def test_explicit_sweeps(self):
n = 8
chi = 16
ham = MPO_ham_mbl(n, dh=4, seed=42)
p0 = MPS_rand_state(n, 2).expand_bond_dimension(chi)
b0 = p0.H
p0.align_(ham, b0)
en0 = (p0 & ham & b0) ^ ...
dmrgx = DMRGX(ham, p0, chi)
dmrgx.sweep_right()
en1 = dmrgx.sweep_left(canonize=False)
assert en0 != en1
dmrgx.sweep_right(canonize=False)
en = dmrgx.sweep_right(canonize=True)
# check normalized
assert_allclose(dmrgx._k.H @ dmrgx._k, 1.0)
k = dmrgx._k.to_dense()
h = ham.to_dense()
el, ev = eigh(h)
# check variance very low
assert np.abs((k.H @ h @ h @ k) - (k.H @ h @ k)**2) < 1e-12
# check exactly one eigenvalue matched well
assert np.sum(np.abs(el - en) < 1e-12) == 1
# check exactly one eigenvector is matched with high fidelity
ovlps = (ev.H @ k).A**2
big_ovlps = ovlps[ovlps > 1e-12]
assert_allclose(big_ovlps, [1])
# check fully
assert is_eigenvector(k, h, tol=1e-10)
def test_sites_mpo_mps_product(self, cyclic):
k = MPS_rand_state(13, 7, cyclic=cyclic)
X = MPO_rand_herm(3, 5, sites=[3, 6, 7], nsites=13, cyclic=cyclic)
b = k.H
k.align_(X, b)
assert (k & X & b) ^ ...