Eigenvalues & Similar Matrices

This is because A is similar to a diagonal matrix D, which means that A = PDP^-1 for some invertible matrix P. So A2 = (PDP^-1)(PDP^-1) = PD(P^-1P)DP^-1 = PIAIP-1 = PIP^-1 = I. So A2 = I, which means that A-1 = A.
  • #1
kingwinner
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Q: Suppose the only eigenvalues of A are 1 and -1, and A is similar to a diagonal matrix. Prove that A^-1 = A

My Attempt:
Suppose the only eigenvalues of A are 1 and -1, and A is similar to a diagonal matrix.
=>A is invertible (since 0 is not an eigenvalue of A)
and there exists invertible P s.t.
(P^-1) A P = D is diagonal
=> A= P D (P^-1)
=> A^-1 = P (D^-1) (P^-1)

Now if I can prove that D = D^-1, then I am done. But I am stuck right here. The trouble is that the eignevalues of A can have any number of multiplicities, so D can be diag{1,1,-1,-1,-1}, diag{1,-1,-1,-1,-1,-1}, etc., there are infinite number of possible D's, how can I prove that D = D^-1 is always true for these infinite number of different settings?


Can someone please help me?
Thanks a lot!
 
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  • #2
The inverse of adiagonal matrix is formed by inverting the diagonal elements. 1^(-1)=1 and (-1)^(-1)=-1. What's the problem?
 
  • #3
Whenever you're stuck, write out an example and solve it. If you have done so, you would have noticed what Dick is saying.
 
  • #4
Dick said:
The inverse of adiagonal matrix is formed by inverting the diagonal elements. 1^(-1)=1 and (-1)^(-1)=-1. What's the problem?

How can I prove this statement? I haven't learned this before...
 
  • #5
Follow JasonRox's advice and just do it. Multiplying diagonal matrices just involves multiplying the diagonal elements. c_ij=a_ik*b_kj sum over k. If they are diagonal, c_ii=a_ii*b_ii.
 
  • #6
You don't really need to calculate A-1 at all. Just showing that A2= A[itex]\cdot[/itex]A= I is sufficient.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to understand the behavior of linear transformations. Eigenvalues represent the scalar values that are associated with a particular transformation, while eigenvectors represent the corresponding vectors that are unchanged by the transformation.

2. How are eigenvalues and eigenvectors related to similar matrices?

Similar matrices are matrices that represent the same linear transformation with respect to different bases. The eigenvalues and eigenvectors of similar matrices are the same, although the corresponding eigenvectors may be expressed as linear combinations of different basis vectors.

3. How can I calculate eigenvalues and eigenvectors?

There are several methods for calculating eigenvalues and eigenvectors, including the characteristic polynomial method and the diagonalization method. These methods involve solving systems of equations and using linear algebra techniques such as matrix multiplication and Gaussian elimination.

4. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, including quantum mechanics, computer graphics, and data analysis. They allow us to understand the behavior of linear transformations and to find solutions to systems of linear equations.

5. Can similar matrices have different eigenvalues?

No, similar matrices have the same eigenvalues, although the corresponding eigenvectors may differ. This is because similar matrices represent the same linear transformation, just with respect to different bases. Therefore, the eigenvalues, which represent the behavior of the transformation, must be the same.

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