OldTDVPEngine

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.tdvp.OldTDVPEngine

Methods

OldTDVPEngine.__init__(psi, model, options)

OldTDVPEngine.estimate_RAM([mem_saving_factor])

Gives an approximate prediction for the required memory usage.

OldTDVPEngine.evolve(N_steps, dt)

Evolve by N_steps*dt.

OldTDVPEngine.evolve_step(dt)

OldTDVPEngine.get_resume_data([...])

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

OldTDVPEngine.prepare_evolve(dt)

Prepare an evolution step.

OldTDVPEngine.resume_run()

Resume a run that was interrupted.

OldTDVPEngine.run()

(Real-)time evolution with TDVP.

OldTDVPEngine.run_evolution(N_steps, dt)

Perform a (real-)time evolution of psi by N_steps * dt.

OldTDVPEngine.run_one_site([N_steps])

Run the TDVP algorithm with the one site algorithm.

OldTDVPEngine.run_two_sites([N_steps])

Run the TDVP algorithm with two sites update.

OldTDVPEngine.sweep_left_right()

Performs the sweep left->right of the second order TDVP scheme with one site update.

OldTDVPEngine.sweep_left_right_two()

Performs the sweep left->right of the second order TDVP scheme with two sites update.

OldTDVPEngine.sweep_right_left()

Performs the sweep right->left of the second order TDVP scheme with one site update.

OldTDVPEngine.sweep_right_left_two()

Performs the sweep left->right of the second order TDVP scheme with two sites update.

OldTDVPEngine.switch_engine(other_engine, *)

Initialize algorithm from another algorithm instance of a different class.

OldTDVPEngine.theta_svd_left_right(theta)

Performs the SVD from left to right.

OldTDVPEngine.theta_svd_right_left(theta)

Performs the SVD from right to left.

OldTDVPEngine.update_s_h0(s, H, dt)

Update with the zero site Hamiltonian (update of the singular value)

OldTDVPEngine.update_theta_h1(Lp, Rp, theta, ...)

Update with the one site Hamiltonian.

OldTDVPEngine.update_theta_h2(Lp, Rp, theta, ...)

Update with the two sites Hamiltonian.

Class Attributes and Properties

OldTDVPEngine.TDVP_params

OldTDVPEngine.time_dependent_H

whether the algorithm supports time-dependent H

OldTDVPEngine.verbose

class tenpy.algorithms.tdvp.OldTDVPEngine(psi, model, options, environment=None, **kwargs)[source]

Bases: TimeEvolutionAlgorithm

Time dependent variational principle algorithm for MPS.

Deprecated since version 0.10.0: Replace this engine with the new TDVPEngine.

Deprecated since version 0.6.0: Renamed parameter/attribute TDVP_params to options.

Parameters:
  • psi – Same as for Algorithm.

  • model – Same as for Algorithm.

  • options – Same as for Algorithm.

  • **kwargs – Same as for Algorithm.

  • environment – Initial environment. If None (default), it will be calculated at the beginning.

Options

config TDVP
option summary

active_sites

The number of active sites to be used for the time evolution. [...]

dt (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm

Minimal time step by which to evolve.

lanczos_options

Lanczos options as described in :cfg:config:`Lanczos`.

N_steps (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm

Number of time steps `dt` to evolve by in :meth:`run`. [...]

preserve_norm (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm

Whether the state will be normalized to its initial norm after each time st [...]

start_time (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm

Initial value for :attr:`evolved_time`.

trunc_params

Truncation parameters as described in :func:`~tenpy.algorithms.truncation.t [...]

option active_sites

The number of active sites to be used for the time evolution. If set to 1, run_one_site() is used. The bond dimension will not increase! If set to 2, run_two_sites() is used.

option trunc_params: dict

Truncation parameters as described in truncate()

option lanczos_options: dict

Lanczos options as described in Lanczos.

options

Optional parameters.

Type:

dict

evolved_time

Indicating how long psi has been evolved, psi = exp(-i * evolved_time * H) psi(t=0).

Type:

float | complex

psi

The MPS, time evolved in-place.

Type:

MPS

environment

The environment, storing the LP and RP to avoid recalculations.

Type:

MPOEnvironment

lanczos_options

Options passed on to LanczosEvolution.

Type:

Config

run()[source]

(Real-)time evolution with TDVP.

run_one_site(N_steps=None)[source]

Run the TDVP algorithm with the one site algorithm.

Warning

Be aware that the bond dimension will not increase!

Parameters:

N_steps (integer. Number of steps) –

run_two_sites(N_steps=None)[source]

Run the TDVP algorithm with two sites update.

The bond dimension will increase. Truncation happens at every step of the sweep, according to the parameters set in trunc_params.

Parameters:

N_steps (integer. Number of steps) –

sweep_left_right()[source]

Performs the sweep left->right of the second order TDVP scheme with one site update.

Evolve from 0.5*dt.

sweep_left_right_two()[source]

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Evolve from 0.5*dt

sweep_right_left()[source]

Performs the sweep right->left of the second order TDVP scheme with one site update.

Evolve from 0.5*dt

sweep_right_left_two()[source]

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Evolve from 0.5*dt

update_theta_h1(Lp, Rp, theta, W, dt)[source]

Update with the one site Hamiltonian.

Parameters:
  • Lp (Array) – tensor representing the left environment

  • Rp (Array) – tensor representing the right environment

  • theta (Array) – the theta tensor which needs to be updated

  • W (Array) – MPO which is applied to the ‘p’ leg of theta

update_theta_h2(Lp, Rp, theta, W0, W1, dt)[source]

Update with the two sites Hamiltonian.

Parameters:
theta_svd_left_right(theta)[source]

Performs the SVD from left to right.

Parameters:

theta (tenpy.linalg.np_conserved.Array) – the theta tensor on which the SVD is applied

theta_svd_right_left(theta)[source]

Performs the SVD from right to left.

Parameters:

theta (tenpy.linalg.np_conserved.Array,) – The theta tensor on which the SVD is applied

update_s_h0(s, H, dt)[source]

Update with the zero site Hamiltonian (update of the singular value)

Parameters:
  • s (tenpy.linalg.np_conserved.Array) – representing the singular value matrix which is updated

  • H (H0_mixed) – zero site Hamiltonian that we need to apply on the singular value matrix

  • dt (complex number) – time step of the evolution

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.

evolve(N_steps, dt)[source]

Evolve by N_steps*dt.

Subclasses may override this with a more efficient way of do N_steps update_step.

Parameters:
  • N_steps (int) – The number of time steps by dt to take at once.

  • dt (float) – Small time step. Might be ignored if already used in prepare_update().

Returns:

trunc_err – Sum of truncation errors introduced during evolution.

Return type:

TruncationError

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

prepare_evolve(dt)[source]

Prepare an evolution step.

This method is used to prepare repeated calls of evolve() given the model. For example, it may generate approximations of U=exp(-i H dt). To avoid overhead, it may cache the result depending on parameters/options; but it should always regenerate it if force_prepare_evolve is set.

Parameters:

dt (float) – The time step to be used.

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_evolution(N_steps, dt)[source]

Perform a (real-)time evolution of psi by N_steps * dt.

This is the inner part of run() without the logging. For parameters see TimeEvolutionAlgorithm.

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().

time_dependent_H = False

whether the algorithm supports time-dependent H