[0.5.0] - 2019-12-18

Backwards incompatible changes

• Major rewriting of the DMRG Engines, see issue #39 and issue #85 for details. The EngineCombine and EngineFracture have been combined into a single TwoSiteDMRGEngine with an The run function works as before. In case you have directly used the EngineCombine or EngineFracture, you should update your code and use the TwoSiteEngine instead.

• Moved init_LP and init_RP method from MPS into MPSEnvironment and MPOEnvironment.

Changed

• Addition/subtraction of Array: check whether the both arrays have the same labels in differnt order, and in that case raise a warning that we will transpose in the future.

• Made tenpy.linalg.np_conserved.Array.get_block() public (previously tenpy.linalg.np_conserved.Array._get_block).

• groundstate() now returns a tuple (E0, psi0) instead of just psi0. Moreover, the argument charge_sector was added.

• Simplification in the Lattice: Instead of having separate arguments/attributes/functions for 'nearest_neighbors', 'next_nearest_neighbors', 'next_next_nearest_neighbors' and possibly (Honeycomb) even 'fourth_nearest_neighbors', 'fifth_nearest_neighbors', collect them in a dictionary called pairs. Old call structures still allowed, but deprecated.

• issue #94: Array addition and inner() should reflect the order of the labels, if they coincided. Will change the default behaviour in the future, raising FutureWarning for now.

• Default parameter for DMRG params: increased precision by setting P_tol_min down to the maximum of 1.e-30, lanczos_params['svd_min']**2 * P_tol_to_trunc, lanczos_params['trunc_cut']**2 * P_tol_to_trunc by default.

Added

• tenpy.algorithms.mps_common with the Sweep class and EffectiveH to be a OneSiteH or TwoSiteH.

• Single-Site DMRG with the SingleSiteDMRG.

• Example function in examples/c_tebd.py how to run TEBD with a model originally having next-nearest neighbors.

• increase_L() to allow increasing the unit cell of an MPS.

• Additional option order='folded' for the Chain.

• tenpy.algorithms.exact_diag.ExactDiag.from_H_mpo() wrapper as replacement for tenpy.networks.mpo.MPO.get_full_hamiltonian() and tenpy.networks.mpo.MPO.get_grouped_mpo(). The latter are now deprecated.

• Argument max_size to limit the matrix dimension in ExactDiag.

• tenpy.linalg.sparse.FlatLinearOperator.from_guess_with_pipe() to allow quickly converting matvec functions acting on multi-dimensional arrays to a FlatLinearOperator by combining the legs into a LegPipe.

• tenpy.tools.math.speigsh() for hermitian variant of speigs()

• Allow for arguments 'LA', 'SA' in argsort().

• tenpy.linalg.lanczos.lanczos_arpack() as possiple replacement of the self-implemented lanczos function.

• tenpy.algorithms.dmrg.full_diag_effH() as another replacement of lanczos().

• The new DMRG parameter 'diag_method' allows to select a method for the diagonalization of the effective Hamiltonian. See tenpy.algorithms.dmrg.DMRGEngine.diag() for details.

• dtype attribute in EffectiveH.

• tenpy.linalg.charges.LegCharge.get_qindex_of_charges() to allow selecting a block of an Array from the charges.

• tenpy.algorithms.mps_common.EffectiveH.to_matrix to allow contracting an EffectiveH to a matrix, as well as metadata tenpy.linalg.sparse.NpcLinearOperator.acts_on and tenpy.algorithms.mps_common.EffectiveH.N.

• argument only_physical_legs in tenpy.networks.mps.MPS.get_total_charge()

Fixed

• MPO expectation_value() did not work for finite systems.

• Calling compute_K() repeatedly with default parameters but on states with different chi would use the chi of the very first call for the truncation parameters.

• allow MPSEnvironment and MPOEnvironment to have MPS/MPO with different length

• group_sites() didn’t work correctly in some situations.

• matvec_to_array() returned the transposed of A.

• tenpy.networks.mps.MPS.from_full() messed up the form of the first array.

• issue #95: blowup of errors in DMRG with update_env > 0. Turns out to be a problem in the precision of the truncation error: TruncationError.eps was set to 0 if it would be smaller than machine precision. To fix it, I added from_S().