PurificationApplyMPO¶
full name: tenpy.algorithms.purification.PurificationApplyMPO
parent module:
tenpy.algorithms.purification
type: class
Inheritance Diagram

Methods
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Perform N_sweeps sweeps without optimization to update the environment. |
Remove no longer needed environments after an update. |
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Return necessary data to resume a |
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Define the schedule of the sweep. |
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Initialize the environment. |
Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H. |
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Set self.mixer to the class specified by options['mixer']. |
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Cleanup the effects of a mixer. |
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Deactivate the mixer. |
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Algorithm-specific actions to be taken after local update. |
Prepare self for calling |
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Reset the statistics. |
Resume a run that was interrupted. |
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Run the compression. |
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One 'sweep' of a sweeper algorithm. |
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Initialize algorithm from another algorithm instance of a different class. |
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Update the left and right environments after an update of the state. |
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Perform local update. |
Given a new two-site wave function theta, split it and save it in |
Class Attributes and Properties
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The number of sites to be optimized at once. |
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- class tenpy.algorithms.purification.PurificationApplyMPO(psi, U_MPO, options, **kwargs)[source]¶
Bases:
VariationalApplyMPO
Variant of VariationalApplyMPO suitable for purification.
- EffectiveH[source]¶
alias of
PurificationTwoSiteU
- update_local(_, optimize=True)[source]¶
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)[source]¶
Given a new two-site wave function theta, split it and save it in
psi
.
- 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
- 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 optimiztion 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 savepsi
,model
andoptions
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
- get_sweep_schedule()[source]¶
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.
- 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 asMixer
and is used to instantiate themixer
.None
uses no mixer.True
uses the mixer specified by theDefaultMixer
class attribute. The default depends on the subclass ofSweep
.
See also
- mixer_cleanup()[source]¶
Cleanup the effects of a mixer.
A
sweep()
with an enabledMixer
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()[source]¶
Deactivate the mixer.
Set
self.mixer=None
and revert any other effects ofmixer_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 theTwoSiteH
and hence hasn_optimize=2
, while theSingleSiteDMRGEngine
hasn_optimize=1
.
- post_update_local(err, **update_data)[source]¶
Algorithm-specific actions to be taken after local update.
An example would be to collect statistics.
- prepare_update_local()[source]¶
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
- reset_stats(resume_data=None)[source]¶
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 fortrunc_params['chi_max']
. For examplechi_list={0: 50, 20: 100}
useschi_max=50
for the first 20 sweeps andchi_max=100
afterwards.
- resume_run()[source]¶
Resume a run that was interrupted.
In case we saved an intermediate result at a
checkpoint
, this function allows to resume therun()
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 callrun()
.
- run()[source]¶
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 duringinit_env()
!- Returns
max_trunc_err – The maximal truncation error of a two-site wave function.
- Return type
- sweep(optimize=True)[source]¶
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
- classmethod switch_engine(other_engine, *, options=None, **kwargs)[source]¶
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 theTwoSiteDMRGEngine
to theOneSiteDMRGEngine
.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)[source]¶
Update the left and right environments after an update of the state.
- Parameters
**update_data – Whatever is returned by
update_local()
.