Tensor product

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In mathematics, the tensor product, denoted by , may be applied in different contexts to vectors, matrices, tensors, vector spaces, algebras, topological vector spaces, and modules, among many other structures or objects. In each case the significance of the symbol is the same: the most general bilinear operation. In some contexts, this product is also referred to as outer product. The term "tensor product" is also used in relation to monoidal categories. The variant of is used in control theory.

Tensor product of vector spaces

The tensor product of two vector spaces V and W over a field K is another vector space over K. It is denoted VK W, or VW when the underlying field K is understood.

Prerequisite: the free vector space

The definition of requires the notion of the free vector space F(S) on some set S. The elements of the vector space F(S) are formal sums of elements of S with coefficients in a given field K. A formal sum is an expression written in the form of a sum in which no actual arithmetic operations can be carried out. For example 2a + 3b is a formal sum, and {{ safesubst:#invoke:Unsubst||$B=1/1 − x}} is a formal sum with no restrictions on values of x (versus the usual case where |x| < 1 must hold for a geometric series to converge), since no "plugging in" will actually be performed. For the set of all formal sums of elements of S with coefficients in K to be a vector space, we need to define addition and scalar multiplication. The terms of a formal sum can be written in any order and the addition of formal sums is associative.

Addition of formal sums is defined as follows: if mn, aism + ajsn cannot be simplified. If m = n, then aism + ajsn = (ai + aj) ⋅ sm.

Scalar multiplication of formal sums is defined as follows: If k is in the field K, then k(a1s1 + a2s2 + ... + ansn) = ka1s1 + ka2s2 + ... + kansn.

The dimension of the vector space equals the number of elements in S.


Given two vector spaces V and W over a field K, the tensor product U of V and W, denoted as U = VW is defined as the vector space whose elements and operations are constructed as follows:

From the cartesian product V × W, the free vector space F(V × W) over K is formed. The vectors of VW are then defined to be the equivalence classes of F(V × W) under the following equivalence relations:

The operations of VW, i.e. the map of vector addition +: U × UU and scalar multiplication ⋅: K × UU are defined to be the respective operations +F and F from F(V × W), acting on any representatives

in the involved equivalence classes outputting the one equivalence class of the result.

One can show that the result is actually independent of which representatives of the involved classes have been chosen. In other words, the operations are well-defined.

In short, the tensor product VW is defined as the quotient space F(V × W)/N over the subspace N of F(V × W), which expresses the equivalence relations described above:

Notation and examples

Given bases {vi} and {wi} for V and W respectively, the tensors {viwj} form a basis for VW. The dimension of the tensor product therefore is the product of dimensions of the original spaces; for instance RmRn will have dimension mn.

Elements of VW are sometimes referred to as tensors, although this term refers to many other related concepts as well.[1] An element of VW of the form vw is called a pure or simple tensor. In general, an element of the tensor product space is not a pure tensor, but rather a finite linear combination of pure tensors. That is to say, if v1 and v2 are linearly independent, and w1 and w2 are also linearly independent, then v1w1 + v2w2 cannot be written as a pure tensor. The number of simple tensors required to express an element of a tensor product is called the tensor rank (not to be confused with tensor order, which is the number of spaces one has taken the product of, in this case 2; in notation, the number of indices), and for linear operators or matrices, thought of as (1, 1) tensors (elements of the space VV), it agrees with matrix rank.

Tensor product of linear maps

The tensor product also operates on linear maps between vector spaces. Specifically, given two linear maps S : VX and T : WY between vector spaces, the tensor product of the two linear maps S and T is a linear map

defined by

In this way, the tensor product becomes a bifunctor from the category of vector spaces to itself, covariant in both arguments.[2]

If S and T are both injective, surjective, or continuous then ST is, respectively, injective, surjective, continuous.

By choosing bases of all vector spaces involved, the linear maps S and T can be represented by matrices. Then, the matrix describing the tensor product ST is the Kronecker product of the two matrices. For example, if V, X, W, and Y above are all two-dimensional and bases have been fixed for all of them, and S and T are given by the matrices and , respectively, then the tensor product of these two matrices is

The resultant rank is at most 4, and the resultant dimension 16. Here rank denotes the tensor rank (number of requisite indices), while the matrix rank counts the number of degrees of freedom in the resulting array.

A dyadic product is the special case of the tensor product between two vectors of the same dimension.

