def dmrg_infinite_size_sawtooth(para=None, A=None):
from library.MPSClass import MpsInfiniteSawtooth as Minf
is_print = True
t_start = time.time()
info = dict()
if is_print:
print('Start ' + str(para['n_site']) +
'-site iDMRG (sawtooth) calculation')
if para is None:
para = pm.generate_parameters_infinite_dmrg_sawtooth()
para = pm.make_para_consistent_idmrg_sawtooth(para)
if A is None:
d = para['d']
A = Minf(para['form'],
d,
para['chi'],
para['d']**para['n_site'],
n_site=para['n_site'],
is_symme_env=para['is_symme_env'],
dmrg_type=para['dmrg_type'],
spin=para['spin'])
ob = dict()
e1 = 0
de = 1
A.update_ort_tensor_mps_sawtooth()
A.update_bath_onsite_sawtooth(para['j1'], para['j2'], para['hx'],
para['hz'])
A.update_effective_ops_sawtooth()
# iDMRG sweep
for t in range(0, para['sweep_time']):
A.update_central_tensor_sawtooth(para['tau'], para['j1'], para['j2'],
para['hx'], para['hz'])
if t % para['dt_ob'] == 0:
A.rho_from_central_tensor_sawtooth()
ob['eb'], ob['mag'], ob['energy_site'], ob[
'ent'] = A.observation_sawtooth(para['j1'], para['j2'],
para['hx'], para['hz'])
if is_print:
print('At the %g-th sweep: Eb = ' % t + str(e1))
de = sum(abs(ob['eb'] - e1)) / ob['eb'].__len__()
if de > para['break_tol']:
e1 = ob['eb']
elif is_print:
print('Converged with de = %g' % de)
break
if t == para['sweep_time']:
print('Not sufficiently converged with de = %g' % de)
A.update_ort_tensor_mps_sawtooth()
A.update_bath_onsite_sawtooth(para['j1'], para['j2'], para['hx'],
para['hz'])
A.update_effective_ops_sawtooth()
info['t_cost'] = time.time() - t_start
print('Energy per site = %g' % ob['energy_site'])
print('x-magnetization = ' + str(ob['mag']['x']))
print('z-magnetization = ' + str(ob['mag']['z']))
print('Entanglement = ' + str(ob['ent']))
print('Total time cost: %g' % info['t_cost'])
return A, ob, info
def dmrg_infinite_size(para=None, A=None, hamilt=None):
from library.MPSClass import MpsInfinite as Minf
is_print = True
t_start = time.time()
info = dict()
if is_print:
print('Start ' + str(para['n_site']) + '-site iDMRG calculation')
if para is None:
para = pm.generate_parameters_infinite_dmrg_sawtooth()
if hamilt is None:
hamilt = hamiltonian_heisenberg(para['spin'], para['jxy'], para['jxy'],
para['jz'], para['hx'] / 2,
para['hz'] / 2)
if A is None:
if para['dmrg_type'] is 'mpo':
d = para['d']**para['n_site']
else:
d = para['d']
A = Minf(para['form'],
d,
para['chi'],
para['d']**para['n_site'],
n_site=para['n_site'],
is_symme_env=para['is_symme_env'],
dmrg_type=para['dmrg_type'],
hamilt_index=para['hamilt_index'])
if A.dmrg_type is 'mpo':
tensor = hamiltonian2cell_tensor(hamilt, para['tau'])
else:
tensor = np.zeros(0)
if A.n_site == 1:
# singe-site iDMRG
e0 = 0
e1 = 1
else:
# double-site iDMRG (including White's way)
e0 = np.zeros((1, 3))
e1 = np.ones((1, 3))
de = 1
if A.is_symme_env:
A.update_ort_tensor_mps('left')
if A.dmrg_type is 'white':
A.update_bath_onsite()
A.update_effective_ops()
else:
A.update_left_env(tensor)
else:
A.update_ort_tensor_mps('both')
A.update_left_env(tensor)
A.update_right_env(tensor)
# iDMRG sweep
for t in range(0, para['sweep_time']):
if A.dmrg_type is 'mpo':
A.update_central_tensor(tensor)
else:
A.update_central_tensor((para['tau'], 'full'))
if t % para['dt_ob'] == 0:
A.rho_from_central_tensor()
e1 = A.observe_energy(hamilt)
if is_print:
print('At the %g-th sweep: Eb = ' % t + str(e1))
de = np.sum(abs(e0 - e1)) / A.n_site
if de > para['break_tol']:
e0 = e1
elif is_print:
print('Converged with de = %g' % de)
break
if t == para['sweep_time']:
print('Not sufficiently converged with de = %g' % de)
if A.is_symme_env:
A.update_ort_tensor_mps('left')
if A.dmrg_type is 'mpo':
A.update_left_env(tensor)
else:
A.update_bath_onsite()
A.update_effective_ops()
else:
A.update_ort_tensor_mps('both')
A.update_left_env(tensor)
A.update_right_env(tensor)
ob = {'eb': e1}
info['t_cost'] = time.time() - t_start
if is_print:
print('Total time cost: %g' % info['t_cost'])
return A, ob, info
