VariationalCompression¶
full name: tenpy.algorithms.mps_common.VariationalCompression
parent module:
tenpy.algorithms.mps_common
type: class
Inheritance Diagram
Methods
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Initialize self. |
Perform N_sweeps sweeps without optimization to update the environment. |
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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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Algorithm-specific actions to be taken after local update. |
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Prepare everything algorithm-specific to perform a local update. |
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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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Perform local update. |
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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 over at once. |
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-
class
tenpy.algorithms.mps_common.
VariationalCompression
(psi, options, resume_data=None)[source]¶ Bases:
tenpy.algorithms.mps_common.Sweep
Variational compression of an MPS (in place).
To compress an MPS psi, use
VariationalCompression(psi, options).run()
.The algorithm is the same as described in
VariationalApplyMPO
, except that we dont have an MPO in the networks - one can think of the MPO being trivial.- Parameters
psi (
MPS
) – The state to be compressed.options (dict) – See
VariationalCompression
.resume_data (None | dict) – By default (
None
) ignored. If a dict, it should contain the data returned byget_resume_data()
when intending to continue/resume an interrupted run, in particular ‘init_env_data’.
Options
-
config
VariationalCompression
¶ option summary By default (``None``) this feature is disabled. [...]
Whether to combine legs into pipes. This combines the virtual and [...]
init_env_data (from Sweep) in DMRGEngine.init_env
Dictionary as returned by ``self.env.get_initialization_data()`` from [...]
lanczos_params (from Sweep) in Sweep
Lanczos parameters as described in :cfg:config:`Lanczos`.
Number of sweeps to perform.
orthogonal_to (from Sweep) in DMRGEngine.init_env
List of other matrix product states to orthogonalize against. [...]
Number of sweeps to be performed without optimization to update [...]
Number of sweeps that have already been performed.
Truncation parameters as described in :cfg:config:`truncation`.
-
option
trunc_params
: dict¶ Truncation parameters as described in
truncation
.
-
option
N_sweeps
: int¶ Number of sweeps to perform.
-
option
-
EffectiveH
[source]¶ alias of
tenpy.algorithms.mps_common.TwoSiteH
-
run
()[source]¶ Run the compression.
The state
psi
is compressed in place.- Returns
max_trunc_err – The maximal truncation error of a two-site wave function.
- Return type
-
init_env
(_, resume_data=None)[source]¶ Initialize the environment.
The first argument is not used and only there for compatibility with the Sweep class. The second argument is the resume_data passed during initialization, as returned by
get_resume_data()
.
-
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
-
get_resume_data
()[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(resume_data)
An algorithm which doesn’t support this should override resume_run to raise an Error.
- 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.
- 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. Only those LP and RP that can be used later should be updated.
- 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 rigth (True) of the current one, and update_LP, update_RP indicate whether it is necessary to update the LP and RP. The latter are chosen such that the environment is growing for infinite systems, but we only keep the minimal number of environment tensors in memory.- Return type
-
property
n_optimize
¶ the number of sites to be optimized over 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
(update_data)[source]¶ Algorithm-specific actions to be taken after local update.
An example would be to collect statistics.
-
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.
-
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.
-
option
-
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()
.