Universal property

The tensor product as defined above is a universal property. In this context, this means that the tensor product is uniquely defined, up to isomorphism: there is only one tensor product. In the context of linear algebra and vector spaces, the maps in question are required to be linear maps. The tensor product of vector spaces, as defined above, satisfies the following universal property: there is a bilinear map (i.e., linear in each variable v and w) φ : V × WVW such that given any other vector space Z together with a bilinear map h : V × WZ, there is a unique linear map Template:Overset : VWZ satisfying h = Template:Oversetφ. In this sense, φ is the most general bilinear map that can be built from V × W. In particular, this implies that any spaces with such a (uniquely defined) tensor product are examples of symmetric monoidal categories, as this is the defining characteristic of the category. Uniqueness of the tensor product means that for any other bilinear map φ′ : V × WV ⊗′ W with the above property there is an isomorphism k : VWV ⊗′ W such that φ′ = kφ holds.

This characterization can simplify proving statements about the tensor product. For example, the tensor product is symmetric: that is, there is a canonical isomorphism:

To construct, say, a map from left to right, it suffices, by the universal property, to give a bilinear map V × WWV. This is done by mapping (v, w) to wv. Constructing a map in the opposite direction is done similarly, as is checking that the two linear maps VWWV and WVVW are inverse to one another.

Similar reasoning can be used to show that the tensor product is associative, that is, there are natural isomorphisms

Therefore, it is customary to omit the parentheses and write V1V2V3.

Tensor powers and braiding

Let n be a non-negative integer. The nth tensor power of the vector space V is the n-fold tensor product of V with itself. That is

A permutation σ of the set {1, 2, ..., n} determines a mapping of the nth Cartesian power of V

defined by


be the natural multilinear embedding of the Cartesian power of V into the tensor power of V. Then, by the universal property, there is a unique isomorphism

such that

The isomorphism τσ is called the braiding map associated to the permutation σ.

Product of tensors

{{#invoke:see also|seealso}} For non-negative integers r and s a (r,s)-tensor on a vector space V is an element of

Here V is the dual vector space (which consists of all linear maps f from V to the ground field K).

There is a product map, called the (tensor) product of tensors

It is defined by grouping all occurring "factors" V together: writing vi for an element of V and fi for elements of the dual space,

Picking a basis of V and the corresponding dual basis of V, TTemplate:Su(V) is endowed with a natural basis (this basis is described in the article on Kronecker products). In terms of these bases, the components of a (tensor) product of two (or more) tensors can be computed. For example, if F and G are two covariant tensors of rank m and n respectively (i.e. FTTemplate:Su, and GTTemplate:Su), then the components of their tensor product are given by

[3] Thus, the components of the tensor product of two tensors are the ordinary product of the components of each tensor. Another example: let U be a tensor of type (1, 1) with components Uαβ, and let V be a tensor of type (1, 0) with components Vγ. Then


Relation to dual space

A particular example is the tensor product of some vector space V with its dual vector space V (which consists of all linear maps f from V to the ground field K). In this case, there is a natural "evaluation" map

which on elementary tensors is defined by

The resulting map

is called tensor contraction (for r, s > 0).

On the other hand, if V is finite-dimensional, there is a map in the other direction (called coevaluation)

where v1, ..., vn is a basis of V, and vi is its dual basis. The interplay of evaluation and coevaluation map can be used to characterize finite-dimensional vector spaces without referring to bases.[4]

Tensor product vs. Hom

Given three vector spaces U, V, W the tensor product is linked to the vector space of all linear maps, as follows:

Here Hom(-,-) denotes the K-vector space of all linear maps. This is an example of adjoint functors: the tensor product is "left adjoint" to Hom.

Adjoint representation

The tensor may be naturally viewed as a module for the Lie algebra End(V) by means of the diagonal action: for simplicity let us assume r = s = 1, then, for each u ∈ End(V),

where u in End(V) is the transpose of u, that is, in terms of the obvious pairing on VV,


There is a canonical isomorphism given by

Under this isomorphism, every u in End(V) may be first viewed as an endomorphism of and then viewed as an endomorphism of End(V). In fact it is the adjoint representation ad(u) of End(V).

Tensor products of modules over a ring

{{#invoke:main|main}} The tensor product of two modules A and B over a commutative ring R is defined in exactly the same way as the tensor product of vector spaces over a field:

where now F(A × B) is the free R-module generated by the cartesian product and G is the R-module generated by the same relations as above.

