
dtype = np.float64
blocks, _, shapes = _find_diagonal_sparse_blocks(
column_index.flat_charges + row_index.flat_charges,
column_index.flat_flows + row_index.flat_flows,
len(column_index.flat_charges))
data = np.empty(np.int64(np.sum(np.prod(shapes, axis=0))), dtype=dtype)
for n, block in enumerate(blocks):
data[block] = np.ravel(np.eye(shapes[0, n], shapes[1, n], dtype=dtype))
order = [list(np.arange(0, len(column_index.flat_charges)))] + [
list(
np.arange(
len(column_index.flat_charges),
len(column_index.flat_charges) + len(row_index.flat_charges)))
]
return BlockSparseTensor(
data=data,
charges=column_index.flat_charges + row_index.flat_charges,
flows=column_index.flat_flows + row_index.flat_flows,
order=order,
check_consistency=False)
def test_eigsh_valid_init_operator_with_shape_sanity_check(dtype):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
D = 16
index = Index(U1Charge.random(D, 0, 0), True)
indices = [index, index.copy().flip_flow()]
a = BlockSparseTensor.random(indices, dtype=dtype)
H = a + a.T.conj()
def mv(vec, mat):
return mat @ vec
init = BlockSparseTensor.random([index], dtype=dtype)
eta1, U1 = backend.eigsh_lanczos(mv, [H], init)
v1 = np.reshape(U1[0].todense(), (D))
v1 = v1 / sum(v1)
eta2, U2 = np.linalg.eigh(H.todense())
v2 = U2[:, 0]
v2 = v2 / sum(v2)
np.testing.assert_allclose(eta1[0], min(eta2))
np.testing.assert_allclose(v1, v2)
