What are the properties of rotation axes in N-dimensions?

In summary, rotations in higher dimensions can be thought of as rotations parallel to planes instead of around an axis. The number of scalar parameters needed to describe a rotation in n-dimensional Euclidean space is n(n-1)/2, the number of combinations of pairs of axes. In 4-space, simultaneous rotation about two planes that do not share a common axis results in a double rotation. Any orthogonal matrix in SO(n) with n being even must have at least two real eigenvalues, and the nxn identity matrix is an example of a matrix in SO(n) with real eigenvalues.
  • #1
mnb96
715
5
Hi,
it is a clear fact that rotations in 3D keep the vectors on the rotation axis unchanged.
In 2D only the zero vector is unchanged.

How can one generalize the concept of rotation axis in N-dimensions?
I've read that for example in 4D one can only rotate around planes.

So are the rotation "hyper-axes" always subspaces of dimensionality n-2?
 
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  • #2
How do you define "rotation" in n dimensions?
 
  • #3
I assumed that "rotations" in n-dimensional Euclidean spaces are represented by the group [tex]SO(n)[/tex], since they describe linear transformations which preserve angles and distances.
 
  • #4
The concept of rotating about an axis is special to 3-space. Instead of thinking of rotation in 3-space as being about an axis, think of rotation in 3-space as being parallel to a plane. This way of thinking about rotation does generalize. There is only one plane parallel to the two-space plane, the plane itself. Rotation in 2-space can be described by a single scalar parameter. There are three orthogonal subplanes in 3-space: e.g., the xy, yz, and zx planes. It takes three parameters to describe rotation in 3-space. There are six orthogonal subplanes in 4-space: the three from the xyz 3-subspace plus the wx, yw, and wz subplanes. It takes six scalar parameters to describe rotation in 4-space. This way of thinking makes the two dimension rotation the primitive of rotation in any Euclidean n-space. In general, it takes n(n-1)/2 parameters to describe rotation in n-space, the number of combinations of pairs of axes.

If you want to think of rotation as being about something rather than parallel to a plane, the "about" is a n-2 subspace of the Euclidean n-space. Since 4-2=2, rotation parallel to a plane in 4-space is equivalent to rotation about a plane.

Any rotation in three space can be described in terms of a rotation about a single eigenaxis / parallel to a single eigenplane. This extends to 4-space. Simultaneously rotating parallel to a pair of planes (about a pair of planes) that share a common axis yields a single simple rotation parallel to / about some eigenplane. However, something new happens in four space. Simultaneously rotating about a pair of planes that do not share a common axis (e.g., the xy and wz planes) yields a double rotation. There is no single eigenplane of rotation for such a double rotation.
 
  • #5
thanks! your answer was clear!
There is still one issue bugging my mind.

What can we say if we have a [itex]n\times n[/itex] matrix which:
1) belongs to SO(n)
2) has only one real eigenvector

Can we say we are rotating about an axis in n-dimensions or not?
 
  • #6
mnb96 said:
There is still one issue bugging my mind.

What can we say if we have a [itex]n\times n[/itex] matrix which:
1) belongs to SO(n)
2) has only one real eigenvector

Can we say we are rotating about an axis in n-dimensions or not?
Sure.

Now, can that situation ever arise in 4-space? Think about it for a bit.
 
  • #7
I'd need to show that any orthogonal NxN matrix with N even, never has only one real eigenvector but at least two. However I don't know yet how to prove that.
 
  • #8
Think in terms of eigenvalues rather than eigenvectors. What are the implications of the complex conjugate root theorem with regard to the eigenvalues of a real NxN rotation matrix in the case that N is even?
 
  • #9
Thanks a lot for the hint!
For some reason I can't remember having studied that important theorem!

I guess the answer is:
We know that the eigenvalues of a matrix NxN are given by the zeroes of a polynomial of degree N.
If we assume the matrix has only one real eigenvalue, it follows from the complex conjugate root theorem that the remaining N-1 roots must be pairs of complex conjugate numbers, but this is not possible because now N-1 is an odd number, so we must have an even number of real eigenvalues.

One last thing:
is it possible matrices in SO(n) with n even to have some real eigenvalue at all? I'd say yes, but I haven't proved it.
 
  • #10
Well, the nxn identity matrix belongs to SO(n), for all n.
 
  • #11
mnb96 said:
One last thing:
is it possible matrices in SO(n) with n even to have some real eigenvalue at all? I'd say yes, but I haven't proved it.
Sure. Look at any the six primitive rotations in SO(4), for example. One is a rotation about the wz plane,

[tex]\bmatrix
\phantom{-}\cos \theta & \sin \theta & 0 & 0 \\
-\sin\theta & \cos \theta & 0 & 0 \\
0 & 0 & 1 & 0 \\
0 & 0 & 0 & 1
\endbmatrix[/tex]

Here 1 is a double eigenvalue with eigenvectors [tex]\hat z[/tex] and [tex]\hat w[/tex]. Any vector with zero x and y components with remain unchanged upon rotation about the wz plane.
 

1. What are N-dimensions rotation axes?

N-dimensions rotation axes refer to the imaginary lines or axes around which an object rotates in N-dimensional space. These axes are used to describe the orientation and movement of objects in higher dimensions.

2. How many axes are there in N-dimensional rotation?

The number of axes in N-dimensional rotation depends on the number of dimensions. In 2D rotation, there is only one axis (Z-axis) around which an object can rotate. In 3D rotation, there are three axes (X, Y, and Z) and in 4D rotation, there are six axes (X, Y, Z, W, V, and U).

3. Can objects rotate around multiple axes in N-dimensional space?

Yes, objects can rotate around multiple axes in N-dimensional space. For example, in 4D space, an object can rotate around the X, Y, and Z axes simultaneously.

4. How is rotation around N-dimensions different from 2D or 3D rotation?

In N-dimensional rotation, the orientation and movement of objects are described in higher dimensions, which can be difficult to visualize. In 2D or 3D rotation, objects rotate around fixed axes, while in N-dimensional rotation, objects can rotate around multiple axes simultaneously.

5. What are some real-life applications of N-dimensional rotation?

N-dimensional rotation has various applications in fields such as mathematics, physics, and computer graphics. It is used to describe the movement of objects in higher dimensions, analyze complex systems, and create 3D animations and simulations.

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