Show for tensors (A · B) : C = A^T · C : B = C · B^T : A

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In summary, using Einstein notation, the identities (A · B) : C = A^T · C : B = C · B^T : A are true. The double inner product can be converted into a summation, making it easier to solve. With this understanding, the identities can be proven.
  • #1
bubbles_kav
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Homework Statement


using Einstein notation, show the following identities are true

(A · B) : C = A^T · C : B = C · B^T : A



Homework Equations





The Attempt at a Solution


(A · B) : C=(A_{ij} · B_{jk} ) : C
= D_{ik} C_{ik}
= C_{ik} D_{ik}
= C_{ik} (A_{ij} · B_{jk} )

That's as far as I can get. No clue as what to do next, any pointers would be
greatly appreciated :)
 
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  • #2
Bjk = BTkj
 
  • #3
Thanks for the response :)
I got it solved since then, I had trouble trying to convert the double inner product as
a summation. Once I figured that out, it was as easy as pie
 

1. What is a tensor?

A tensor is a mathematical object that describes the relationship between vectors and scalars. It is often represented by an array of numbers and can be used to solve problems in physics, engineering, and other fields.

2. What does A · B represent in the equation?

A · B represents the dot product or inner product of tensors A and B. This operation results in a scalar value and is used to measure the similarity or projection of one tensor onto another.

3. What does C = A^T · C : B mean?

This part of the equation represents the outer product of the transpose of tensor A and C, multiplied by tensor B. The result is a new tensor C with dimensions determined by the sizes of the original tensors.

4. How is C · B^T : A different from A · B?

C · B^T : A is a different arrangement of the same operation, where tensor B is transposed before being multiplied by A. This can result in a different scalar value or a different tensor, depending on the sizes and values of the original tensors.

5. What are some practical applications of this equation?

This equation is commonly used in fields such as physics, engineering, and data analysis to solve problems involving vectors and tensors. It can also be used in machine learning and artificial intelligence algorithms to process and analyze large datasets.

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