Algorithm¶
full name: tenpy.algorithms.algorithm.Algorithm
parent module:
tenpy.algorithms.algorithm
type: class
Inheritance Diagram

Methods
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Return necessary data to resume a |
Resume a run that was interrupted. |
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Actually run the algorithm. |
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Initialize algorithm from another algorithm instance of a different class. |
Class Attributes and Properties
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- class tenpy.algorithms.algorithm.Algorithm(psi, model, options, *, resume_data=None, cache=None)[source]¶
Bases:
object
Base class and common interface for a tensor-network based algorithm in TeNPy.
- Parameters
psi – Tensor network to be updated by the algorithm.
model (
Model
| None) – Model with the representation of the hamiltonian suitable for the algorithm. None for algorithms which don’t require a model.options (dict-like) – Optional parameters for the algorithm. In the online documentation, you can find the correct set of options in the Config Index.
resume_data (None | dict) – Can only be passed as keyword argument. By default (
None
) ignored. If a dict, it should contain the data returned byget_resume_data()
when intending to continue/resume an interrupted run. If it contains psi, this takes precedence over the argument psi.cache (None |
DictCache
) – The cache to be used to reduce memory usage. None defaults to a new, trivialDictCache
which keeps everything in RAM.
Options
- config Algorithm¶
option summary Truncation parameters as described in :cfg:config:`truncation`.
- option trunc_params: dict¶
Truncation parameters as described in
truncation
.
- psi¶
Tensor network to be updated by the algorithm.
- checkpoint¶
An event that the algorithm emits at regular intervals when it is in a “well defined” step, where an intermediate status report, measurements and/or interrupting and saving to disk for later resume make sense.
- Type
- cache¶
The cache to be used.
- Type
DictCache
or subclass
- resume_data¶
Data given as parameter resume_data and/or to be returned by
get_resume_data()
.- Type
- _resume_psi¶
Possibly a copy of psi to be used for
get_resume_data()
.
- 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()
.
- 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()
.
- 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