def absorb_lm(self, nt, if_sqrt, which_vb):
# which_vb does NOT count physical bond
tensor = self.tensors[nt].copy()
if if_sqrt:
if which_vb is 'all':
tensor = tm.absorb_matrices2tensor(tensor, [
np.diag(np.sqrt(self.lm[self.pos_lm[nt][n]]))
for n in range(0, self.nVirtual)
], [n + 1 for n in range(0, self.nVirtual)])
elif type(which_vb) is int:
tensor = tm.absorb_matrix2tensor(
tensor,
np.diag(np.sqrt(self.lm[self.pos_lm[nt][which_vb]])),
which_vb + 1)
else:
tensor = tm.absorb_matrices2tensor(tensor, [
np.diag(np.sqrt(self.lm[self.pos_lm[nt][n]]))
for n in which_vb
], [n + 1 for n in which_vb])
else:
if which_vb is 'all':
tensor = tm.absorb_matrices2tensor(tensor, [
np.diag(self.lm[self.pos_lm[nt][n]])
for n in range(0, self.nVirtual)
], [n + 1 for n in range(0, self.nVirtual)])
elif type(which_vb) is int:
tensor = tm.absorb_matrix2tensor(
tensor, np.diag(self.lm[self.pos_lm[nt][which_vb]]),
which_vb + 1)
else:
tensor = tm.absorb_matrices2tensor(
tensor,
[np.diag(self.lm[self.pos_lm[nt][n]])
for n in which_vb], [n + 1 for n in which_vb])
return tensor
def one_bond_so_transformation(self, nt1, vb1, nt2, vb2):
# Super-orthogonal transformation on one virtual bond
# vb does NOT count the physical bond
if self._is_debug:
if self.pos_lm[nt1][vb1] != self.pos_lm[nt2][vb2]:
bf.print_error(
'In one_bond_so_transformation, the two virtual bonds must'
'correspond to the same lambda')
m1 = self.bond_env_matrix_simple(nt1, vb1)
m2 = self.bond_env_matrix_simple(nt2, vb2)
flag = False
if self._is_debug:
_lm = self.lm[self.pos_lm[nt1][vb1]].copy()
flag = (self.chi == self.tensors[nt1].shape[vb1 + 1])
u1, u2, self.lm[self.pos_lm[nt1]
[vb1]] = tm.transformation_from_env_mats(
m1,
m2,
self.lm[self.pos_lm[nt1][vb1]],
self.chi,
norm_way=1)[:3]
if self._is_debug and flag:
_tmp = u1.dot(np.diag(self.lm[self.pos_lm[nt1][vb1]])).dot(u2.T)
err = np.linalg.norm(tm.off_diagonal_mat(_tmp).reshape(-1, ))
if err > 1e-10:
print(
'Warning of the transformations from environment: not diagonal (%g)'
% err)
_tmp = np.diag(_tmp)
_tmp = _tmp / np.linalg.norm(_tmp)
err = np.linalg.norm(_tmp - self.lm[self.pos_lm[nt1][vb1]])
if err > 1e-10:
print(
'Warning of the transformations from environment: not recover lm (%g)'
% err)
print(self.lm[self.pos_lm[nt1][vb1]])
self.tensors[nt1] = tm.absorb_matrix2tensor(self.tensors[nt1], u1,
vb1 + 1)
self.tensors[nt2] = tm.absorb_matrix2tensor(self.tensors[nt2], u2,
vb2 + 1)
self.tensors[nt1] /= max(abs(self.tensors[nt1].reshape(-1, 1)))
self.tensors[nt2] /= max(abs(self.tensors[nt2].reshape(-1, 1)))
# self.lm[self.pos_lm[nt1][vb1]] = tm.normalize_tensor(self.lm[self.pos_lm[nt1][vb1]])[0]
return m1, m2
