Is a Real 3x3 Matrix with a Square of Identity Diagonalizable?

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In summary, a real 3x3 matrix, A, is diagonalizable over the complex numbers by a real matrix, P, and the eigenvalues are square roots of 1. The attempt at a solution is to consider A+I and A-I as endomorphisms of a 3-dimensional vector space, use the rank-nullity theorem, and note that the kernels (resp. ranges) have intersection {0}. Finally, P can be taken to be real because the eigenvectors are real.
  • #1
Hello Kitty
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[SOLVED] Diagonalizing a 3x3 matrix

Homework Statement



I want to show that a real 3x3 matrix, A, whose square is the identity is diagonalizable by a real matrix P and that A has (real) eigenvalues of modulus 1.

Homework Equations



None.

The Attempt at a Solution



Since any matrix is diagonalizable over the complex numbers, I deduced that since there exists a complex matrix P such that PAP^{-1} = diag{x,y,z} (so x,y,z the eigenvalues of A), then diag{x^2,y^2,z^2} = (PAP^{-1})^2 = Id, hence the eigenvalues are square roots of 1 therefore must be real of modulus 1 as required.

I'm not totally sure my reasoning is sound. Even if it is, there is still the problem that P may be complex.
 
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  • #2
How do you figure that every matrix is diagonalizable over the complex numbers?? That's not true. Try diagonalizing [1 1,0 1]. On the other hand A^2=I can be factored into (A+I)(A-I)=0. Try thinking about the kernel and range of (A+I) and (A-I).
 
  • #3
OK at last I think I see how to do this. I consider A+I and A-I as endomorphisms of a 3-dimensional vector space. Then use the rank-nullity theorem to deduce that

(i) dim(K+) + dim(R+) = 3

and

(ii) dim(K-) + dim(R-) = 3

(I hope the notation is obvious.)

Then I note that the kernels (resp. ranges) have intersection {0}, hence giving

(iii) dim(K+) + dim(K-) \le 3

and

(iv) dim(R+) + dim(R-) \le 3

Then (i) - (iii) combined with (iv) - (ii) gives say dim(R+) = dim(K-) whence we deduce that

dim(K+) + dim(K-) = 3

Finally note that these are the dimensions of the eigenspaces for 1 and -1 and so A is diagonalizable and once we know that the condition on the eigenvalues follows.

I'm not 100% sure that the matrix that we would use to actually DO the diagonalizing is real though.
 
  • #4
That's basically it. Once you know K has three independent eigenvectors with eigenvalues +/-1 you are done. You know P can be taken to be real because your eigenvectors are real. It's just the change of basis from the standard basis to your eigenvectors.
 
  • #5
Thank you so much!
 

1. What is the process of diagonalizing a 3x3 matrix?

Diagonalization is a process used to simplify a matrix by transforming it into a special form called a diagonal matrix. This is achieved by finding a set of eigenvectors and eigenvalues for the original matrix and using them to create a new matrix with the same dimensions but with all non-diagonal elements equal to zero.

2. Why is diagonalizing a 3x3 matrix useful?

Diagonalization is useful in several applications, including solving systems of linear equations, finding the powers of a matrix, and calculating the inverse of a matrix. It also simplifies the matrix and makes it easier to perform calculations with.

3. How do you find the eigenvalues of a 3x3 matrix?

The eigenvalues of a 3x3 matrix can be found by solving the characteristic equation, which is obtained by subtracting the scalar variable from the main diagonal elements and finding the determinant of the resulting matrix. The solutions to this equation will be the eigenvalues of the original matrix.

4. Can all 3x3 matrices be diagonalized?

No, not all 3x3 matrices can be diagonalized. For a matrix to be diagonalizable, it must have a full set of linearly independent eigenvectors. If the matrix does not have this, it cannot be diagonalized.

5. What is the relationship between the eigenvalues and eigenvectors in a 3x3 matrix?

The eigenvalues and eigenvectors in a 3x3 matrix are closely related. The eigenvalues are the scalar values that are associated with the eigenvectors, which are the corresponding non-zero vectors that are transformed only by a scalar multiple when multiplied by the original matrix.

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