Can determinants be calculated using tensor notation?

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In summary, the homework equations are: det(A)=e_{ijk}A_{1i}A_{2j}A_{3k} and det(AB)= det(E_j)*det(E_j-1)...(E_1) * det(B). C=AB, so det(C)=det(AB).
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Durato
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[SOLVED] Tensor notation-Determinants

I'm trying to learn the basics of Tensor calculus using a free online book (Introduction to Tensor Calculus and Continuum Mechanics), and I got stuck on this question (Part 2 in book, after non-math introduction).

link to part 2, questions (mine #19) at end:
http://www.math.odu.edu/~jhh/part2.PDF"

Homework Statement


Let A and B denote 3x3 matrices with elements [tex]A_{ij}[/tex] and [tex]B_{ij}[/tex]
respectively. Show that if C = AB is a matrix product, then det(C) = det(A)*det(B)
where det = determinant.

Hint: use the result from example 1.1-9

Homework Equations


det(A) = [tex]e_{ijk}A_{1i}A_{2j}A_{3k}[/tex]

The Attempt at a Solution


The matrix multiplication of A*B =C, in indical notation, is
[tex]C_{ij}=A_{im}B_{mj}[/tex] (I think) where the first subscript in A,B and C is the row number and the second subscript the column of the matrix.

Then, plugging into 'relevant equation' above, we get
det(C) = [tex]e_{ijk}C_{1i}C_{2j}C_{3k}[/tex]
det(C) = [tex]e_{ijk}(A_{1m}B_{mi})(A_{2n}B_{nj})(A_{3x}B_{xk})[/tex]

Then, I compare this to just multiplying det(A)*det(B)
det(A) * det(B) = [tex](e_{ijk}A_{1i}A_{2j}A_{3k})(e_{rst}B_{1r}B_{2s}B_{3t})[/tex]

However, from here, I can't seem to make the connection between the two:cry:
I would try to expand it, but I don't think this is the way to do it, since it would get rid of the advantage of indical notation. Help would be appreciated! Thxs!

PS: on a side note, whenever you preview a post, the template is pasted again to the end of the post, which is annoying. Maybe someone can fix this...
 
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  • #2
I believe you need to prove this in two cases. If B is not invertible, then det(B)=0. There is a non-trivial solution. Thus AB is not invertible.

Else, suppose A and B are invertible/nonsingular, then there are elementary matrices, A=Ej, E_j-1,... E_1
det(A)=det(E_j)*det(E_j-1)...*(E_1)

Now, det(AB)= det(E_j)*det(E_j-1)...(E_1) * det(B).
Then A and B can be expressed as a product of elementary matrices.

Edit:Since C=AB, then det(C)=det(AB). Hope that helps.
 
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  • #3
Maybe I'm reading the intent incorrectly, but I think the problem specifically wants him to prove it using indicial notation.
 
  • #4
Oh sorry.

Let C=AB. By indicial notation, C_ij=A_ik B_kj

Since A is 3x3 matrix, then det(A)= A_11(A_22...) - A_12(A21..) + A_13(A_21...) by method of solving for determinants where n>2
Then det(A)= epsilon_ijk A_i1 A_j2 A_k3 = epsilon_ijk A_1i A_2j A_3k
Therefore, epsilon_rst det(A) = epsilon_ijk A_ir A_js A_kt

We know that det(C)=det(AB)=epsilon_ijk C_i1 C_j2 C_k3
=...= epsilon_rst B_r1 B_s2 B_t3 det(A)
 
  • #5
I can't believe i didn't see it! Thanks guys!:smile:
 

1. What is tensor notation?

Tensor notation is a mathematical notation used to represent tensors, which are multi-dimensional arrays used to describe the relationships between different physical quantities in a system.

2. How is tensor notation different from other notations?

Tensor notation is different from other notations, such as vector or matrix notation, because it can represent higher order tensors with more than two indices. It also has a more concise and compact representation compared to other notations.

3. What is a determinant in tensor notation?

In tensor notation, a determinant is a scalar value that represents the scaling factor of a linear transformation on a space. It is used to determine whether a set of vectors is linearly independent or not.

4. How is the determinant calculated in tensor notation?

The determinant is calculated by summing the products of the elements in a matrix, where the sign of each product is determined by a specific pattern based on the indices of the elements. This process is called the Leibniz formula.

5. What are some practical applications of tensor notation and determinants?

Tensor notation and determinants are commonly used in fields such as physics, engineering, and machine learning. They are used in the analysis and manipulation of multi-dimensional data, as well as in solving systems of linear equations and determining the properties of geometric transformations.

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