linalg

• full name: tenpy.linalg

• parent module: tenpy

• type: module

Module description

Linear-algebra tools for tensor networks.

Most notably is the module np_conserved, which contains everything needed to make use of charge conservation in the context of tensor networks.

Relevant contents of charges are imported to np_conserved, so you probably won’t need to import charges directly.

Submodules

np_conserved	A module to handle charge conservation in tensor networks.
charges	Basic definitions of a charge.
svd_robust	(More) robust version of singular value decomposition.
random_matrix	Provide some random matrix ensembles for numpy.
sparse	Providing support for sparse algorithms (using matrix-vector products only).
krylov_based	Lanczos algorithm for np_conserved arrays.
truncation	Truncation of Schmidt values.