Sweep¶
full name: tenpy.algorithms.mps_common.Sweep
parent module:
tenpy.algorithms.mps_common
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 |
Define the schedule of the sweep. |
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(Re-)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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Actually run the algorithm. |
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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 algorithm-specific local update. |
Class Attributes and Properties
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The number of sites to be optimized at once. |
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- class tenpy.algorithms.mps_common.Sweep(psi, model, options, *, orthogonal_to=None, **kwargs)[source]¶
Bases:
Algorithm
Prototype class for a ‘sweeping’ algorithm.
This is a base class, intended to cover common procedures in all algorithms that ‘sweep’ left-right over the MPS (for infinite MPS: over the MPS unit cell). Examples for such algorithms are DMRG, TDVP, and variational compression.
- Parameters
psi – Other parameters as described in
Algorithm
.model – Other parameters as described in
Algorithm
.options – Other parameters as described in
Algorithm
.**kwargs – Other parameters as described in
Algorithm
.orthogonal_to (None | list of
MPS
| list of dict) – States to orthogonalize against, seeinit_env()
.
Options
- config Sweep¶
option summary chi_list in Sweep.reset_stats
By default (``None``) this feature is disabled. [...]
chi_list_reactivates_mixer in Sweep.sweep
If True, the mixer is reset/reactivated each time the bond dimension growth [...]
Whether to combine legs into pipes. This combines the virtual and [...]
init_env_data in DMRGEngine.init_env
Dictionary as returned by ``self.env.get_initialization_data()`` from [...]
Lanczos parameters as described in :cfg:config:`Lanczos`.
mixer in DMRGEngine.mixer_activate
Specifies which :class:`Mixer` to use, if any. [...]
mixer_params in DMRGEngine.mixer_activate
Mixer parameters as described in :cfg:config:`Mixer`.
orthogonal_to in DMRGEngine.init_env
Deprecated in favor of the `orthogonal_to` function argument (forwarded fro [...]
start_env in DMRGEngine.init_env
Number of sweeps to be performed without optimization to update the environment.
trunc_params (from Algorithm) in Algorithm
Truncation parameters as described in :cfg:config:`truncation`.
- option 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, \(O(2 d^3 \chi^3 D)\).
- option lanczos_params: dict¶
Lanczos parameters as described in
Lanczos
.
- EffectiveH¶
Class attribute; a subclass of
EffectiveH
. It’s length attribute determines how many sites are optimized/updated at once, see alson_optimize
.- Type
class
- env¶
Environment for contraction
<psi|H|psi>
.- Type
- i0¶
Only set during sweep. Left-most of the EffectiveH.length sites to be updated in
update_local()
.- Type
- move_right¶
Only set during sweep. Whether the next i0 of the sweep will be right (True), left (False) or at the same position (None) as the current one.
- Type
bool | None
- update_LP_RP¶
Only set during a sweep, see
get_sweep_schedule()
. Indicates whether it is necessary to update the LP and RP inupdate_env()
.
- ortho_to_envs¶
List of environments
<psi|psi_ortho>
, where psi_ortho is an MPS to orthogonalize against.- Type
list of
MPSEnvironment
- shelve¶
If a simulation runs out of time (time.time() - start_time > max_seconds), the run will terminate with shelve = True.
- Type
- chi_list¶
A dictionary to gradually increase the chi_max parameter of trunc_params. See
Sweep.chi_list
- Type
dict |
None
- 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
- 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
.
- 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. IfNone
, 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 theMPSEnvironment
; thepsi
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 thatresume_data['orthogonal_to']
takes precedence over the argument.
Options
Deprecated since version 0.6.0: Options LP, LP_age, RP and RP_age are now collected in a dictionary init_env_data with different keys init_LP, init_RP, age_LP, age_RP
Deprecated since version 0.8.0: Instead of passing the init_env_data as a option, it should be passed as dict entry of resume_data.
- option Sweep.init_env_data: dict¶
Dictionary as returned by
self.env.get_initialization_data()
fromget_initialization_data()
. Deprecated, use the resume_data function/class argument instead.
-
option Sweep.orthogonal_to: list of
MPS
¶ Deprecated in favor of the orthogonal_to function argument (forwarded from the class argument) with the same effect.
- 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.
- 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.
- 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
- 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
- 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 theself.EffectiveH.length
sites to be updated inupdate_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
- 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
- update_local(theta, **kwargs)[source]¶
Perform algorithm-specific local update.
For two-site algorithms with
n_optimize
= 2, this always optimizes the sitesi0
and i0 + 1. For single-site algorithms, the effective H only acts on site i0, but afterwards it also updates the bond to the right ifmove_right
is True, or the bond to the left ifmove_right
is False. Since the svd for truncation gives tensors to be multiplied into the tensors on both sides of the bond, tensors of two sites are updated even for single-site algorithms: when right-moving, site i0 + 1 is also updated; site i0 - 1 when left-moving.- Parameters
theta (
Array
) – Local single- or two-site wave function, as returned byprepare_update_local()
.- Returns
update_data – Data to be processed by
update_env()
andpost_update_local()
, e.g. containing the truncation error as err. Ifcombine
is set, it should also contain the U and VH from the SVD.- Return type
- 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()
.
- post_update_local(err, **update_data)[source]¶
Algorithm-specific actions to be taken after local update.
An example would be to collect statistics.
- 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.
- 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_deactivate()[source]¶
Deactivate the mixer.
Set
self.mixer=None
and revert any other effects ofmixer_acitvate()
.
- 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.
- 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()
.
- 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()
.