Sweep

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.mps_common.Sweep

Methods

Sweep.__init__(psi, model, options, *[, ...])

Sweep.environment_sweeps(N_sweeps)

Perform N_sweeps sweeps without optimization to update the environment.

Sweep.estimate_RAM([mem_saving_factor])

Gives an approximate prediction for the required memory usage.

Sweep.free_no_longer_needed_envs()

Remove no longer needed environments after an update.

Sweep.get_resume_data([sequential_simulations])

Return necessary data to resume a run() interrupted at a checkpoint.

Sweep.get_sweep_schedule()

Define the schedule of the sweep.

Sweep.init_env([model, resume_data, ...])

(Re-)initialize the environment.

Sweep.make_eff_H()

Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.

Sweep.mixer_activate()

Set self.mixer to the class specified by options['mixer'].

Sweep.mixer_cleanup()

Cleanup the effects of a mixer.

Sweep.mixer_deactivate()

Deactivate the mixer.

Sweep.post_update_local(err, **update_data)

Algorithm-specific actions to be taken after local update.

Sweep.prepare_update_local()

Prepare self for calling update_local().

Sweep.reset_stats([resume_data])

Reset the statistics.

Sweep.resume_run()

Resume a run that was interrupted.

Sweep.run()

Actually run the algorithm.

Sweep.sweep([optimize])

One 'sweep' of a sweeper algorithm.

Sweep.switch_engine(other_engine, *[, options])

Initialize algorithm from another algorithm instance of a different class.

Sweep.update_env(**update_data)

Update the left and right environments after an update of the state.

Sweep.update_local(theta, **kwargs)

Perform algorithm-specific local update.

Class Attributes and Properties

Sweep.DefaultMixer

Sweep.S_inv_cutoff

Sweep.engine_params

Sweep.n_optimize

The number of sites to be optimized at once.

Sweep.use_mixer_by_default

Sweep.verbose

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, see init_env().

Options

config Sweep
option summary

chi_list in IterativeSweeps.reset_stats

By default (``None``) this feature is disabled. [...]

chi_list_reactivates_mixer in IterativeSweeps.sweep

If True, the mixer is reset/reactivated each time the bond dimension growth [...]

combine

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_params

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 also n_optimize.

Type:

class

E_trunc_list

List of truncation energies throughout a sweep.

Type:

list

env

Environment for contraction <psi|H|psi>.

Type:

MPOEnvironment

finite

Whether the MPS boundary conditions are finite (True) or infinite (False)

Type:

bool

i0

Only set during sweep. Left-most of the EffectiveH.length sites to be updated in update_local().

Type:

int

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 in update_env().

Type:

(bool, bool)

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:

bool

sweeps

The number of sweeps already performed.

Type:

int

S_inv_cutoff

Cutoff for singular values when taking inverses of them is required.

Type:

float

time0

Time marker for the start of the run.

Type:

float

trunc_err_list

List of truncation errors from the last sweep.

Type:

list

chi_list

A dictionary to gradually increase the chi_max parameter of trunc_params. See Sweep.chi_list

Type:

dict | None

mixer

If None, no mixer is used (anymore), otherwise the mixer instance.

Type:

Mixer | None

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

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.

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. If None, 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 the MPSEnvironment; the psi 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 that resume_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() from get_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 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.

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.

Iterate 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:

float

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 the self.EffectiveH.length sites to be updated in update_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:

iterable of (int, bool, (bool, bool))

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

make_eff_H()[source]

Create new instance of self.EffectiveH at self.i0 and set it to self.eff_H.

update_local(theta, **kwargs)[source]

Perform algorithm-specific local update.

For two-site algorithms with n_optimize = 2, this always optimizes the sites i0 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 if move_right is True, or the bond to the left if move_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 by prepare_update_local().

Returns:

update_data – Data to be processed by update_env() and post_update_local(), e.g. containing the truncation error as err. If combine is set, it should also contain the U and VH from the SVD.

Return type:

dict

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 optimization 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 as Mixer and is used to instantiate the mixer. None uses no mixer. True uses the mixer specified by the DefaultMixer class attribute. The default depends on the subclass of Sweep.

option Sweep.mixer_params: dict

Mixer parameters as described in Mixer.

See also

mixer_deactivate

mixer_deactivate()[source]

Deactivate the mixer.

Set self.mixer=None and revert any other effects of mixer_activate().

mixer_cleanup()[source]

Cleanup the effects of a mixer.

A sweep() with an enabled Mixer leaves the MPS psi with 2D arrays in S. To recover the original form, this function simply performs one sweep with disabled mixer.

estimate_RAM(mem_saving_factor=None)[source]

Gives an approximate prediction for the required memory usage.

This calculation is based on the requested bond dimension, the local Hilbert space dimension, the number of sites, and the boundary conditions.

Parameters:

mem_saving_factor (float) – Represents the amount of RAM saved due to conservation laws. By default, it is ‘None’ and is extracted from the model automatically. However, this is only possible in a few cases and needs to be estimated in most cases. This is due to the fact that it is dependent on the model parameters. If one has a better estimate, one can pass the value directly. This value can be extracted by building the initial state psi (usually by performing DMRG) and then calling print(psi.get_B(0).sparse_stats()) TeNPy will automatically print the fraction of nonzero entries in the first line, for example, 6 of 16 entries (=0.375) nonzero. This fraction corresponds to the mem_saving_factor; in our example, it is 0.375.

Returns:

usage – Required RAM in MB.

Return type:

float

See also

tenpy.simulations.simulation.estimate_simulation_RAM

global function calling this.

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 behavior implemented here is to just call run().

run()[source]

Actually run the algorithm.

Needs to be implemented in subclasses.

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().