That is, however, something particular to the case of tensor product.

We have a tensor product for the combined state of both systems.

The tensor product as defined above is a universal property.

One can try to be more clever in defining a tensor product.

This is a special case of a topological tensor product.

In the remaining cases, both factors of the tensor product require four or more colors.

Not to be confused with tensor products of spin representations.

This is the application of the more general tensor product to matrices.

Compare also the section Tensor product of linear maps above.

In this talk we give results on the exposed points of unit ball tensor product spaces.

For the tensor product, this is a consequence of the general fact .

It is also used in tensor product model transformation-based controller design.

A general context for tensor product is that of a monoidal category.

In general, the tensor product of complete spaces is not complete again.

Higher "weights" then just correspond to taking additional tensor products with this space in the range.

A host of categorial models can be given using Day's tensor product construction.

The state of the composite system is then described by the following tensor product:

One can also construct tensor products of super vector spaces.

This is just a tensor product of three qubits, and different from cloning a state.

Here stands for the tensor product of line bundles.

Let S act on this tensor product space by permuting the indices.

The following examples show how tensor products arise naturally.

The action of on an -fold tensor product factors through the braid group.

Then the tensor product of the arrow category is the original composition operator.

Roughly speaking this means that there is only one sensible way to define the tensor product.

It is defined using the Kronecker product and normal matrix addition.

It is also a principal submatrix of the Kronecker product.

It based on the generalized linear model with the design matrix written as a Kronecker product.

The Kronecker product can be used to get a convenient representation for some matrix equations.

Another definition is the Kronecker product of two matrices, to obtain a block matrix.

The Kronecker product should not be confused with the usual matrix multiplication, which is an entirely different operation.

By taking Kronecker products of this representation with itself repeatedly, one may construct all higher irreducible representations.

The Kronecker product of two unimodular matrices is also unimodular.

The outer product is simply the Kronecker product, limited to vectors (instead of matrices).

I assume you mean the Kronecker product (the matrix being multiplied by 1-gamma) is p.s.d., rather than the vector.

It is also equivalent to the Kronecker product of the adjacency matrices of the graphs.

The tensor product, outer product and Kronecker product all convey the same general idea.

Others have very different names (outer product, tensor product, Kronecker product) but convey essentially the same idea.

The vectorization is frequently used together with the Kronecker product to express matrix multiplication as a linear transformation on matrices.

Generic C++ and Fortran 90 codes for calculating Kronecker products of two matrices.

Where denotes the Kronecker product and Vec the vectorization of the matrix Y.

A column-wise Kronecker product of two matrices may also be called the Khatri-Rao product.

The outer product of vectors can be also regarded as a special case of the Kronecker product of matrices.

In mathematics, the Kronecker product, denoted by , is an operation on two matrices of arbitrary size resulting in a block matrix.

Here, however, we will also need to use the Matrix Differential Calculus (Kronecker product and vectorization transformations).

The Kronecker product is named after Leopold Kronecker, even though there is little evidence that he was the first to define and use it.

Essentially the Tracy-Singh product is the pairwise Kronecker product for each pair of partitions in the two matrices.

The simplest form of multiplication associated with matrices is scalar multiplication, which is a special case of the Kronecker product.

One can interpret this on the level of symmetric functions, giving a formula for the Kronecker product of two Schur polynomials:

The equivalence between the above matrix normal and multivariate normal density functions can be shown using several properties of the trace and Kronecker product, as follows.