More generally, the tensor product can be defined even if the ring is non-commutative (abba). In this case A has to be a right-R-module and B is a left-R-module, and instead of the last two relations above, the relation

is imposed. If R is non-commutative, this is no longer an R-module, but just an abelian group.

The universal property also carries over, slightly modified: the map φ : A × BAR B defined by (a, b) → ab is a middle linear map (referred to as "canonical middle linear map".[5]); that is,[6] it satisfies:

The first two properties make φ a bilinear map of the abelian group A × B. For any middle linear map ψ of A × B, a unique group homomorphism f of AR B satisfies ψ = fφ, and this property determines within group isomorphism. See the main article for details.

Computing the tensor product

For vector spaces, the tensor product VW is quickly computed since bases of V of W immediately determine a basis of VW, as was mentioned above. For modules over a general (commutative) ring, not every module is free. For example, Z/n is not a free abelian group (= Z-module). The tensor product with Z/n is given by

More generally, given a presentation of some R-module M, that is, a number of generators miM, iI together with relations , with , the tensor product can be computed as the following cokernel:

Here NJ := ⨁jJ N and the map is determined by sending some nN in the jth copy of NJ to ajin (in NI). Colloquially, this may be rephrased by saying that a presentation of M gives rise to a presentation of MR N. This is referred to by saying that the tensor product is a right exact functor. It is not in general left exact, that is, given an injective map of R-modules M1M2, the tensor product

is not usually injective. For example, tensoring the (injective) map given by multiplication with n, n : ZZ with Z/n yields the zero map 0 : Z/nZ/n, which is not injective. Higher Tor functors measure the defect of the tensor product being not left exact. All higher Tor functors are assembled in the derived tensor product.

Tensor product of algebras

{{#invoke:main|main}} Let R be a commutative ring. The tensor product of R-modules applies, in particular, if A and B are R-algebras. In this case, the tensor product AR B is an R-algebra itself by putting

For example,

A particular example is when A and B are fields containing a common subfield R. The tensor product of fields is closely related to Galois theory: if, say, A = R[x] / f(x), where f is some irreducible polynomial with coefficients in R, the tensor product can be calculated as

where now f is interpreted as the same polynomial, but with its coefficients regarded as elements of B. In the larger field B, the polynomial may become reducible, which brings in Galois theory. For example, if A = B is a Galois extension of R, then

is isomorphic (as an A-algebra) to the Adeg(f).

Other examples of tensor products

Tensor product of Hilbert spaces


Topological tensor product


Tensor product of graded vector spaces


Tensor product of quadratic forms


Tensor product of multilinear maps

Given multilinear maps and their tensor product is the multilinear function

Tensor product of graphs


Monoidal categories

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


Exterior and symmetric algebra

Two notable constructions in linear algebra can be constructed as quotients of the tensor product: the exterior algebra and the symmetric algebra. For example, given a vector space V, the exterior product

is defined as

Note that when V's underlying field does not have characteristic 2, then this definition is equivalent to

The image of in the exterior product is usually denoted and satisfies, by construction, . Similar constructions are possible for (n factors), giving rise to , the n-th exterior power of V. The latter notion is the basis of differential n-forms.

The symmetric algebra is constructed in a similar manner:

That is, in the symmetric algebra two adjacent vectors (and therefore all of them) can be interchanged. The resulting objects are called symmetric tensors.

Tensor product of line bundles


Tensor product in programming

Array programming languages

Array programming languages may have this pattern built in. For example, in APL the tensor product is expressed as (for example or ). In J the tensor product is the dyadic form of */ (for example a */ b or a */ b */ c).

Note that J's treatment also allows the representation of some tensor fields, as a and b may be functions instead of constants. This product of two functions is a derived function, and if a and b are differentiable, then a */ b is differentiable.

However, these kinds of notation are not universally present in array languages. Other array languages may require explicit treatment of indices (for example, MATLAB), and/or may not support higher-order functions such as the Jacobian derivative (for example, Fortran/APL).

See also


  1. See Tensor or Tensor (intrinsic definition).
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  3. Analogous formulas also hold for contravariant tensors, as well as tensors of mixed variance. Although in many cases such as when there is an inner product defined, the distinction is irrelevant.
  4. See Compact closed category.
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