Index Gymnastics: Matrix Representations & Rank-2 Tensor Components

In summary, the conversation discusses the calculation of various tensor components using matrix representations. The correct order of multiplication for ##t^{AB}## is given as ##t^{AB} = \begin{bmatrix} -1 & 0 \\ G+F & 1 \end{bmatrix} \begin{bmatrix} 0 & 1 \\ 1 & -F \end{bmatrix}## and the reason for this is that the index that is contracted with the metric is the second index of ##t##. This is because the first index in the matrix representation corresponds to the row index and the second corresponds to the column index. Additionally, it is noted that the metric is symmetric so it does not matter whether you
  • #1
SiennaTheGr8
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I'm trying to get the hang of index gymnastics, but I think I'm confused about the relationship between rank-2 tensor components and their matrix representations.

So in Hartle's book Gravity, there's Example 20.7 on p. 428. We're given the following metric:

##g_{AB} = \begin{bmatrix} F & 1 \\ 1 & 0 \end{bmatrix} \qquad \qquad g^{AB} = \begin{bmatrix} 0 & 1 \\ 1 & -F \end{bmatrix}##

And we're given the following components of a tensor:

##t_{AB} = \begin{bmatrix} G & 1 \\ -1 & 0 \end{bmatrix}##

Then we're asked to calculate ##{t^A}_B## and ##t^{AB}## (etc.).

Okay:

##{t^A}_B = g^{AC} t_{CB} = \begin{bmatrix} 0 & 1 \\ 1 & -F \end{bmatrix} \begin{bmatrix} G & 1 \\ -1 & 0 \end{bmatrix} = \begin{bmatrix} -1 & 0 \\ G+F & 1 \end{bmatrix}##

That's correct.

Now, for ##t^{AB}## I was thinking:

##t^{AB} = g^{AC} {t_C}^B##,

which is correct, but I didn't have ##{t_A}^B## yet, so I had to approach it another way. I thought maybe:

##t^{AB} = g^{CB} {t^A}_C = \begin{bmatrix} 0 & 1 \\ 1 & -F \end{bmatrix} \begin{bmatrix} -1 & 0 \\ G+F & 1 \end{bmatrix}##.

But that's actually the wrong order for the matrix multiplication (though if I'm not mistaken, ##g^{CB} {t^A}_C = {t^A}_C g^{CB}## -- is that right?). Instead, the correct answer is:

##t^{AB} = \begin{bmatrix} -1 & 0 \\ G+F & 1 \end{bmatrix} \begin{bmatrix} 0 & 1 \\ 1 & -F \end{bmatrix} \Big( \stackrel{?}{=} {t^A}_C g^{CB} \Big)##.

So I suppose what I'm asking is, first, whether it's indeed true that ##g^{CB} {t^A}_C = {t^A}_C g^{CB}##, and then second, what should have tipped me off that I needed to "reverse" the order of the matrices representing ##g^{CB}## and ##{t^A}_C## when calculating ##t^{AB}##?

Hope that's clear enough.
 
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  • #2
SiennaTheGr8 said:
So I suppose what I'm asking is, first, whether it's indeed true that ##g^{CB} {t^A}_C = {t^A}_C g^{CB}##, and then second, what should have tipped me off that I needed to "reverse" the order of the matrices representing ##g^{CB}## and ##{t^A}_C## when calculating ##t^{AB}##?
Yes, that is true. What should have tipped you off is that the index that is contracted with ##g^{CB}## is the second index of ##t##, not the first. In the matrix representation, the first index is the row index and the second the column index. Note that the metric is symmetric so it does not matter whether you multiply by it or its transpose. What matters is where you put it when you deal with the matrix representation.
 
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Related to Index Gymnastics: Matrix Representations & Rank-2 Tensor Components

1. What is the purpose of index gymnastics in matrix representations?

Index gymnastics is a technique used to manipulate indices in order to simplify and solve complex problems involving matrices. It helps to represent matrices in a concise and efficient manner, making it easier to perform calculations and solve equations.

2. How is a rank-2 tensor represented in index gymnastics?

A rank-2 tensor is represented using two indices, one for each row and column. This allows for the tensor components to be accessed and manipulated using the index gymnastics technique.

3. What are the advantages of using index gymnastics for matrix representations?

Index gymnastics allows for the manipulation of indices in a systematic and organized way, making it easier to solve complex problems involving matrices. It also helps to reduce the amount of written work and simplify equations.

4. Can index gymnastics be applied to tensors of higher ranks?

Yes, index gymnastics can be used for tensors of any rank. However, as the rank increases, the number of indices required also increases, making the calculations more complex.

5. How does index gymnastics relate to other mathematical concepts, such as linear algebra?

Index gymnastics is a technique used in linear algebra to manipulate matrices and tensors. It is often used in conjunction with other concepts such as matrix operations, eigenvectors, and eigenvalues to solve problems involving matrices and tensors.

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