Symmetric tensor


In mathematics, a symmetric tensor is a tensor that is invariant under a permutation of its vector arguments:
for every permutation σ of the symbols Alternatively, a symmetric tensor of order r represented in coordinates as a quantity with r indices satisfies
The space of symmetric tensors of order r on a finite-dimensional vector space V is naturally isomorphic to the dual of the space of homogeneous polynomials of degree r on V. Over fields of characteristic zero, the graded vector space of all symmetric tensors can be naturally identified with the symmetric algebra on V. A related concept is that of the antisymmetric tensor or alternating form. Symmetric tensors occur widely in engineering, physics and mathematics.

Definition

Let V be a vector space and
a tensor of order k. Then T is a symmetric tensor if
for the braiding maps associated to every permutation σ on the symbols .
Given a basis of V, any symmetric tensor T of rank k can be written as
for some unique list of coefficients that are symmetric on the indices. That is to say
for every permutation σ.
The space of all symmetric tensors of order k defined on V is often denoted by Sk or Symk. It is itself a vector space, and if V has dimension N then the dimension of Symk is the binomial coefficient
We then construct Sym as the direct sum of Symk for k = 0,1,2,...

Examples

There are many examples of symmetric tensors. Some include, the metric tensor,, the Einstein tensor, and the Ricci tensor,.
Many material properties and fields used in physics and engineering can be represented as symmetric tensor fields; for example: stress, strain, and anisotropic conductivity. Also, in diffusion MRI one often uses symmetric tensors to describe diffusion in the brain or other parts of the body.
Ellipsoids are examples of algebraic varieties; and so, for general rank, symmetric tensors, in the guise of homogeneous polynomials, are used to define projective varieties, and are often studied as such.

Symmetric part of a tensor

Suppose is a vector space over a field of characteristic 0. If is a tensor of order, then the symmetric part of is the symmetric tensor defined by
the summation extending over the symmetric group on k symbols. In terms of a basis, and employing the Einstein summation convention, if
then
The components of the tensor appearing on the right are often denoted by
with parentheses around the indices being symmetrized. Square brackets are used to indicate anti-symmetrization.

Symmetric product

If T is a simple tensor, given as a pure tensor product
then the symmetric part of T is the symmetric product of the factors:
In general we can turn Sym into an algebra by defining the commutative and associative product ⊙. Given two tensors and, we use the symmetrization operator to define:
It can be verified that the resulting product is in fact commutative and associative. In some cases the operator is omitted:.
In some cases an exponential notation is used:
Where v is a vector.
Again, in some cases the ⊙ is left out:

Decomposition

In analogy with the theory of symmetric matrices, a symmetric tensor of order 2 can be "diagonalized". More precisely, for any tensor T ∈ Sym2, there are an integer r, non-zero unit vectors v1,...,vr ∈ V and weights λ1,...,λr such that
The minimum number r for which such a decomposition is possible is the rank of T. The vectors appearing in this minimal expression are the principal axes of the tensor, and generally have an important physical meaning. For example, the principal axes of the inertia tensor define the Poinsot's ellipsoid representing the moment of inertia. Also see Sylvester's law of inertia.
For symmetric tensors of arbitrary order k, decompositions
are also possible. The minimum number r for which such a decomposition is possible is the symmetric rank of T. This minimal decomposition is called a Waring decomposition; it is a symmetric form of the tensor rank decomposition. For second-order tensors this corresponds to the rank of the matrix representing the tensor in any basis, and it is well known that the maximum rank is equal to the dimension of the underlying vector space. However, for higher orders this need not hold: the rank can be higher than the number of dimensions in the underlying vector space. Moreover, the rank and symmetric rank of a symmetric tensor may differ.