What is the Interpretation of a Tensor?

In summary, the conversation discusses the computation of a specific expression involving a matrix M. The final result is determined to be 4, which is also the determinant of the matrix. While the symmetries of the matrix are considered, it is not possible to quickly evaluate the determinant by only looking at these symmetries.
  • #1
S. Moger
53
2

Homework Statement



[itex]M= \begin{pmatrix}
2 & -1 & 0\\
-1 & 2 & -1\\
0 & -1 & 2\\
\end{pmatrix}[/itex]

Compute [itex]\frac{1}{6}\epsilon_{ijk}\epsilon_{lmn} M_{il} M_{jm} M_{kn}[/itex] .

The Attempt at a Solution



I computed the result which is 4, by realizing that there are 36 non-zero levi-civita containing components to sum. Within this group, for each fixed {i,l} there are 4 possible sets of {in},{km}. As the order of the M_{ab}'s inside the product doesn't matter they can be rearranged to form sums of 2 each at a time. The positive contribution to the sum arises from the (now) three occurences of the product of the diagonal elements [itex]M_{ii} M_{jj} M_{kk} = 2 \cdot 2 \cdot 2 = 8[/itex] (not meant to be read as sums). The negative contributions arise from products of specific non-diagonal components multipied by a specific diagonal component. Because of interchangeability inside the product (or alternatively, in this case, symmetry) all get bundled in pairs of two. Each non-zero contribution (i.e. non-{1,3} permutation) then equals [itex]2 \cdot (-1 \cdot -1 \cdot 2) = 4[/itex], and there are three of them. So the final result turns out to be

[itex]2 (3 \cdot 8 - 3 \cdot 4) = 24[/itex], which divided by 6 leaves us with 4.

However, it feels like I'm missing something here (I'm not using the symmetry in any kind of crucial way). Is this the way you would solve this problem? Would it be possible to "see" the result by simply looking at the expression (i.e. interpreting it before doing the math)? Or for example by expressing the epsilons in deltas?( Edit: I now see it's the determinant. But still, it isn't super apparent from just doing the math mindlessly (which is my type of thing until I feel I grasp the basics.)
 
Last edited:
  • #3
Apologies for not having Latex here. As you have discovered, your expression is just the determinant of the matrix. This is evident by expanding
eps(i,j,k) eps(l,m,n) M(i,l) M(j,m) M(k,n) as six lines, one for each sequence of possible i,j,k that result in non zero epsilon components:

= eps (1,2,3) eps(l,m,n) M(1,l) M(2,m) M(3,n)
+ eps (2,3,1) eps(l,m,n) M(2,l) M(3,m) M(1,n)
+ eps (3,1,2) eps(l,m,n) M(3,l) M(1,m) M(2,n)
+ three other lines, you get the idea.

But each line is equal to the determinant which is defined as eps(l,m,n) M(1,l) M(2,m) M(3,n). To see this you have to reorder the l,m,n indices in each line except the first.

Your fundamental question, why can't this be done by looking at the symmetries of the M matrix, is equivalent to asking: can the determinant of a symmetric matrix be quickly evaluated by looking at symmetries? Not as far as I know.
 
  • Like
Likes S. Moger
  • #4
Thanks, that's a very clear and nice explanation.
 

1. What is a tensor?

A tensor is a mathematical object that describes the relationships between different coordinate systems and their components. It is commonly used in the fields of physics, engineering, and mathematics to model and analyze physical phenomena.

2. How is a tensor interpreted?

A tensor can be interpreted as a linear transformation that maps vectors from one coordinate system to another. It represents the change in direction and magnitude of a vector when moving from one coordinate system to another.

3. What is the importance of tensor interpretation?

Tensor interpretation is important because it allows scientists to understand and analyze complex systems with multiple variables and coordinate systems. It also helps to identify symmetries and patterns in data, which can lead to new insights and discoveries.

4. Can tensors be visualized?

Yes, tensors can be visualized in certain cases, such as when dealing with two-dimensional or three-dimensional systems. However, in higher dimensions, it becomes more difficult to visualize tensors, and mathematical representations are used instead.

5. How are tensors used in scientific research?

Tensors are used in a variety of scientific research fields, including physics, engineering, and data analysis. They are particularly useful in fields that deal with complex systems, such as fluid dynamics, electromagnetism, and general relativity. Tensors are also used in machine learning and data analysis to analyze and classify large sets of data.

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