Product of a symmetric and antisymmetric tensor

In summary, the symmetric matrix S can be multiplied by an antisymmetric matrix A to produce an antisymmetric tensor, but this is not always the case. The space of antisymmetric tensors in ##R^4## is well-defined, and contains all 4 of the 3-index Levi-Civita tensors.
  • #1
stedwards
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It seems there should be a list of tensor identities on the internet that answers the following, but I can't find one.

For tensors in ##R^4##,

##S = S_\mu{}^\nu = S_{(\mu}{}^{\nu)}## is a symmetric tensor.
##A = A_{\nu\rho\sigma}= A_{[\nu\rho\sigma]}## is an antisymmetric tensor in all indices.

The basis vectors are suppressed for simplicity.

Parentheses ##(\ \ )## enclose symmetric indices; the tensor remains the same upon an exchange of any two indices.
Brackets ##[\ \ ]## enclose antisymmetric indices; the tensor changes sign on an exchange of any two indices.

Is the inner product ## SA=S_\mu{}^\nu A_{\nu\rho\sigma}## antisymmetric in all indices?

(In a related aside, ##A_{\nu\rho\sigma}## can be expressed as a scalar times a "unit valued" antiysmmetric tensor. Call it ##\epsilon^3{}_{\mu\nu\rho}## in ##R^4##, or the 3 index Levi-Civita tensor in 4 dimensions. But I don't see any internet reference to this animal either. Are my keywords lacking?)
 
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  • #3
I don't think the contraction will have any specific symmetry between the index ##\mu## and the other indices.

Suppose you were just talking about ##n \times n## matrices. If you multiplied a symmetric matrix S with an antisymmetric matrix A, would you expect the result to have any sort of symmetry?
 
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  • #4
Ben Niehoff said:
I don't think the contraction will have any specific symmetry between the index ##\mu## and the other indices.

Suppose you were just talking about ##n \times n## matrices. If you multiplied a symmetric matrix S with an antisymmetric matrix A, would you expect the result to have any sort of symmetry?

Well...I am hoping, not expecting.

As it turns out, ##S=\delta##, or ##{S_\mu}^\nu = \delta_\mu^\nu## obtains an antisymmetric tensor for the ##SA## product.

In general, in the orthonormal coordinate system where S is diagonal, ##S = ({S_0}^0,\ {S_1}^1,\ {S_2}^2,\ {S_3}^3)##. The ##SA## product has 4 corresponding, independent values.

I had thought this was sufficient to supply the exception to disprove the hypothesis, but on second glance, no.
 
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  • #5
The result has multiple interesting antisymmetric properties but not, in general, is the product antisymmetric.

In orthonormal coordinates the tensor ##\epsilon_{\mu\nu\rho}## is equal to it's symbol. Under a change of coordinates, it remains antisymmetric. Symmetric tensors likewise remain symmetric.

We can construct a product space ##\hat{q}_\delta = \hat{\theta}_\alpha \wedge \hat{\theta}_\beta \wedge \hat{\theta}_\gamma##. Bases vectors are identified with hats. ##\hat{\theta}_\alpha## are the contravariant bases of the orthonormal 4-space. ##\hat{\theta}_\alpha## are the covariant basis of the orthonormal 4-space.

There are for independent bases in ##q##. One choice of basis is:
##\hat{q}_0 = \hat{\theta}_1 \wedge \hat{\theta}_2 \wedge \hat{\theta}_3##
##\hat{q}_1 = \hat{\theta}_2 \wedge \hat{\theta}_3 \wedge \hat{\theta}_0##
##\hat{q}_2 = \hat{\theta}_3 \wedge \hat{\theta}_0 \wedge \hat{\theta}_1##
##\hat{q}_3 = \hat{\theta}_0 \wedge \hat{\theta}_1 \wedge \hat{\theta}_2##​

In one choice of orthonormal coordinates, S is a diagonal matrix. Define the elements as,
##a = {S_0}^0##
##b = {S_1}^1##
##c = {S_2}^2##
##d = {S_3}^3##.​

##A= k\epsilon##. All 3 index antisymmetric tensors are ##k## times the corresponding 3 index Levi-Civita in ##R^4##.

Putting it all together, with more abuse of notation (leaving out the basis vectors on the left side),

##{S_0}^0 A_{0\mu\nu} = ak(\hat{q}_0 - \hat{q}_1 + \hat{q}_2)##
##{S_1}^1 A_{1\mu\nu} = bk(\hat{q}_1 - \hat{q}_2 + \hat{q}_3)##
##{S_2}^2 A_{2\mu\nu} = ck(\hat{q}_2 - \hat{q}_3 + \hat{q}_0)##
##{S_3}^3 A_{3\mu\nu} = dk(\hat{q}_3 - \hat{q}_0 + \hat{q}_1)##.

##{S_\mu}^\nu A_{\nu\rho\sigma}## is a linear combination of 3 out of 4 of 3-index Levi-Civita tensors of ##R^4## in orthonormal coordinates.

How this variegates under a general coordinate transformation, I don't know yet. I could be more literate in tensor densities.​
 
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1. What is a symmetric tensor?

A symmetric tensor is a mathematical object that is invariant under certain transformations. In simpler terms, it means that if you switch the order of the indices of the tensor, the value remains the same. This is similar to a symmetric matrix, where the value of an element is equal to its corresponding element when the rows and columns are switched.

2. What is an antisymmetric tensor?

An antisymmetric tensor is a mathematical object that changes sign when the indices are switched. In other words, if you swap the indices of an antisymmetric tensor, the value will be the negative of its initial value. This is similar to an antisymmetric matrix, where the value of an element is equal to the negative of its corresponding element when the rows and columns are switched.

3. What is the product of a symmetric and antisymmetric tensor?

The product of a symmetric and antisymmetric tensor is a mathematical operation that involves multiplying the two tensors together. The resulting tensor will have the same symmetry properties as the individual tensors, meaning it will be symmetric if both tensors are symmetric, and antisymmetric if one of the tensors is antisymmetric.

4. How is the product of a symmetric and antisymmetric tensor calculated?

The product of a symmetric and antisymmetric tensor is calculated by using the properties of tensor multiplication. This involves summing over all the possible combinations of indices and multiplying the corresponding elements of the two tensors. The resulting tensor will have the same number of indices as the original tensors, but with different values for each index.

5. What are some real-world applications of the product of a symmetric and antisymmetric tensor?

The product of a symmetric and antisymmetric tensor has various applications in physics and engineering. It is commonly used in mechanics, electromagnetism, and fluid dynamics to describe the behavior of physical systems. It is also used in quantum mechanics to describe the properties of particles and their interactions. In engineering, it is used in structural analysis and design to describe the stress and strain of materials under different loading conditions.

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