Understanding Tensors: Comparing gαβAβ and Aβgαβ

In summary, it appears that the g and A in the first example do not commute, while the g and A in the second example do.
  • #1
grzz
1,006
15
I am learning about tensors.
Is gαβAβ the same as Aβgαβ ?
Thanks for any help.
 
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  • #2
Yes.
 
  • #3
But then is
BαβAγ equal to Aγ Bαβ ?
 
  • #4
I think that the tensors A and B do not commute as the g and A do in the previous example. But I am not sure.
Any help!
 
  • #5
grzz said:
But then is
BαβAγ equal to Aγ Bαβ ?
Bαβ is not tensor, it is the component of a tensor. The components of a tensor are real or complex numbers. They commute.
 
  • #6
grzz said:
But then is
BαβAγ equal to Aγ Bαβ ?

As spyphy says this is just multiplication of numbers (components) so order doesn't matter. The full tensors must be formed by contracting the indices with basis elements. It is there where you see the distinctions in order written:
[itex]\mathbf{B}\otimes\mathbf{A}= B_{\alpha\beta} A^y \mathbf{e}^\alpha\otimes\mathbf{e}^\beta\otimes \mathbf{e}_y =A^y B_{\alpha\beta} \mathbf{e}^\alpha\otimes\mathbf{e}^\beta\otimes \mathbf{e}_y[/itex]
but note that:
[itex]\mathbf{B}\otimes\mathbf{A}= B_{\alpha\beta} A^y \mathbf{e}^\alpha\otimes\mathbf{e}^\beta\otimes \mathbf{e}_y \ne B_{\alpha\beta}A^y \mathbf{e}_y\otimes\mathbf{e}^\alpha\otimes\mathbf{e}^\beta = \mathbf{A}\otimes\mathbf{B}[/itex]
take your time parsing these and see the distinction.
 
  • #7
Thanks for the help.
Since [itex]\alpha[/itex] is repeated in g[itex]_{}\beta_{}\alpha[/itex]A[itex]^{}\alpha[/itex] then it was clear to me that this is a sum and the g[itex]_{}\beta_{}\alpha[/itex] and the A[itex]^{}\alpha[/itex] are numbers and so commute.

But I thought that A[itex]_{}\beta_{}\alpha[/itex]B[itex]^{}\gamma[/itex] represented the product of two tensors. From the little I know I thought that sometimes a tensor is represented by one of its components. That is why I said that the second example may not commute.
 
  • #8
I am also poor in using latex!
 
  • #9
grzz said:
Thanks for the help.
Since [itex]\alpha[/itex] is repeated in g[itex]_{}\beta_{}\alpha[/itex]A[itex]^{}\alpha[/itex] then it was clear to me that this is a sum and the g[itex]_{}\beta_{}\alpha[/itex] and the A[itex]^{}\alpha[/itex] are numbers and so commute.

But I thought that A[itex]_{}\beta_{}\alpha[/itex]B[itex]^{}\gamma[/itex] represented the product of two tensors. From the little I know I thought that sometimes a tensor is represented by one of its components. That is why I said that the second example may not commute.
[itex]A_{\beta\alpha}B^\gamma[/itex] is equal to both the [itex]{}_{\beta\alpha}{}^\gamma[/itex] component of the tensor [itex]A\otimes B[/itex], and the [itex]{}^\gamma{}_{\beta\alpha}[/itex] component of the tensor [itex]B\otimes A[/itex].

Click the quote button if you want to see how I did the LaTeX. Try changing something and use the preview feature to see what it looks like. (To be able to preview, you need to trick the forum software into thinking that you're typing a reply, e.g. by typing at least 4 characters after the quote tags).
 
Last edited:
  • #10
Thank you because in those last four lines you gave me the best tutorial about LaTeX.
 
  • #11

1. What is the concept of tensors and why are they difficult to understand?

Tensors are mathematical objects that are used to represent physical quantities with multiple dimensions. They are difficult to understand because they involve abstract mathematical concepts and can have complex properties that are not easily visualized.

2. How are tensors used in scientific research and applications?

Tensors are used in a variety of scientific fields such as physics, engineering, and computer science. They are particularly useful in fields that deal with complex systems or data with multiple dimensions, such as fluid dynamics, machine learning, and quantum mechanics.

3. What are some common challenges in working with tensors?

Some common challenges in working with tensors include understanding their properties and operations, manipulating and visualizing them, and handling large amounts of data represented by tensors.

4. Are there any resources available to help with understanding tensors?

Yes, there are many resources available such as textbooks, online courses, and tutorials that can help with understanding tensors. Additionally, there are various software packages and libraries that can assist with working with tensors.

5. Can you provide an example of a real-world application of tensors?

One example of a real-world application of tensors is in image recognition and computer vision. Tensors are used to represent images and their features, and machine learning algorithms can be applied to these tensors to recognize objects in images.

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