Skew symmetric matrix question

In summary: I was just suggesting that if you want to avoid using components (like Aij) then you can use the more general definition of "adjoint" …… which just happens to be transpose if you are working in Euclidean space and are using the scalar product! :smile:… ooh! … I've just noticed … that means your proof is valid in any inner product space, using any inner product! :smile:(and I'm sure you know that anyway, but I thought I'd mention it just in case you hadn't thought about it :wink:)… and if you're not using Euclidean space or the scalar product, the proof is still true (using the definition of "adjoint")
  • #1
TTob
21
0
Let A in n x n real matrix.
For every x in R^n we have <Ax,x>=0 where < , > is scalar product.
prove that A^t=-A (A is skew symmetric matrix)
 
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  • #2
TTob said:
Let A in n x n real matrix.
For every x in R^n we have <Ax,x>=0 where < , > is scalar product.
prove that A^t=-A (A is skew symmetric matrix)

Hi TTob! :smile:

Hint: just write out <Ax,x>=0 in terms of Aij etc …

then jiggle it around a bit! :smile:

What do you get?
 
  • #3
Thank you.

note [tex]x=(x_1,...,x_n)[/tex] and [tex]A=(a_{ij})[/tex].
then [tex](Ax)_i=[/tex] [tex]\sum_{\substack{0\leq j\leq n}} a_{ij}x_j[/tex]
then
[tex]<Ax,x>=[/tex] [tex]\sum_{\substack{0\leq j\leq n \\ 0\leq i\leq n}} a_{ij}x_i x_j=0[/tex]

when you put [tex]x=e_i[/tex] you get [tex]a_{ii}=0[/tex] for all i.
when you put [tex]x=e_i+e_j[/tex] you get [tex]a_{ii}+a_{ij}+a_{ji}+a_{jj}=0[/tex] for all i,j .

so [tex]a_{ji}=-a_{ij}[/tex] for all i,j and then [tex]A^t=-A[/tex].
 
  • #4
Hi TTob! :smile:

Yes … or if you want to avoid using components …

[tex]0\ =\ <A(x+y),x+y>\ -\ <Ax,x>\ -\ <Ay,y>\ \ =\ \ <Ax,y>\ +\ <Ay,x>\ \ =\ \ <(A + A^T)x,y>[/tex] :smile:
 
  • #5
Or use the fact that <Ax, y>= <x, ATy>. (That is a more general definition of "adjoint")

From that, <Ax, x>= <x, ATx>. If A is skew-symmetric, <x, AT x>= -<Ax, x> so <Ax, x>= -<Ax, x> and <Ax, x>= 0.
 
  • #6
HallsofIvy said:
… <Ax, x>= <x, ATx>. If A is skew-symmetric, <x, AT x>= -<Ax, x> so <Ax, x>= -<Ax, x> and <Ax, x>= 0.

Hi HallsofIvy! :smile:

erm …
I think you've proved the transpose of the original theorem! :wink:
 

1. What is a skew symmetric matrix?

A skew symmetric matrix is a square matrix in which the elements below the main diagonal are equal in magnitude but opposite in sign to the elements above the main diagonal.

2. How is a skew symmetric matrix different from a symmetric matrix?

A symmetric matrix is a square matrix in which the elements are reflected along the main diagonal, so that the elements above and below the diagonal are equal. A skew symmetric matrix, on the other hand, has elements that are equal in magnitude but opposite in sign above and below the diagonal.

3. What are the properties of a skew symmetric matrix?

Some properties of a skew symmetric matrix include: the main diagonal elements are all equal to zero, the transpose of a skew symmetric matrix is equal to its negative, and the determinant of a skew symmetric matrix is either 0 (for odd-sized matrices) or a negative number (for even-sized matrices).

4. How are skew symmetric matrices used in real-world applications?

Skew symmetric matrices are commonly used in physics and engineering, particularly in the study of rotational motion. They are also used in computer graphics to represent three-dimensional rotations and in statistics for multivariate analysis.

5. How can I determine if a matrix is skew symmetric?

To determine if a matrix is skew symmetric, you can check if it is equal to the negative of its transpose. Another method is to check if all the elements below the main diagonal are equal in magnitude but opposite in sign to the elements above the main diagonal.

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