Do Rotational Matrices Always Yield Real Eigenvalues?

In summary: Orthogonal matrix means that the eigenvalues are all zero. This means that the matrix does not affect the vector in any way. There are no real or imaginary components that are affected.
  • #1
3.141592654
85
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Homework Statement



Show that the matrix

A = [cos θ -sin θ
sin θ cos θ]

will have complex eigenvalues if θ is not a multiple of π. Give a geometric interpretation of this result.

Homework Equations



Ax = λx, so

det(A-λI) = 0

The Attempt at a Solution



In this case,

A-λI = [(cos θ - λ) -sin θ
sin θ (cos θ - λ)]

so calculating the determinate, I have:

[itex]det(A-λI) = (cos θ - λ)(cos θ - λ) + sin^{2} θ [/itex]

[itex]= cos^{2} θ - 2λcos θ + λ^{2} + sin^{2} θ[/itex]

[itex]= λ^{2} - 2λcos θ + 1[/itex]

Setting this polynomial to zero, I have

[itex]
p(λ) = λ^{2} - 2λcos θ + 1 = 0
[/itex]

When cos θ is not a multiple of π, I need to use quadratic formula to solve this equation. Doing so, I get:

[itex]
λ = cos θ \pm sin θ
[/itex]

However, this value is not complex, is it? I'm a bit confused on what's going on.
 
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  • #2
You slipped up a bit with the quadratic equation. You don't just get sin(theta). Check it and show what you did if you still don't see it.
 
  • #3
Ah, thank you, after fixing the algebra I see the answer is:

[itex]λ = cos θ ± isin θ.[/itex]

In terms of what this means geometrically, it's been a long time since I've done anything with complex numbers, so can you correct the following if it is wrong?

I know that since λ is complex, the corresponding eigenvectors must also be complex, so θ is the argument of the eigenvectors in the complex plane. In that case, the matrix A (and hence λ) multiplies each element, z = x + iy, of the vector x, by cos ± isin θ.

When I multiply λ*z, I get:

[itex]λz = (xcos θ + ysin θ) + i(cos θ sin θ y)[/itex]

So the matrix A essentially makes the real component of each element of x a combination of x and y, where xcos θ + ysin θ ≤ x + y, and it multiplies the imaginary component by cos θ sin θ. In conjunction, these effects would shrink the imaginary component of z and could shrink or stretch the real component.

Is this correct? Is there something else that the problem is hoping that I'll recognize about how lambda acts on the vector x?

Thanks.
 
  • #4
I've realized that the matrix given in this problem is an orthogonal matrix, so it rotates the vector x. However, should I be able to see that lamda (and A) perform a rotation if I didn't realize that A was an orthogonal matrix?
 
  • #5
3.141592654 said:
I've realized that the matrix given in this problem is an orthogonal matrix, so it rotates the vector x. However, should I be able to see that lamda (and A) perform a rotation if I didn't realize that A was an orthogonal matrix?

It's a matrix you've probably seen a lot of times in lower level courses. I think they might have expected you to recognize it, even if they didn't use the words 'orthogonal' back then. It rotates a vector in the x-y plane by an angle theta counter-clockwise. You can do the same thing regarding a point in the x-y plane as a complex number (the Argand plane) and multiplying by cos(theta)+i*sin(theta)=cis(theta)=exp(i*theta). I'm having trouble coming up with anything more formal than that just now.
 
  • #6
Geometric interpretation: Which rotations of the plane have eigenvectors? For example, if I rotate the point (1,0) and get (x,0) for some value of x, what were the possible rotations that could have given me that
 

Related to Do Rotational Matrices Always Yield Real Eigenvalues?

1. What is a complex eigenvalue problem?

A complex eigenvalue problem is a mathematical problem that involves finding the eigenvalues and corresponding eigenvectors of a complex matrix. An eigenvalue is a scalar value that represents how a particular vector is stretched or compressed by a linear transformation, and an eigenvector is a non-zero vector that remains in the same direction after being transformed.

2. How is a complex eigenvalue problem solved?

A complex eigenvalue problem is solved by finding the roots of the characteristic equation, which is a polynomial equation that involves the eigenvalues and coefficients of the matrix. The roots of the characteristic equation are the eigenvalues, and the corresponding eigenvectors can be found by solving a system of linear equations.

3. What is the importance of complex eigenvalue problems?

Complex eigenvalue problems have many applications in various fields such as physics, engineering, and computer science. They are used to study the stability of dynamic systems, analyze quantum mechanical systems, and solve differential equations. They also have important applications in data analysis and signal processing.

4. Can a complex eigenvalue problem have multiple solutions?

Yes, a complex eigenvalue problem can have multiple solutions. In fact, the number of solutions is equal to the dimension of the matrix. This means that a nxn matrix will have n eigenvalues and n corresponding eigenvectors. These solutions may be real or complex numbers.

5. How does a complex eigenvalue problem differ from a real eigenvalue problem?

The main difference between a complex eigenvalue problem and a real eigenvalue problem is the nature of the eigenvalues and eigenvectors. In a real eigenvalue problem, the eigenvalues and eigenvectors are all real numbers, whereas in a complex eigenvalue problem, they can be complex numbers. Additionally, the methods for solving these problems may differ, as complex eigenvalue problems often require the use of complex number arithmetic.

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