PurificationApplyMPO

• full name: tenpy.algorithms.purification.PurificationApplyMPO

• parent module: tenpy.algorithms.purification

• type: class

Inheritance Diagram

Methods

PurificationApplyMPO.__init__(psi, U_MPO, ...)
PurificationApplyMPO.environment_sweeps(N_sweeps)	Perform N_sweeps sweeps without optimization to update the environment.
PurificationApplyMPO.free_no_longer_needed_envs()	Remove no longer needed environments after an update.
PurificationApplyMPO.get_resume_data([...])	Return necessary data to resume a run() interrupted at a checkpoint.
PurificationApplyMPO.get_sweep_schedule()	Define the schedule of the sweep.
PurificationApplyMPO.init_env(U_MPO[, ...])	Initialize the environment.
PurificationApplyMPO.make_eff_H()	Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.
PurificationApplyMPO.mixer_activate()	Set self.mixer to the class specified by options['mixer'].
PurificationApplyMPO.mixer_cleanup()	Cleanup the effects of a mixer.
PurificationApplyMPO.mixer_deactivate()	Deactivate the mixer.
PurificationApplyMPO.post_update_local(err, ...)	Algorithm-specific actions to be taken after local update.
PurificationApplyMPO.prepare_update_local()	Prepare self for calling update_local().
PurificationApplyMPO.reset_stats([resume_data])	Reset the statistics.
PurificationApplyMPO.resume_run()	Resume a run that was interrupted.
PurificationApplyMPO.run()	Run the compression.
PurificationApplyMPO.sweep([optimize])	One 'sweep' of a sweeper algorithm.
PurificationApplyMPO.switch_engine(...[, ...])	Initialize algorithm from another algorithm instance of a different class.
PurificationApplyMPO.update_env(**update_data)	Update the left and right environments after an update of the state.
PurificationApplyMPO.update_local(_[, optimize])	Perform local update.
PurificationApplyMPO.update_new_psi(theta)	Given a new two-site wave function theta, split it and save it in psi.

Class Attributes and Properties

PurificationApplyMPO.DefaultMixer
PurificationApplyMPO.S_inv_cutoff
PurificationApplyMPO.engine_params
PurificationApplyMPO.n_optimize	The number of sites to be optimized at once.
PurificationApplyMPO.use_mixer_by_default
PurificationApplyMPO.verbose

class tenpy.algorithms.purification.PurificationApplyMPO(psi, U_MPO, options, **kwargs)

Bases: VariationalApplyMPO

Variant of VariationalApplyMPO suitable for purification.

EffectiveH

alias of PurificationTwoSiteU

update_local(_, optimize=True)

Perform local update.

This simply contracts the environments and theta from the ket to get an updated theta for the bra self.psi (to be changed in place).

update_new_psi(theta)

Given a new two-site wave function theta, split it and save it in psi.

environment_sweeps(N_sweeps)

Perform N_sweeps sweeps without optimization to update the environment.

Parameters

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

free_no_longer_needed_envs()

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 optimiztion to reduce memory requirements.

get_resume_data(sequential_simulations=False)

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 simulation from where we are now. It might contain an explicit copy of psi.

Return type

dict

get_sweep_schedule()

Define the schedule of the sweep.

Compared to get_sweep_schedule(), we add one extra update at the end with i0=0 (which is the same as the first update of the sweep). This is done to ensure proper convergence after each sweep, even if that implies that the site 0 is then updated twice per sweep.

init_env(U_MPO, resume_data=None, orthogonal_to=None)

Initialize the environment.

Parameters

• U_MPO (MPO) – The MPO to be applied to the sate.

• resume_data (dict) – May contain init_env_data.

• orthogonal_to – Ignored.

make_eff_H()

Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.

mixer_activate()

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.

See also

mixer_deactivate

mixer_cleanup()

Cleanup the effects of a mixer.

