Why the determinant of a matrix is equal to its transpose

In summary: A^{T})In summary, the determinant of a matrix is equal to the determinant of its transpose. This can be proven by showing that the permutation of indices in the determinant formula for the transpose results in the same sum as the original determinant formula.
  • #1
Jennifer1990
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Homework Statement


I don't understand why the determinant of a matrix is equal to its transpose...how is this possible?


Homework Equations





The Attempt at a Solution

 
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  • #2


If you mean the determinant of a matrix is equal to the determinant of the transpose, why do you think that's so impossible? Try and prove it.
 
  • #3


ohhh it works o.o
can i see a rigorous proof of this?
 
  • #4


Yes, if you have functioning eyes and someone shows you such a proof.
 
  • #5


lol that's funny. What i meant is May you show me such a proof, please?
 
  • #6


I might, if you prove it to yourself with a 2 x 2 matrix first.
A = [a b; c d] (this is in row-major order).
 
  • #7


A = [a b; c d]
det (A) = ad-bc

A transpose = [a c; b d]
det A transpose = ad - bc = det A
 
  • #8


In this case, a visual interpretation and the fact that you can take determinants along and row/column is usually a fair explanation.

For a possible proof, you can prove the determinant inductively for matrices of size n. Then you take the idea of permutations to swap rows/columns to show that it does not change the determinant. It should be straight forward from then on.
 
  • #9


The proof is trivial:

If A is an n by n matrix, then:

[tex]\det(A) =\sum_{\pi}\operatorname{sign}(\pi)\prod_{i=1}^{n}A_{i,\pi(i)}[/tex] (1)

The determinant of the transpose can thus be written as:

[tex]\det(A^{T}) =\sum_{\pi}\operatorname{sign}(\pi)\prod_{i=1}^{n}A_{\pi(i),i}[/tex]

So, to prove that the determinant of the transpose is the same, we have move the permutation from the second index to the first in (1). We can do this as follows.

In the product, it doesn't matter in which order the matrix elements are multiplied:

[tex]\prod_{i=1}^{n}A_{i,\pi(i)} = \prod_{i=1}^{n}A_{\pi^{-1}(i),i}[/tex]


Using that the sign of a permutation is the same as the sign of its inverse, gives:


[tex]\det(A) =\sum_{\pi}\operatorname{sign}(\pi^{-1})\prod_{i=1}^{n}A_{\pi^{-1}(i),i}[/tex]


The set of all inverse permutations is the same as the set of all permuations, so we can write this as


[tex]\det(A) =\sum_{\pi}\operatorname{sign}(\pi)\prod_{i=1}^{n}A_{\pi(i),i}[/tex]
 

Related to Why the determinant of a matrix is equal to its transpose

1. Why is the determinant of a matrix equal to its transpose?

The determinant of a matrix is equal to its transpose because the determinant is a measure of how much a matrix scales the space it operates on. Since the transpose of a matrix is equivalent to rotating the matrix, it does not change the amount of scaling and thus the determinant remains the same.

2. Can you provide an example to illustrate this concept?

Yes, let's take the 2x2 matrix [3 2; 5 4]. The determinant of this matrix is (3*4)-(2*5) = 2. If we take the transpose of this matrix, we get [3 5; 2 4]. The determinant of this transpose is also (3*4)-(2*5) = 2. So, we can see that the determinant of the matrix and its transpose are equal.

3. Does this hold true for all matrices?

Yes, this concept holds true for all square matrices. For non-square matrices, the determinant is not defined. Additionally, the transpose of a non-square matrix may not even exist.

4. How does this relate to the properties of determinants?

One of the properties of determinants is that the determinant of a product of matrices is equal to the product of their determinants. So, when we take the transpose of a matrix, we are essentially multiplying it by a rotation matrix, which does not affect the determinant. Therefore, the determinant of the original matrix and its transpose are equal.

5. What is the significance of this property in linear algebra?

This property is significant because it allows us to easily calculate the determinant of a matrix without having to find the cofactor expansion or use other methods. We can simply take the transpose of the matrix and use the determinant of the original matrix to get the same result. This can save time and effort in solving equations involving determinants.

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