DMRGThreadPlusHC

full name: tenpy.algorithms.dmrg_parallel.DMRGThreadPlusHC
parent module: tenpy.algorithms.dmrg_parallel
type: class

Inheritance Diagram

Methods

`DMRGThreadPlusHC.__init__`(psi, model, ...)
`DMRGThreadPlusHC.diag`(theta_guess)	Diagonalize the effective Hamiltonian represented by self.
`DMRGThreadPlusHC.environment_sweeps`(N_sweeps)	Perform N_sweeps sweeps without optimization to update the environment.
`DMRGThreadPlusHC.estimate_RAM`([...])	Gives an approximate prediction for the required memory usage.
`DMRGThreadPlusHC.free_no_longer_needed_envs`()	Remove no longer needed environments after an update.
`DMRGThreadPlusHC.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`DMRGThreadPlusHC.get_sweep_schedule`()	Define the schedule of the sweep.
`DMRGThreadPlusHC.init_env`([model, ...])	(Re-)initialize the environment.
`DMRGThreadPlusHC.is_converged`()	Determines if the algorithm is converged.
`DMRGThreadPlusHC.make_eff_H`()	Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.
`DMRGThreadPlusHC.mixed_svd`(theta)	Get (truncated) B from the new theta (as returned by diag).
`DMRGThreadPlusHC.mixer_activate`()	Set self.mixer to the class specified by options['mixer'].
`DMRGThreadPlusHC.mixer_cleanup`()	Cleanup the effects of a mixer.
`DMRGThreadPlusHC.mixer_deactivate`()	Deactivate the mixer.
`DMRGThreadPlusHC.plot_sweep_stats`([axes, ...])	Plot `sweep_stats` to display the convergence with the sweeps.
`DMRGThreadPlusHC.plot_update_stats`(axes[, ...])	Plot `update_stats` to display the convergence during the sweeps.
`DMRGThreadPlusHC.post_run_cleanup`()	Perform any final steps or clean up after the main loop has terminated.
`DMRGThreadPlusHC.post_update_local`(E0, age, ...)	Perform post-update actions.
`DMRGThreadPlusHC.pre_run_initialize`()	Perform preparations before `run_iteration()` is iterated.
`DMRGThreadPlusHC.prepare_svd`(theta)	Transform theta into matrix for svd.
`DMRGThreadPlusHC.prepare_update_local`()	Prepare self for calling `update_local()`.
`DMRGThreadPlusHC.reset_stats`([resume_data])	Reset the statistics, useful if you want to start a new sweep run.
`DMRGThreadPlusHC.resume_run`()	Resume a run that was interrupted.
`DMRGThreadPlusHC.run`()	Run the DMRG simulation to find the ground state.
`DMRGThreadPlusHC.run_iteration`()	Perform a single iteration, consisting of `N_sweeps_check` sweeps.
`DMRGThreadPlusHC.set_B`(U, S, VH)	Update the MPS with the `U, S, VH` returned by self.mixed_svd.
`DMRGThreadPlusHC.status_update`(...)	Emits a status message to the logging system after an iteration.
`DMRGThreadPlusHC.stopping_criterion`(...)	Determines if the main loop should be terminated.
`DMRGThreadPlusHC.sweep`([optimize, meas_E_trunc])	One 'sweep' of the algorithm.
`DMRGThreadPlusHC.switch_engine`(other_engine, *)	Initialize algorithm from another algorithm instance of a different class.
`DMRGThreadPlusHC.update_env`(**update_data)	Update the left and right environments after an update of the state.
`DMRGThreadPlusHC.update_local`(theta[, optimize])	Perform site-update on the site `i0`.
`DMRGThreadPlusHC.update_segment_boundaries`()	Update the singular values at the boundaries of the segment.

Class Attributes and Properties

`DMRGThreadPlusHC.S_inv_cutoff`
`DMRGThreadPlusHC.n_optimize`	The number of sites to be optimized at once.
`DMRGThreadPlusHC.use_mixer_by_default`

class tenpy.algorithms.dmrg_parallel.DMRGThreadPlusHC(psi, model, options, **kwargs)[source]

Bases: TwoSiteDMRGEngine

EffectiveH[source]: alias of TwoSiteHThreadPlusHC

make_eff_H()[source]: Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.

run()[source]

Run the DMRG simulation to find the ground state.

