A conjugacy class under O(n), orthogonal projection

In summary, the author is saying that the conjugacy class of the matrix p_0 = \begin{pmatrix}I_k && 0 \\ 0 && 0\\ \end{pmatrix} is a submanifold of the affine space S(n)_k because it is the orbit of p_0 under the action of the group O(n) on S(n) by conjugation. The author is unclear as to why this is true.
  • #1
Sajet
48
0
This is not really a homework question per se but I wasn't sure where else to put it:

In a script I'm reading the following set is defined:

[itex]P(n)_k := \{p \in S(n) | p^2 = p, \text{trace } p = k\}[/itex]

(i.e. the set of all real orthogonal projection matrices with trace k).

Now the following statement is made:

"[itex]P(n)_k[/itex] is a submanifold of the affine space [itex]S(n)_k[/itex] since it is the conjugacy class of the matrix [itex]p_0 = \begin{pmatrix}I_k && 0 \\ 0 && 0\\ \end{pmatrix}[/itex], i.e. the orbit of [itex]p_0[/itex] under the action of the group O(n) on S(n) by conjugation."

([itex]S(n)_k[/itex] is the set of all real symmetric matrices with trace k.)

I don't understand the second part of this statement. The conjugacy class should be:

[itex]\{Ap_0A^{-1} | A \in O(n)\} = \{Ap_0A^{t} | A \in O(n)\} =[/itex]

[itex] \{\begin{pmatrix}a_{11}^2 && 0 && ... && 0 && ... && 0 \\ 0 && a_{22}^2 && ... && 0 && ... \\ 0 && 0 && ... && a_{kk}^2 && ... && 0 \\ 0 && 0 && 0 && 0 && ... && 0\end{pmatrix} | (a_{ij}) \in O(n)\}[/itex]

and I don't see why this equals [itex]P(n)_k[/itex]. (Or maybe my calculation is wrong.)
 
Last edited:
Physics news on Phys.org
  • #2
I'm lost at how you found

Sajet said:
[itex]Ap_0A^{t}=\begin{pmatrix}a_{11}^2 && 0 && ... && 0 && ... && 0 \\ 0 && a_{22}^2 && ... && 0 && ... \\ 0 && 0 && ... && a_{kk}^2 && ... && 0 \\ 0 && 0 && 0 && 0 && ... && 0\end{pmatrix}[/itex]

That doesn't seem correct at all.
 
  • #3
You're right. I don't know what happened there. I'll take a look at this again.
 
  • #4
Some keywords which might help: "diagonalization of symmetric matrices"
 
  • #5
Hey! First of all, thank you for your help! I didn't get back to this in the last couple of days but I will take a closer look tomorrow.
 
  • #6
Ok, I figured this out. Thank you.

Just one more thing: I can't really follow why this makes the conjugacy class a submanifold. I know that certain kinds of orbit spaces are manifolds but this is not true for every group action, and I couldn't find anything on conjugacy classes always being submanifolds.
 

1. What is a conjugacy class under O(n)?

A conjugacy class under O(n) refers to a set of orthogonal projection matrices that are equivalent under conjugation, meaning they can be transformed into each other by a change of basis.

2. How are orthogonal projection matrices defined?

Orthogonal projection matrices are square matrices that represent linear transformations onto a subspace that are perpendicular to each other. They are defined as matrices that satisfy the property P^2 = P, where P is the orthogonal projection matrix.

3. How do you determine the size of a conjugacy class under O(n)?

The size of a conjugacy class under O(n) can be determined by counting the number of distinct eigenvalues of the orthogonal projection matrix. The number of distinct eigenvalues corresponds to the number of different ways the matrix can be conjugated.

4. What is the significance of conjugacy classes under O(n) in linear algebra?

Conjugacy classes under O(n) are important in the study of linear algebra because they provide a way to classify and understand the behavior of orthogonal projection matrices. They also have applications in fields such as physics, where orthogonal projection matrices are used to represent physical quantities.

5. How can conjugacy classes under O(n) be applied in real-world scenarios?

Conjugacy classes under O(n) have various applications in real-world scenarios. For example, they are used in computer graphics to project 3D images onto a 2D screen. They are also used in signal processing to remove noise and enhance signals. Additionally, they have applications in data compression and image recognition.

Similar threads

  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Calculus and Beyond Homework Help
Replies
8
Views
995
  • Calculus and Beyond Homework Help
Replies
2
Views
361
  • Calculus and Beyond Homework Help
Replies
11
Views
1K
  • Calculus and Beyond Homework Help
Replies
6
Views
3K
  • Calculus and Beyond Homework Help
Replies
2
Views
2K
  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Linear and Abstract Algebra
Replies
8
Views
779
  • Linear and Abstract Algebra
Replies
3
Views
1K
  • Linear and Abstract Algebra
Replies
9
Views
867
Back
Top