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

psiMPS

Initial guess for the ground state, which is to be optimized in-place.

modelMPOModel

The model representing the Hamiltonian for which we want to find the ground state.

DMRG_paramsdict

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 left-most LP and right-most RP (‘left/right part’)
RP		of the environment. By default (`None`) generate trivial, see `MPOEnvironment` for details.
LP_age	int	The ‘age’ (i.e. number of physical sites invovled into the
RP_age		contraction) of the left-most LP and right-most 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` uses `DensityMatrixMixer` for the 2-site case and `SingleSiteMixer` for the 1-site case.
mixer_params	dict	Non-default initialization arguments of the mixer. Options may be custom to the specified mixer, so they’re documented in the class doc-string 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}` uses `chi_max=50` for the first 20 sweeps and `chi_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 `'lanczos'`. For possible arguments see `DMRGEngine.diag()`.
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 re-start).
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 if `Delta 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 wall-clock 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 current
P_tol_min		truncation error. Therefore, if P_tol_to_trunc is not `None`, we update P_tol of lanczos_params to `max_trunc_err*P_tol_to_trunc`, restricted to the interval [P_tol_min, P_tol_max], where `max_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 current
E_tol_min		truncation error. Therefore, if E_tol_to_trunc is not `None`, we update E_tol of lanczos_params to `max_E_trunc*E_tol_to_trunc`, restricted to the interval [E_tol_min, E_tol_max], where `max_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 by `TwoSiteDMRGEngine`.

Returns

infodict: A dictionary with keys 'E', 'shelve', 'bond_statistics', 'sweep_statistics'