# Tensor train module

Kronecker product expressions appear naturally in spin dynamics because the state space of a multi-spin system is a direct product of the state spaces of individual spins. A simple example is provided by Cartesian projection operators:

[math]\hat{S}_{\left\{ \text{X,Y,Z} \right\}}^{\left( n \right)}=\mathbf{1}\otimes \mathbf{1}\otimes ...\otimes {{\hat{\sigma }}_{\left\{ \text{X,Y,Z} \right\}}}\otimes ...\otimes \mathbf{1}\otimes \mathbf{1}[/math]

where [math]\mathbf{1}[/math] denotes a unit matrix of appropriate dimension and the Pauli matrices [math]\left\{ {{{\hat{\sigma }}}_{\text{X}}},{{{\hat{\sigma }}}_{\text{Y}}},{{{\hat{\sigma }}}_{\text{Z}}} \right\}[/math] occur in the n-th position of the direct product sequence. If the Kronecker products are opened, the matrix is huge. But it is often possible to leave them un-opened.

Although such representations have been known for a long time, they are rather hard to work with. In *Spinach*, we made an attempt. Two types of unopened Kronecker product formats are supported - tensor trains and polyadics. Both are very space-efficient: enormous matrices can be packed into a few kilobytes. However, specialist knowledge of numerical linear algebra is required - this is deep water.

## Creating tensor train and polyadic objects

The function that creates tensor trains is ttclass.m. Use polyadic.m for polyadics.

## Tensor train functions

ttclass/amensolve.m - solves the linear system Ax=y using the AMEn iteration.

ttclass/amensum.m - tensor train summation without loss of accuracy.

ttclass/clearcoeff.m - absorbs the physical coefficient of the tensor train into its cores.

ttclass/conj.m - element-wise conjugation of a matrix in a TT representation.

ttclass/ctranspose.m - conjugate-transpose of a matrix in a TT representation.

ttclass/diag.m – mimics diag for matrices. If an input is ttclass matrix, returns a ttclass vector (matrix with 1 column) containing the diagonal. If an input is a vector, returns a diagonal matrix.

ttclass/dot.m – mimics the dot product operation for matrices.

ttclass/full.m – mimics the corresponding operation for sparse matrices. Computes all Kronecker products and returns full matrix. May result in out-of-memory errors for large matrices.

ttclass/hdot.m – the Hadamard dot product operation.

ttclass/ismatrix.m - returns true for non-empty TT objects.

ttclass/isnumeric.m - returns true for non-empty TT objects

ttclass/isreal.m - returns true for real-valued ttclass objects.

ttclass/kron.m - Kronecker product of tensor train representation of matrices.

ttclass/mean.m – mimics the corresponding operation for matrices. If the input is a ttclass matrix, returns a vector of its mean values along the specified dimension. If the input is a ttclass vector, returns the mean value in a scalar.

ttclass/minus.m - tensor train subtraction.

ttclass/mldivide.m - matrix left divide for TT objects.

ttclass/mrdivide.m - divides a tensor train by a scalar.

ttclass/mtimes.m - matrix product in the TT representation.

ttclass/nnz.m - number of non-zeros used by the TT object.

ttclass/numel.m - Number of elements in the matrix represented by a tensor train.

ttclass/norm.m – computes a norm of the ttclass object. Because of the internal structure of the tensor train format, only the Frobenius norm can be computed reasonably fast.

ttclass/ttort.m - TT orthogonalisation.

ttclass/pack.m - pack up the additions buffer.

ttclass/plus.m - tensor train addition.

ttclass/rand.m - random tensor trains.

ttclass/ranks.m – returns tensor train ranks of the tensor trains buffered in the ttclass.

ttclass/rdivide.m - divides a tensor train by a scalar.

ttclass/revert.m - computes a bit-revert permutation of a tensor train.

ttclass/shrink.m – compresses a given ttclass object into a single tensor train with optimal ranks.

ttclass/sizes.m – returns mode sizes of a ttclass object (dimensions of elementary matrices).

ttclass/size.m - returns the size of the matrix represented by the tensor train.

ttclass/subsref.m - provides indexing functionality for tensor train objects.

ttclass/sum.m - mimics the corresponding operation for matrices. If the input is a ttclass matrix, returns a vector of its sums along the specified dimension. If an input is a ttclass vector, returns a sum of its values in a scalar.

ttclass/trace.m – returns the trace of a given matrix.

ttclass/transpose.m - transpose of a matrix represented by a tensor train.

ttclass/unit_like.m - unit tensor train with the same topology as the one supplied.

ttclass/vec.m – reshapes a ttclass matrix into the ttclass vector with the same elements.

## Polyadic functions

polyadic/ctranspose.m - conjugate-transpose operation.

polyadic/full.m - returns the matrix represented by the polyadic as a full matrix.

polyadic/inflate.m - returns the matrix represented by the polyadic as a sparse matrix.

polyadic/isempty.m - returns true for polyadics with zero dimensions.

polyadic/isfloat.m - returns true.

polyadic/ismatrix.m - returns true.

polyadic/isnumeric.m - returns true.

polyadic/kron.m - concatenates polyadics, which is equivalent to a Kronecker product.

polyadic/minus.m - buffered subtraction operation.

polyadic/mtimes.m - polyadic-times-scalar and polyadic-times-matrix operation.

polyadic/nnz.m - number of non-zeros in the cores of the polyadic.

polyadic/numel.m - number of elements in the matrix represented by a polyadic.

polyadic/plus.m - buffered addition operation.

polyadic/size.m - size of the matrix represented by the polyadic.

polyadic/transpose.m - transpose operation.

polyadic/validate.m - polyadic object structure check.

*Version 2.1, authors: Ilya Kuprov, Dmitry Savostyanov, Sergey Dolgov*