# TimeEvolutionAlgorithm¶

Inheritance Diagram Methods

 `TimeEvolutionAlgorithm.__init__`(psi, model, ...) Return necessary data to resume a `run()` interrupted at a checkpoint. Resume a run that was interrupted. 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]

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
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.