run¶
full name: tenpy.algorithms.dmrg.run
parent module:
tenpy.algorithms.dmrg
type: function

tenpy.algorithms.dmrg.
run
(psi, model, DMRG_params)[source]¶ Run the DMRG algorithm to find the ground state of the given model.
 Parameters
psi (
MPS
) – Initial guess for the ground state, which is to be optimized inplace.model (
MPOModel
) – The model representing the Hamiltonian for which we want to find the ground state.DMRG_params (dict) –
Further optional parameters as described in the following table. Use
verbose>0
to print the used parameters during runtime.key
type
description
LP
npc.Array
Initial leftmost LP and rightmost RP (‘left/right part’)
RP
of the environment. By default (
None
) generate trivial, seeMPOEnvironment
for details.LP_age
int
The ‘age’ (i.e. number of physical sites invovled into the
RP_age
contraction) of the leftmost LP and rightmost RP of the environment.
mixer
str  class  bool
Chooses the
Mixer
to be used. A string stands for one of the mixers defined in this module, a class is used as custom mixer. Default (None
) uses no mixer,True
usesDensityMatrixMixer
for the 2site case andSingleSiteMixer
for the 1site case.mixer_params
dict
Nondefault initialization arguments of the mixer. Options may be custom to the specified mixer, so they’re documented in the class docstring of the mixer.
orthogonal_to
list of MPS
List of other matrix produc states to orthogonalize against. Works only for finite systems. This parameter can be used to find (a few) excited states as follows. First, run DMRG to find the ground state and then run DMRG again while orthogonalizing against the ground state, which yields the first excited state (in the same symmetry sector), and so on.
combine
bool
Whether to combine legs into pipes. This combines the virtual and physical leg for the left site (when moving right) or right side (when moving left) into pipes. This reduces the overhead of calculating charge combinations in the contractions, but one
matvec()
is formally more expensive, \(O(2 d^3 \chi^3 D)\).trunc_params
dict
Truncation parameters as described in
truncate()
chi_list
dict  None
A dictionary to gradually increase the chi_max parameter of trunc_params. The key defines starting from which sweep chi_max is set to the value, e.g.
{0: 50, 20: 100}
useschi_max=50
for the first 20 sweeps andchi_max=100
afterwards. Overwrites trunc_params[‘chi_list’]`. By default (None
) this feature is disabled.lanczos_params
dict
Lanczos parameters as described in
lanczos()
diag_method
str
Method to be used for diagonalzation, default
'default'
. For possible arguments seeDMRGEngine.diag()
.max_N_for_ED
int
Maximum matrix dimension of the effective hamiltonian up to which the
'default'
diag_method uses ED instead of Lanczos.N_sweeps_check
int
Number of sweeps to perform between checking convergence criteria and giving a status update.
sweep_0
int
The number of sweeps already performed. (Useful for restart).
start_env
int
Number of initial sweeps performed without bond optimizaiton to initialize the environment.
update_env
int
Number of sweeps without bond optimizaiton to update the environment for infinite boundary conditions, performed every N_sweeps_check sweeps.
norm_tol
float
After the DMRG run, update the environment with at most norm_tol_iter sweeps until
np.linalg.norm(psi.norm_err()) < norm_tol
.norm_tol_iter
float
Perform at most norm_tol_iter`*`update_env sweeps to converge the norm error below norm_tol. If the state is not converged after that, call
canonical_form()
instead.max_sweeps
int
Maximum number of sweeps to be performed.
min_sweeps
int
Minimum number of sweeps to be performed. Defaults to 1.5*N_sweeps_check.
max_E_err
float
Convergence if the change of the energy in each step satisfies
Delta E / max(E, 1) < max_E_err
. Note that this is also satisfied ifDelta E > 0
, i.e., if the energy increases (due to truncation).max_S_err
float
Convergence if the relative change of the entropy in each step satisfies
Delta S/S < max_S_err
max_hours
float
If the DMRG took longer (measured in wallclock time), ‘shelve’ the simulation, i.e. stop and return with the flag
shelve=True
.P_tol_to_trunc
float
It’s reasonable to choose the Lanczos convergence criteria
P_tol_max
'P_tol'
not many magnitudes lower than the currentP_tol_min
truncation error. Therefore, if P_tol_to_trunc is not
None
, we update P_tol of lanczos_params tomax_trunc_err*P_tol_to_trunc
, restricted to the interval [P_tol_min, P_tol_max], wheremax_trunc_err
is the maximal truncation error (discarded weight of the Schmidt values) due to truncation right after each Lanczos optimization during the sweeps.E_tol_to_trunc
float
It’s reasonable to choose the Lanczos convergence criteria
E_tol_max
'E_tol'
not many magnitudes lower than the currentE_tol_min
truncation error. Therefore, if E_tol_to_trunc is not
None
, we update E_tol of lanczos_params tomax_E_trunc*E_tol_to_trunc
, restricted to the interval [E_tol_min, E_tol_max], wheremax_E_trunc
is the maximal energy difference due to truncation right after each Lanczos optimization during the sweeps.active_sites
int
The number of active sites to be used by DMRG. If set to 1,
SingleSiteDMRGEngine
is used. If set to 2, DMRG is handled byTwoSiteDMRGEngine
.
 Returns
info – A dictionary with keys
'E', 'shelve', 'bond_statistics', 'sweep_statistics'
 Return type