# Symmetric tensor

In mathematics, a symmetric tensor is tensor that is invariant under a permutation of its vector arguments. Thus an rth order symmetric tensor represented in coordinates as a quantity with r indices satisfies

$T_{i_1i_2\dots i_r} = T_{i_{\sigma 1}i_{\sigma 2}\dots i_{\sigma r}}$

for every permutation σ of the symbols {1,2,...,r}.

The space of symmetric tensors of rank r on a finite dimensional vector space 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

$T\in V^{\otimes r}$

a tensor of order r. Then T is a symmetric tensor if

$\tau_\sigma T = T\,$

for the braiding maps associated to every permutation σ on the symbols {1,2,...,r} (or equivalently for every transposition on these symbols).

Given a basis {ei} of V, any symmetric tensor T of rank r can be written as

$T = \sum_{i_1,\dots,i_r=1}^N T_{i_1i_2\dots i_r} e^{i_1} \otimes e^{i_2}\otimes\cdots \otimes e^{i_r}$

for some unique list of coefficients $T_{i_1i_2\dots i_r}$ (the components of the tensor in the basis) that are symmetric on the indices. That is to say

$T_{i_{\sigma 1}i_{\sigma 2}\dots i_{\sigma r}} = T_{i_1i_2\dots i_r}$

for every permutation σ.

The space of all symmetric tensors of rank r defined on V is often denoted by Sr(V) or Symr(V). It is itself a vector space, and if V has dimension N then the dimension of Symr(V) is the binomial coefficient

$\dim\, \operatorname{Sym}^r(V) = {N + r - 1 \choose r}.$

### Symmetric part of a tensor

Suppose $V$ is a vector space over a field of characteristic 0. If $T\in V^{\otimes r}$ is a tensor of order $r$, then the symmetric part of $T$ is the symmetric tensor defined by

$\operatorname{Sym}\, T = \frac{1}{r!}\sum_{\sigma\in\mathfrak{S}_r} \tau_\sigma T,$

the summation extending over the symmetric group on r symbols. In terms of a basis, and employing the Einstein summation convention, if

$T = T_{i_1i_2\dots i_r}e^{i_1}\otimes e^{i_2}\otimes\cdots \otimes e^{i_r},$

then

$\operatorname{Sym}\, T = \frac{1}{r!}\sum_{\sigma\in \mathfrak{S}_r} T_{i_{\sigma 1}i_{\sigma 2}\dots i_{\sigma r}} e^{i_1}\otimes e^{i_2}\otimes\cdots \otimes e^{i_r}.$

The components of the tensor appearing on the right are often denoted by

$T_{(i_1i_2\dots i_r)} = \frac{1}{r!}\sum_{\sigma\in \mathfrak{S}_r} T_{i_{\sigma 1}i_{\sigma 2}\dots i_{\sigma r}}$

with parentheses around the indices which have been symmetrized. [Square brackets are used to indicate anti-symmetrization.]

If T is a simple tensor, given as a pure tensor product

$T=v_1\otimes v_2\otimes\cdots \otimes v_r$

then the symmetric part of T is the symmetric product of the factors:

$v_1\odot v_2\odot\cdots\odot v_r := \frac{1}{r!}\sum_{\sigma\in\mathfrak{S}_r} v_{i_{\sigma 1}}\otimes v_{i_{\sigma 2}}\otimes\cdots\otimes v_{i_{\sigma r}}.$

## Examples

Many material properties and fields used in physics and engineering can be represented as symmetric tensor fields; for example, stress, strain, and anisotropic conductivity.

In full analogy with the theory of symmetric matrices, a (real) symmetric tensor of order 2 can be "diagonalized". More precisely, for any tensor T ∈ Sym2(V), there is an integer n and non-zero vectors v1,...,vn ∈ V such that

$T = \sum_{i=1}^n \pm v_i\otimes v_i.$

This is Sylvester's law of inertia. The minimum number n 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 ellipsoid representing the moment of inertia.

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.