Engine¶
full name: tenpy.algorithms.tdvp.Engine
parent module:
tenpy.algorithms.tdvp
type: class
Inheritance Diagram
Methods
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Initialize self. |
Return necessary data to resume a |
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Resume a run that was interrupted. |
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(Real-)time evolution with TDVP. |
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Run the TDVP algorithm with the one site algorithm. |
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Run the TDVP algorithm with two sites update. |
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Relabel the svd. |
Performs the sweep left->right of the second order TDVP scheme with one site update. |
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Performs the sweep left->right of the second order TDVP scheme with two sites update. |
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Performs the sweep right->left of the second order TDVP scheme with one site update. |
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Performs the sweep left->right of the second order TDVP scheme with two sites update. |
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Performs the SVD from left to right. |
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Performs the SVD from right to left. |
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Update with the zero site Hamiltonian (update of the singular value) |
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Update with the one site Hamiltonian. |
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Update with the two sites Hamiltonian. |
Class Attributes and Properties
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class
tenpy.algorithms.tdvp.
Engine
(psi, model, options)[source]¶ Bases:
tenpy.algorithms.tdvp.TDVPEngine
Deprecated old name of
TDVPEngine
.-
get_resume_data
()[source]¶ Return necessary data to resume a
run()
interrupted at a checkpoint.At a
checkpoint
, you can savepsi
,model
andoptions
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(resume_data)
An algorithm which doesn’t support this should override resume_run to raise an Error.
- 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.
- Return type
-
resume_run
()[source]¶ Resume a run that was interrupted.
In case we saved an intermediate result at a
checkpoint
, this function allows to resume therun()
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 callrun()
.
-
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) –
-
set_anonymous_svd
(U, new_label)[source]¶ Relabel the svd.
- Parameters
U (
tenpy.linalg.np_conserved.Array
) – the tensor which lacks a leg_label
-
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
-
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 updatedH (H0_mixed) – zero site Hamiltonian that we need to apply on the singular value matrix
dt (complex number) – time step of the evolution
-
update_theta_h2
(Lp, Rp, theta, W0, W1, dt)[source]¶ Update with the two sites Hamiltonian.
- Parameters
Lp (
tenpy.linalg.np_conserved.Array
) – tensor representing the left environmentRp (
tenpy.linalg.np_conserved.Array
) – tensor representing the right environmenttheta (
tenpy.linalg.np_conserved.Array
) – the theta tensor which needs to be updatedW (
tenpy.linalg.np_conserved.Array
) – MPO which is applied to the ‘p0’ leg of thetaW1 (
tenpy.linalg.np_conserved.Array
) – MPO which is applied to the ‘p1’ leg of theta
-