def test_opt(wavelet_tensors):
h, iso, dis = wavelet_tensors
s = np.reshape(np.eye(2**3) / 2**3, [2] * 6)
for _ in range(20):
s = simple_mera.descend(h, s, iso, dis)
s, iso, dis = simple_mera.optimize_linear(h, s, iso, dis, 100)
en = np.trace(np.reshape(s, [2**3, -1]) @ np.reshape(h, [2**3, -1]))
assert en < -1.25
def test_energy(wavelet_tensors):
h, iso, dis = wavelet_tensors
s = np.reshape(np.eye(2**3) / 2**3, [2] * 6)
for _ in range(20):
s = simple_mera.descend(h, s, iso, dis)
en = np.trace(np.reshape(s, [2**3, -1]) @ np.reshape(h, [2**3, -1]))
assert np.isclose(en, -1.242, rtol=1e-3, atol=1e-3)
en = simple_mera.binary_mera_energy(h, s, iso, dis)
assert np.isclose(en, -1.242, rtol=1e-3, atol=1e-3)
def test_descend(random_tensors):
h, s, iso, dis = random_tensors
s = simple_mera.descend(h, s, iso, dis)
assert len(s.shape) == 6
D = s.shape[0]
smat = np.reshape(s, [D**3] * 2)
assert np.isclose(np.trace(smat), 1.0)
assert np.isclose(np.linalg.norm(smat - np.conj(np.transpose(smat))), 0.0)
spec, _ = np.linalg.eigh(smat)
assert np.alltrue(spec >= 0.0)
