Regarding Diagonalization of Matrix by Spectral Theorem

In summary, the spectral theorem for self-adjoint operators states that a matrix P can be found such that P^{-1}AP is diagonal. This same theorem can be applied to a square and symmetric matrix of the form [U 0 U; 0 0 0; U 0 U] where U is a square matrix and 0 is a matrix of zeros. The solution is that the columns of P are the eigenvectors of A. To confirm this, the matrix can be transformed into a diagonal form by using the transformation shown above. This reduces the problem to the diagonalization of a self-adjoint operator, where U is now diagonalized.
  • #1
JustYouAsk
5
0
According to the spectral theorem for self-adjoint operators you can find a matrix P such that P[tex]^{-1}[/tex]AP is diagonal, i.e. P[tex]^{T}[/tex]AP (P can be shown to be orthogonal). I'm not sure what the result is if the same can be done for the following square (size n X n) and symmetric matrix of the form:
A=
[ U 0 U ]
[ 0 0 0 ]
[ U 0 U ]

where U is square matrix and 0 is a matrix of zeros.

If I am not mistaken the solution is that the columns of P are simply the eigenvectors of A? can anyone confirm this?
 
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  • #2
First if [itex]A[/itex] is Hermitian then [itex]U[/itex] is also Hermitian, then use the transformation,

[tex]
\left[ {\begin{array}{*{20}c}
I & 0 & 0 \\
0 & I & 0 \\
{ - I} & 0 & I \\
\end{array}} \right]\left[ {\begin{array}{*{20}c}
U & 0 & U \\
0 & 0 & 0 \\
U & 0 & U \\
\end{array}} \right]\left[ {\begin{array}{*{20}c}
I & 0 & { - I} \\
0 & I & 0 \\
0 & 0 & I \\
\end{array}} \right] = \left[ {\begin{array}{*{20}c}
U & 0 & 0 \\
0 & 0 & 0 \\
0 & 0 & 0 \\
\end{array}} \right]

[/tex]

Now, we are back in the business because now it is the case where you start the argument, diagonalization of a self adjoint operator where we diagonalize [itex]U[/itex] now
 

Related to Regarding Diagonalization of Matrix by Spectral Theorem

1. What is the spectral theorem for diagonalization of matrices?

The spectral theorem is a mathematical theorem that states that any square matrix with distinct eigenvalues can be diagonalized by a similarity transformation. This means that the matrix can be transformed into a diagonal matrix by finding a set of eigenvectors and using them to create a new basis for the matrix.

2. Why is diagonalization of matrices important?

Diagonalization of matrices is important because it allows for easier computation and analysis of the matrix. Diagonal matrices have simpler properties and are easier to manipulate, making it easier to solve linear equations, find powers of matrices, and calculate determinants.

3. How do you use the spectral theorem to diagonalize a matrix?

To use the spectral theorem to diagonalize a matrix, you first need to find the eigenvalues and eigenvectors of the matrix. Then, you can use these eigenvectors to create a new basis for the matrix. Finally, you can use the diagonalization formula to transform the matrix into a diagonal matrix.

4. What is the difference between eigenvalues and eigenvectors?

Eigenvalues are scalar values that represent how a linear transformation stretches or compresses a vector in a given direction. Eigenvectors are the corresponding vectors that are only scaled by the eigenvalue when the transformation is applied. In the context of diagonalization, eigenvectors are used to create a new basis for the matrix.

5. Can any matrix be diagonalized using the spectral theorem?

No, not all matrices can be diagonalized using the spectral theorem. The matrix must have distinct eigenvalues in order for the theorem to apply. If the matrix does not have distinct eigenvalues, it can still be diagonalized using other methods, such as Jordan canonical form.

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