# truncate

tenpy.algorithms.truncation.truncate(S, options)[source]

Given a Schmidt spectrum S, determine which values to keep.

Options

config truncation
option summary

chi_max

Keep at most `chi_max` Schmidt values.

chi_min

Keep at least `chi_min` Schmidt values.

degeneracy_tol

Don't cut between neighboring Schmidt values with [...]

svd_min

Discard all small Schmidt values ``S[i] < svd_min``.

trunc_cut

Discard all small Schmidt values as long as [...]

option chi_max: int

Keep at most chi_max Schmidt values.

option chi_min: int

Keep at least chi_min Schmidt values.

option degeneracy_tol: float

Don’t cut between neighboring Schmidt values with `|log(S[i]/S[j])| < degeneracy_tol`, or equivalently `|S[i] - S[j]|/S[j] < exp(degeneracy_tol) - 1 ~= degeneracy_tol` for small degeneracy_tol. In other words, keep either both i and j or none, if the Schmidt values are degenerate with a relative error smaller than degeneracy_tol, which we expect to happen in the case of symmetries.

option svd_min: float

Discard all small Schmidt values `S[i] < svd_min`.

option trunc_cut: float

Discard all small Schmidt values as long as `sum_{i discarded} S[i]**2 <= trunc_cut**2`.

Parameters:
• S (1D array) – Schmidt values (as returned by an SVD), not necessarily sorted. Should be normalized to `np.sum(S*S) == 1.`.

• options (dict-like) – Config with constraints for the truncation, see `truncation`. If a constraint can not be fulfilled (without violating a previous one), it is ignored. A value `None` indicates that the constraint should be ignored.

Returns:

• mask (1D bool array) – Index mask, True for indices which should be kept.

• norm_new (float) – The norm of the truncated Schmidt values, `np.linalg.norm(S[mask])`. Useful for re-normalization.

• err (`TruncationError`) – The error of the represented state which is introduced due to the truncation.