VariationalApplyMPO¶
full name: tenpy.algorithms.mps_common.VariationalApplyMPO
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 |
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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. |
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.mps_common.VariationalApplyMPO(psi, U_MPO, options, **kwargs)[source]¶
Bases:
VariationalCompression
Variational compression for applying an MPO to an MPS (in place).
To apply an MPO U_MPO to an MPS psi, use
VariationalApplyMPO(psi, U_MPO, options).run()
.The goal is to find a new MPS phi (with N tensors) which is optimally close to
U_MPO|psi>
, i.e. it is normalized and maximizes| <phi|U_MPO|psi> |^2
. The network for this (with M tensors for psi) is given by.——-M[0]—-M[1]—-M[2]—- … —-.| | | | |LP[0]—W[0]—-W[1]—-W[2]—- … — RP[-1]| | | | |.——-N[0]*—N[1]*—N[2]*— … —-.Here LP and RP are the environments with partial contractions, see also
MPOEnvironment
. This algorithms sweeps through the sites, updating 2 N tensors in eachupdate_local()
, say on sites i0 and i1 = i0 +1. We need to maximize:| .-------M[i0]---M[i1]---. | | | | | | LP[i0]--W[i0]---W[i1]---RP[i1] | | | | | | .-------N[i0]*--N[i1]*--.
The optimal solution is given by:
| .-------M[i0]---M[i1]---. | ---N[i0]---N[i1]--- | | | | | | | = SVD of LP[i0]--W[i0]---W[i1]---RP[i1] | | | | | | .----- --.
- Parameters
psi (
MPS
) – The state to whichU_MPO (
MPO
) – MPO to be applied to the state.options (dict) – See
VariationalCompression
.**kwargs – Further keyword arguments as described in the
Sweep
class.
Options
- config VariationalApplyMPO¶
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`.
max_sweeps (from VariationalCompression) in VariationalCompression
Minimum and maximum number of sweeps to perform for the compression.
min_sweeps (from VariationalCompression) in VariationalCompression
Minimum and maximum number of sweeps to perform for the compression.
orthogonal_to (from Sweep) in DMRGEngine.init_env
Deprecated in favor of the `orthogonal_to` function argument (forwarded fro [...]
Number of sweeps to be performed without optimization to update the environment.
start_env_sites (from VariationalCompression) in VariationalCompression
Number of sites to contract for the inital LP/RP environment in case of inf [...]
tol_theta_diff (from VariationalCompression) in VariationalCompression
Stop after less than `max_sweeps` sweeps if the 1-site wave function change [...]
trunc_params (from VariationalCompression) in VariationalCompression
Truncation parameters as described in :cfg:config:`truncation`.
- 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).
- 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.
- 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. If optimize=False, don’t actually diagonalize the effective hamiltonian, but only update the environment.
- 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()
.