A sweep() with an enabled Mixer leaves the MPS psi with 2D arrays in S. To recover the originial form, this function simply performs one sweep with disabled mixer.

mixer_deactivate()

Deactivate the mixer.

Set self.mixer=None and revert any other effects of mixer_acitvate().

property n_optimize

The number of sites to be optimized at once.

Indirectly set by the class attribute EffectiveH and it’s length. For example, TwoSiteDMRGEngine uses the TwoSiteH and hence has n_optimize=2, while the SingleSiteDMRGEngine has n_optimize=1.

post_update_local(err, **update_data)

Algorithm-specific actions to be taken after local update.

An example would be to collect statistics.

prepare_update_local()

Prepare self for calling update_local().

Returns

theta – Current best guess for the ground state, which is to be optimized. Labels are 'vL', 'p0', 'p1', 'vR', or combined versions of it (if self.combine). For single-site DMRG, the 'p1' label is missing.

Return type

Array

reset_stats(resume_data=None)

Reset the statistics. Useful if you want to start a new Sweep run.

This method is expected to be overwritten by subclass, and should then define self.update_stats and self.sweep_stats dicts consistent with the statistics generated by the algorithm particular to that subclass.

Parameters

resume_data (dict) – Given when resuming a simulation, as returned by get_resume_data(). Here, we read out the sweeps.

Options

option Sweep.chi_list: None | dict(int -> int)

By default (None) this feature is disabled. A dict allows to gradually increase the chi_max. An entry at_sweep: chi states that starting from sweep at_sweep, the value chi is to be used for trunc_params['chi_max']. For example chi_list={0: 50, 20: 100} uses chi_max=50 for the first 20 sweeps and chi_max=100 afterwards.

resume_run()

Resume a run that was interrupted.

In case we saved an intermediate result at a checkpoint, this function allows to resume the run() of the algorithm (after re-initialization with the resume_data). Since most algorithms just have a while loop with break conditions, the default behaviour implemented here is to just call run().

run()

Run the compression.

The state psi is compressed in place.

Warning

Call this function directly after initializing the class, without modifying psi inbetween. A copy of psi is made during init_env()!

Returns

max_trunc_err – The maximal truncation error of a two-site wave function.

Return type

TruncationError

sweep(optimize=True)

One ‘sweep’ of a sweeper algorithm.

Iteratate over the bond which is optimized, to the right and then back to the left to the starting point.

Parameters

optimize (bool, optional) – Whether we actually optimize the state, e.g. to find the ground state of the effective Hamiltonian in case of a DMRG. (If False, just update the environments).

Options

option Sweep.chi_list_reactivates_mixer: bool

If True, the mixer is reset/reactivated each time the bond dimension growths due to Sweep.chi_list.

Returns

max_trunc_err – Maximal truncation error introduced.

Return type

float

classmethod switch_engine(other_engine, *, options=None, **kwargs)

Initialize algorithm from another algorithm instance of a different class.

You can initialize one engine from another, not too different subclasses. Internally, this function calls get_resume_data() to extract data from the other_engine and then initializes the new class.

Note that it transfers the data without making copies in most case; even the options! Thus, when you call run() on one of the two algorithm instances, it will modify the state, environment, etc. in the other. We recommend to make the switch as engine = OtherSubClass.switch_engine(engine) directly replacing the reference.

Parameters

• cls (class) – Subclass of Algorithm to be initialized.

• other_engine (Algorithm) – The engine from which data should be transferred. Another, but not too different algorithm subclass-class; e.g. you can switch from the TwoSiteDMRGEngine to the OneSiteDMRGEngine.

• options (None | dict-like) – If not None, these options are used for the new initialization. If None, take the options from the other_engine.

• **kwargs – Further keyword arguments for class initialization. If not defined, resume_data is collected with get_resume_data().

update_env(**update_data)

Update the left and right environments after an update of the state.

Parameters

**update_data – Whatever is returned by update_local().