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 {1, 2, ..., r}. 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 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.


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 {1,2,...,k} (or equivalently for every transposition on these symbols).

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

for some unique list of coefficients (the components of the tensor in the basis) 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(V) or Symk(V). It is itself a vector space, and if V has dimension N then the dimension of Symk(V) is the binomial coefficient

We then construct Sym(V) as the direct sum of Symk(V) for k = 0,1,2,...


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 T Vk 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


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(V) into an algebra by defining the commutative and associative product ⊙.[1] Given two tensors T1 Symk1(V) and T2 Symk2(V), we use the symmetrization operator to define:

It can be verified (as is done by Kostrikin and Manin[1]) that the resulting product is in fact commutative and associative. In some cases the operator is omitted: T1T2 = T1T2.

In some cases an exponential notation is used:

Where v is a vector. Again, in some cases the ⊙ is left out:


In 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 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 (symmetric) 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.[2] 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.[3]

See also


  1. Kostrikin, Alexei I.; Manin, Iurii Ivanovich (1997). Linear algebra and geometry. Algebra, Logic and Applications. 1. Gordon and Breach. pp. 276–279. ISBN 9056990497.
  2. Comon, P.; Golub, G.; Lim, L. H.; Mourrain, B. (2008). "Symmetric Tensors and Symmetric Tensor Rank". SIAM Journal on Matrix Analysis and Applications. 30 (3): 1254. arXiv:0802.1681. doi:10.1137/060661569.
  3. Shitov, Yaroslav (2018). "A Counterexample to Comon's Conjecture". SIAM Journal on Applied Algebra and Geometry. 2 (3): 428–443. arXiv:1705.08740. doi:10.1137/17m1131970. ISSN 2470-6566.


  • Bourbaki, Nicolas (1989), Elements of mathematics, Algebra I, Springer-Verlag, ISBN 3-540-64243-9.
  • Bourbaki, Nicolas (1990), Elements of mathematics, Algebra II, Springer-Verlag, ISBN 3-540-19375-8.
  • Greub, Werner Hildbert (1967), Multilinear algebra, Die Grundlehren der Mathematischen Wissenschaften, Band 136, Springer-Verlag New York, Inc., New York, MR 0224623.
  • Sternberg, Shlomo (1983), Lectures on differential geometry, New York: Chelsea, ISBN 978-0-8284-0316-0.
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