TimeEvolutionAlgorithm¶

full name: tenpy.algorithms.algorithm.TimeEvolutionAlgorithm
parent module: tenpy.algorithms.algorithm
type: class

Inheritance Diagram

Methods

`TimeEvolutionAlgorithm.__init__`(psi, model, ...)
`TimeEvolutionAlgorithm.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`TimeEvolutionAlgorithm.resume_run`()	Resume a run that was interrupted.
`TimeEvolutionAlgorithm.run`()	Perform a real-time evolution of `psi` by N_steps`*`dt.

Class Attributes and Properties

`TimeEvolutionAlgorithm.time_dependent_H`	whether the algorithm supports time-dependent H
`TimeEvolutionAlgorithm.verbose`

class tenpy.algorithms.algorithm.TimeEvolutionAlgorithm(psi, model, options, **kwargs)[source]¶

Bases: tenpy.algorithms.algorithm.Algorithm

Common interface for (real) time evolution algorithms.

Parameters are the same as for Algorithm.

Options

config TimeEvolutionAlgorithm¶

option	summary
dt	Minimal time step by which to evolve.
N_steps	Number of time steps `dt` to evolve by in :meth:`run`. [...]
start_time	Initial value for :attr:`evolved_time`.
trunc_params (from Algorithm) in Algorithm	Truncation parameters as described in :cfg:config:`truncation`.

option start_time: float¶: Initial value for evolved_time.

option dt: float¶: Minimal time step by which to evolve.

option N_steps: int¶: Number of time steps dt to evolve by in run(). Adjusting dt and N_steps at the same time allows to keep the evolution time done in run() fixed. Further, e.g., the Trotter decompositions of order > 1 are slightly more efficient if more than one step is performed at once.

evolved_time¶

Indicating how long psi has been evolved, psi = exp(-i * evolved_time * H) psi(t=0). Not that the real-part of t is increasing for a real-time evolution, while the imaginary-part of t is decreasing for a imaginary time evolution.

Type: float | complex

time_dependent_H = False¶: whether the algorithm supports time-dependent H

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

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]¶

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

You probably want to call this in a loop.