VariationalApplyMPO¶

full name: tenpy.algorithms.mps_common.VariationalApplyMPO
parent module: tenpy.algorithms.mps_common
type: class

Inheritance Diagram

Methods

`VariationalApplyMPO.__init__`(psi, U_MPO, ...)
`VariationalApplyMPO.environment_sweeps`(N_sweeps)	Perform N_sweeps sweeps without optimization to update the environment.
`VariationalApplyMPO.free_no_longer_needed_envs`()	Remove no longer needed environments after an update.
`VariationalApplyMPO.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`VariationalApplyMPO.get_sweep_schedule`()	Define the schedule of the sweep.
`VariationalApplyMPO.init_env`(U_MPO[, ...])	Initialize the environment.
`VariationalApplyMPO.make_eff_H`()	Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.
`VariationalApplyMPO.post_update_local`(err, ...)	Algorithm-specific actions to be taken after local update.
`VariationalApplyMPO.prepare_update_local`()	Prepare self for calling `update_local()`.
`VariationalApplyMPO.reset_stats`([resume_data])	Reset the statistics.
`VariationalApplyMPO.resume_run`()	Resume a run that was interrupted.
`VariationalApplyMPO.run`()	Run the compression.
`VariationalApplyMPO.sweep`([optimize])	One 'sweep' of a sweeper algorithm.
`VariationalApplyMPO.switch_engine`(...[, options])	Initialize algorithm from another algorithm instance of a different class.
`VariationalApplyMPO.update_env`(**update_data)	Update the left and right environments after an update of the state.
`VariationalApplyMPO.update_local`(_[, optimize])	Perform local update.
`VariationalApplyMPO.update_new_psi`(theta)	Given a new two-site wave function theta, split it and save it in `psi`.

Class Attributes and Properties

`VariationalApplyMPO.engine_params`
`VariationalApplyMPO.n_optimize`	The number of sites to be optimized at once.
`VariationalApplyMPO.verbose`

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().

.——-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 each update_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 which
U_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
chi_list (from Sweep) in Sweep.reset_stats	By default (``None``) this feature is disabled. [...]
combine (from Sweep) in Sweep	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 [...]
start_env (from Sweep) in DMRGEngine.init_env	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`.

renormalize¶

Used to keep track of renormalization in the last sweep for psi.norm.

Type: list

EffectiveH[source]¶: alias of TwoSiteH

init_env(U_MPO, resume_data=None, orthogonal_to=None)[source]¶

Initialize the environment.

Parameters

U_MPO (MPO) – The MPO to be applied to the sate.
resume_data (dict) – May contain init_env_data.
orthogonal_to – Ignored.

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 save psi, model and options 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: dict

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.

make_eff_H()[source]¶: Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.

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 the TwoSiteH and hence has n_optimize=2, while the SingleSiteDMRGEngine has n_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: Array

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 for trunc_params['chi_max']. For example chi_list={0: 50, 20: 100} uses chi_max=50 for the first 20 sweeps and chi_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 the run() 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 call run().

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 during init_env()!

Returns: max_trunc_err – The maximal truncation error of a two-site wave function.
Return type: TruncationError

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.

Parameters: optimize (bool, optional) – Whether we actually optimize to find the ground state of the effective Hamiltonian. (If False, just update the environments).
Returns: max_trunc_err – Maximal truncation error introduced.
Return type: float

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 the TwoSiteDMRGEngine to the OneSiteDMRGEngine.
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().

update_new_psi(theta)[source]¶: Given a new two-site wave function theta, split it and save it in psi.