Returns:

E (float) – The energy of the resulting ground state MPS.
psi (MPS) – The MPS representing the ground state after the simulation, i.e. just a reference to psi.

DefaultMixer[source]: alias of DensityMatrixMixer

diag(theta_guess)[source]

Diagonalize the effective Hamiltonian represented by self.

option DMRGEngine.max_N_for_ED: int: Maximum matrix dimension of the effective hamiltonian up to which the 'default' diag_method uses ED instead of Lanczos.

option DMRGEngine.diag_method: str

One of the following strings:

‘default’: Same as 'lanczos' for large bond dimensions, but if the total dimension of the effective Hamiltonian does not exceed the DMRG parameter 'max_N_for_ED' it uses 'ED_block'.
‘lanczos’: lanczos() Default, the Lanczos implementation in TeNPy.
‘arpack’: lanczos_arpack() Based on scipy.linalg.sparse.eigsh(). Slower than ‘lanczos’, since it needs to convert the npc arrays to numpy arrays during each matvec, and possibly does many more iterations.
‘ED_block’: full_diag_effH() Contract the effective Hamiltonian to a (large!) matrix and diagonalize the block in the charge sector of the initial state. Preserves the charge sector of the explicitly conserved charges. However, if you don’t preserve a charge explicitly, it can break it. For example if you use a SpinChain({'conserve': 'parity'}), it could change the total “Sz”, but not the parity of ‘Sz’.
‘ED_all’: full_diag_effH() Contract the effective Hamiltonian to a (large!) matrix and diagonalize it completely. Allows to change the charge sector even for explicitly conserved charges. For example if you use a SpinChain({'conserve': 'Sz'}), it can change the total “Sz”.

Parameters:

theta_guess (Array) – Initial guess for the ground state of the effective Hamiltonian.

Returns:

E0 (float) – Energy of the found ground state.
theta (Array) – Ground state of the effective Hamiltonian.
N (int) – Number of Lanczos iterations used. -1 if unknown.
ov_change (float) – Change in the wave function 1. - abs(<theta_guess|theta_diag>)

environment_sweeps(N_sweeps)[source]

Perform N_sweeps sweeps without optimization to update the environment.

Parameters:: N_sweeps (int) – Number of sweeps to run without optimization

estimate_RAM(mem_saving_factor=None)[source]

Gives an approximate prediction for the required memory usage.

This calculation is based on the requested bond dimension, the local Hilbert space dimension, the number of sites, and the boundary conditions.

Parameters:: mem_saving_factor (float) – Represents the amount of RAM saved due to conservation laws. By default, it is ‘None’ and is extracted from the model automatically. However, this is only possible in a few cases and needs to be estimated in most cases. This is due to the fact that it is dependent on the model parameters. If one has a better estimate, one can pass the value directly. This value can be extracted by building the initial state psi (usually by performing DMRG) and then calling print(psi.get_B(0).sparse_stats()) TeNPy will automatically print the fraction of nonzero entries in the first line, for example, 6 of 16 entries (=0.375) nonzero. This fraction corresponds to the mem_saving_factor; in our example, it is 0.375.
Returns:: usage – Required RAM in MB.
Return type:: float

See also

tenpy.simulations.simulation.estimate_simulation_RAM: global function calling this.

free_no_longer_needed_envs()[source]

Remove no longer needed environments after an update.

This allows to minimize the number of environments to be kept. For large MPO bond dimensions, these environments are by far the biggest part in memory, so this is a valuable optimization to reduce memory requirements.

get_resume_data(sequential_simulations=False)[source]

Return necessary data to resume a run() interrupted at a checkpoint.

At a checkpoint, you can save psi, model and options along with the data returned by this function. When the simulation aborts, you can resume it using this saved data with:

eng = AlgorithmClass(psi, model, options, resume_data=resume_data)
eng.resume_run()

An algorithm which doesn’t support this should override resume_run to raise an Error.

