Can someone help me about skew symmetric?

In summary: Could you solve the last two parts by using this definition for me,please? I have no any idea for the last two parts. Thanks!
  • #1
JerryKelly
11
0
Let A be an nxn skew symmetric mx.(A^T=-A).
i) Show that if X is a vector in R^n then (X,AX)=0
ii) Show that 0 is the only possible eigenvalue of A
iii)Show that A^2 is symmetric
iv)Show that every eigenvalue of A^2 is nonpositive.
v)Show that if X is an eigenvector of A^2 , then so is AX
vi)With X as in v), show that the subspace W spanned by X and AX is an A-subspace.
vii)Assuming AX not= 0. let U=X/||X||, V=AX/||AX||. Show that AU=(AU,V)V and that AV=-(AU,V)U.
ix) Show if U,V,U3 , ...,Un is an orthonormal basis of R^n, then the mx of a reletive to this basis has the form (0,b,0,0...0;-b,0,0,0...0;0,0,0,a,0...0;0,0,-a,0,0...0;C some format to the end), where C is skew symmetric (n-2)x(n-2) mx.


Thanks!
 
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  • #2
This smells like homework. What have you tried on these questions so far?
 
  • #3
i) follows from the definition of transpose and the properties of inner products, what do you know about them?
 
  • #4
Yes,it is homework question. So far, I just have a idea for part iv), and I have no idea for the rest of them. For part iv), i can use (AX,X)=(X,-AX)=-A(X,X). Except this one, I have no idea how to do the rest of them. Could you give me some help,please? Thanks!
Galileo said:
This smells like homework. What have you tried on these questions so far?
 
  • #5
I think you need to relearn some basics, or correct some typos.

How can (x,-Ax)=-A(x,x)?

The left hand side is a scalar, the right hand side is a matrix (times a scalar)

Remember the definition of transpose is made to satisfy

[tex](Mx,y)=(x,M^Ty)[/tex]

and that pretty much is all you need, in fact it solves pretty much everything (along with the definition of eigenvalue/vector)
 
Last edited:
  • #6
Could you solve the last two parts by using this definition for me,please? I have no any idea for the last two parts. Thanks!
matt grime said:
I think you need to relearn some basics, or correct some typos.

How can (x,-Ax)=-A(x,x)?

The left hand side is a scalar, the right hand side is a matrix (times a scalar)

Remember the definition of transpose is made to satisfy

[tex](Mx,y)=(x,M^Ty)[/tex]

and that pretty much is all you need, in fact it solves pretty much everything (along with the definition of eigenvalue/vector)
 

1. What is a skew symmetric matrix?

A skew symmetric matrix is a square matrix in which the elements below the main diagonal are mirrored across the diagonal from the elements above the main diagonal. This means that for any element aij in the matrix, aij = -aji.

2. How do I check if a matrix is skew symmetric?

To check if a matrix is skew symmetric, you can transpose the matrix and then compare it to the negative of the original matrix. If the two matrices are equal, then the original matrix is skew symmetric.

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

A skew symmetric matrix has the following properties:

  • The main diagonal of the matrix consists of all zeros.
  • The matrix is symmetric about the main diagonal.
  • The sum of any two skew symmetric matrices is also skew symmetric.
  • The scalar multiple of a skew symmetric matrix is also skew symmetric.
  • The determinant of a skew symmetric matrix is either 0 or a negative number.

4. How is a skew symmetric matrix used in science?

Skew symmetric matrices have various applications in science, particularly in physics and engineering. Some examples include:

  • In mechanics, skew symmetric matrices are used to represent angular velocity and angular acceleration.
  • In electromagnetism, skew symmetric matrices are used to describe the transformation of electric and magnetic fields.
  • In quantum mechanics, skew symmetric matrices are used to represent spin operators.

5. Can a non-square matrix be skew symmetric?

No, a non-square matrix cannot be skew symmetric. Skew symmetry is a property that is only applicable to square matrices, as it involves the elements of the matrix being mirrored across the main diagonal. Non-square matrices do not have a main diagonal, therefore they cannot exhibit skew symmetry.

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