Tensor (intrinsic definition)


In mathematics, the modern component-free approach to the theory of a tensor views a tensor as an abstract object, expressing some definite type of multilinear concept. Their can be derived from their definitions, as linear maps or more generally; and the rules for manipulations of tensors arise as an extension of linear algebra to multilinear algebra.
In differential geometry an intrinsic geometric statement may be described by a tensor field on a manifold, and then doesn't need to make reference to coordinates at all. The same is true in general relativity, of tensor fields describing a physical property. The component-free approach is also used extensively in abstract algebra and homological algebra, where tensors arise naturally.

Definition via tensor products of vector spaces

Given a finite set of vector spaces over a common field F, one may form their tensor product, an element of which is termed a tensor.
A tensor on the vector space V is then defined to be an element of a vector space of the form:
where V∗ is the dual space of V.
If there are m copies of V and n copies of V∗ in our product, the tensor is said to be of and contravariant of order m and covariant order n and total order. The tensors of order zero are just the scalars, those of contravariant order 1 are the vectors in V, and those of covariant order 1 are the one-forms in V∗. The space of all tensors of type is denoted
The space of type tensors
is isomorphic in a natural way to the space of linear transformations from V to V. A bilinear form on a real vector space V,, corresponds in a natural way to a type tensor in
An example of such a bilinear form may be defined, termed the associated metric tensor, and is usually denoted g.

Tensor rank

A simple tensor is a tensor that can be written as a product of tensors of the form
where a, b,..., d are nonzero and in V or V∗ – that is, if the tensor is nonzero and completely factorizable. Every tensor can be expressed as a sum of simple tensors. The rank of a tensor T is the minimum number of simple tensors that sum to T.
The zero tensor has rank zero. A nonzero order 0 or 1 tensor always has rank 1. The rank of a non-zero order 2 or higher tensor is less than or equal to the product of the dimensions of all but the highest-dimensioned vectors in which the tensor can be expressed, which is d when each product is of n vectors from a finite-dimensional vector space of dimension d.
The term rank of a tensor extends the notion of the rank of a matrix in linear algebra, although the term is also often used to mean the order of a tensor. The rank of a matrix is the minimum number of column vectors needed to span the range of the matrix. A matrix thus has rank one if it can be written as an outer product of two nonzero vectors:
The rank of a matrix A is the smallest number of such outer products that can be summed to produce it:
In indices, a tensor of rank 1 is a tensor of the form
The rank of a tensor of order 2 agrees with the rank when the tensor is regarded as a matrix, and can be determined from Gaussian elimination for instance. The rank of an order 3 or higher tensor is however often very hard to determine, and low rank decompositions of tensors are sometimes of great practical interest. Computational tasks such as the efficient multiplication of matrices and the efficient evaluation of polynomials can be recast as the problem of simultaneously evaluating a set of bilinear forms
for given inputs xi and yj. If a low-rank decomposition of the tensor T is known, then an efficient evaluation strategy is known.

Universal property

The space can be characterized by a universal property in terms of multilinear mappings. Amongst the advantages of this approach are that it gives a way to show that many linear mappings are "natural" or "geometric". Explicit computational information can then be written down using bases, and this order of priorities can be more convenient than proving a formula gives rise to a natural mapping. Another aspect is that tensor products are not used only for free modules, and the "universal" approach carries over more easily to more general situations.
A scalar-valued function on a Cartesian product of vector spaces
is multilinear if it is linear in each argument. The space of all multilinear mappings from the product into W is denoted
LN. When N = 1, a multilinear mapping is just an ordinary linear mapping, and the space of all linear mappings from V to W is denoted.
The universal characterization of the tensor product implies that, for each multilinear function
there exists a unique linear function
such that
for all vi ∈ V and αi ∈ V∗.
Using the universal property, it follows that the space of -tensors admits a natural isomorphism
Each V in the definition of the tensor corresponds to a V* inside the argument of the linear maps, and vice versa.. In particular, one has
and
and

Tensor fields

, physics and engineering must often deal with tensor fields on smooth manifolds. The term tensor is sometimes used as a shorthand for tensor field. A tensor field expresses the concept of a tensor that varies from point to point on the manifold.