Parameters:: sequential_simulations (bool) – If True, return only the data for re-initializing a sequential simulation run, where we “adiabatically” follow the evolution of a ground state (for variational algorithms), or do series of quenches (for time evolution algorithms); see run_seq_simulations().
Returns:: resume_data – Dictionary with necessary data (apart from copies of psi, model, options) that allows to continue the algorithm run from where we are now. It might contain an explicit copy of psi.
Return type:: dict

get_sweep_schedule()[source]

Define the schedule of the sweep.

One ‘sweep’ is a full sequence from the leftmost site to the right and back.

Returns:: schedule – Schedule for the sweep. Each entry is (i0, move_right, (update_LP, update_RP)), where i0 is the leftmost of the self.EffectiveH.length sites to be updated in update_local(), move_right indicates whether the next i0 in the schedule is right (True), left (False) or equal (None) of the current one, and update_LP, update_RP indicate whether it is necessary to update the LP and RP of the environments.
Return type:: iterable of (int, bool, (bool, bool))

init_env(model=None, resume_data=None, orthogonal_to=None)[source]

(Re-)initialize the environment.

This function is useful to (re-)start a Sweep with a slightly different model or different (engine) parameters. Note that we assume that we still have the same psi. Calls reset_stats().

Parameters:

model (MPOModel) – The model representing the Hamiltonian for which we want to find the ground state. If None, keep the model used before.
resume_data (None | dict) – Given when resuming a simulation, as returned by get_resume_data(). Can contain another dict under the key init_env_data; the contents of init_env_data get passed as keyword arguments to the environment initialization.
orthogonal_to (None | list of MPS | list of dict) – List of other matrix product states to orthogonalize against. Instead of just the state, you can specify a dict with the state as ket and further keyword arguments for initializing the MPSEnvironment; the psi to be optimized is used as bra. Works only for finite or segment MPS; for infinite MPS it must be None. This 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. Note that resume_data['orthogonal_to'] takes precedence over the argument.

Options

option Sweep.start_env: int: Number of sweeps to be performed without optimization to update the environment.

Raises:: ValueError – If the engine is re-initialized with a new model, which legs are incompatible with those of hte old model.

is_converged()[source]

Determines if the algorithm is converged.

Does not cover any other reasons to abort, such as reaching a time limit. Such checks are covered by stopping_condition().

Options

option DMRGEngine.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 might be satisfied even if Delta E > 0, i.e., if the energy increases (due to truncation).

option DMRGEngine.max_S_err: float: Convergence if the relative change of the entropy in each step satisfies |Delta S|/S < max_S_err

mixed_svd(theta)[source]

Get (truncated) B from the new theta (as returned by diag).

The goal is to split theta and truncate it:

|   -- theta --   ==>    -- U -- S --  VH -
|      |   |                |          |

Without a mixer, this is done by a simple svd and truncation of Schmidt values.

With a mixer, the state is perturbed before the SVD. The details of the perturbation are defined by the Mixer class.

Note that the returned S is a general (not diagonal) matrix, with labels 'vL', 'vR'.

Parameters:

theta (Array) – The optimized wave function, prepared for svd.

Returns:

U (Array) – Left-canonical part of theta. Labels '(vL.p)', 'vR'.
S (1D ndarray | 2D Array) – Without mixer just the singular values of the array; with mixer it might be a general matrix with labels 'vL', 'vR'; see comment above.
VH (Array) – Right-canonical part of theta. Labels 'vL', '(p.vR)'.
err (TruncationError) – The truncation error introduced.
S_approx (ndarray) – Just the S if a 1D ndarray, or an approximation of the correct S (which was used for truncation) in case S is 2D Array.

mixer_activate()[source]

Set self.mixer to the class specified by options[‘mixer’].

option Sweep.mixer: str | class | bool | None: Specifies which Mixer to use, if any. A string stands for one of the mixers defined in this module. A class is assumed to have the same interface as Mixer and is used to instantiate the mixer. None uses no mixer. True uses the mixer specified by the DefaultMixer class attribute. The default depends on the subclass of Sweep.

option Sweep.mixer_params: dict: Mixer parameters as described in Mixer.