PurificationApplyMPO

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.purification.PurificationApplyMPO

Methods

PurificationApplyMPO.__init__(psi, U_MPO, …)

Initialize self.

PurificationApplyMPO.environment_sweeps(N_sweeps)

Perform N_sweeps sweeps without optimization to update the environment.

PurificationApplyMPO.get_sweep_schedule()

Define the schedule of the sweep.

PurificationApplyMPO.init_env(U_MPO)

Initialize the environment.

PurificationApplyMPO.make_eff_H()

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

PurificationApplyMPO.post_update_local(…)

Algorithm-specific actions to be taken after local update.

PurificationApplyMPO.prepare_update()

Prepare everything algorithm-specific to perform a local update.

PurificationApplyMPO.reset_stats()

Reset the statistics.

PurificationApplyMPO.resume_run()

Resume a run that was interrupted.

PurificationApplyMPO.run()

Run the compression.

PurificationApplyMPO.sweep([optimize])

One ‘sweep’ of a sweeper algorithm.

PurificationApplyMPO.update_LP(_)

PurificationApplyMPO.update_RP(_)

PurificationApplyMPO.update_local(_[, optimize])

Perform local update.

PurificationApplyMPO.update_new_psi(theta)

Class Attributes and Properties

PurificationApplyMPO.engine_params

PurificationApplyMPO.n_optimize

the number of sites to be optimized over at once.

class tenpy.algorithms.purification.PurificationApplyMPO(psi, U_MPO, options)[source]

Bases: tenpy.algorithms.mps_common.VariationalApplyMPO

Variant of VariationalApplyMPO suitable for purification.

EffectiveH[source]

alias of tenpy.algorithms.purification.PurificationTwoSiteU

update_local(_, optimize=True)[source]

Perform local update.

This simply contracts the environments and theta from the ket to get an updated theta for the bra self.psi (to be changed in place).

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

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. Only those LP and RP that can be used later should be updated.

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 rigth (True) of the current one, and update_LP, update_RP indicate whether it is necessary to update the LP and RP. The latter are chosen such that the environment is growing for infinite systems, but we only keep the minimal number of environment tensors in memory.

Return type

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

init_env(U_MPO)[source]

Initialize the environment.

Parameters

U_MPO (MPO) – The MPO to be applied to the sate.

make_eff_H()[source]

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

property n_optimize

the number of sites to be optimized over 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.

post_update_local(update_data)[source]

Algorithm-specific actions to be taken after local update.

An example would be to collect statistics.

prepare_update()[source]

Prepare everything algorithm-specific to perform a local update.

reset_stats()[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.

option Sweep.sweep_0: int

Number of sweeps that have already been performed.

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. Since most algorithms just have a while loop with break conditions, the default behaviour implemented here is to just call run().

run()[source]

Run the compression.

The state psi is compressed in place.

Returns

max_trunc_err – The maximal truncation error of a two-site wave function.

Return type

TruncationError

sweep(optimize=True)[source]

One ‘sweep’ of a sweeper algorithm.

Iteratate over the bond which is optimized, to the right and then back to the left to the starting point. If optimize=False, don’t actually diagonalize the effective hamiltonian, but only update the environment.

Parameters

optimize (bool, optional) – Whether we actually optimize to find the ground state of the effective Hamiltonian. (If False, just update the environments).

Returns

max_trunc_err – Maximal truncation error introduced.

Return type

float