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Sunday, July 8, 2012

How to diagonalize a matrix


Consider a matrix
A=\begin{bmatrix}
1 & 2  & 0 \\
0 & 3  & 0 \\
2 & -4 & 2 \end{bmatrix}.
This matrix has eigenvalues
 \lambda_1 = 3, \quad \lambda_2 = 2, \quad \lambda_3= 1.
So A is a 3-by-3 matrix with 3 different eigenvalues, therefore it is diagonalizable. Note that if there are exactly n distinct eigenvalues in an n×n matrix then this matrix is diagonalizable.
These eigenvalues are the values that will appear in the diagonalized form of matrix A, so by finding the eigenvalues of A we have diagonalized it. We could stop here, but it is a good check to use the eigenvectors to diagonalize A.


The eigenvectors of A are
 v_1 = \begin{bmatrix} -1 \\ -1 \\ 2 \end{bmatrix}, \quad v_2 = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}, \quad v_3 = \begin{bmatrix} -1 \\ 0 \\ 2 \end{bmatrix}.
One can easily check that A v_k = \lambda_k v_k.
Now, let P be the matrix with these eigenvectors as its columns:
P=
\begin{bmatrix}
-1 & 0 & -1 \\
-1 & 0  & 0 \\
2 & 1 & 2 \end{bmatrix}.
Note there is no preferred order of the eigenvectors in P; changing the order of the eigenvectors in P just changes the order of the eigenvalues in the diagonalized form of A.
Then P diagonalizes A, as a simple computation confirms:
P^{-1}AP =
\begin{bmatrix}
0 & -1 & 0 \\
2 & 0  & 1 \\
-1 & 1 & 0 \end{bmatrix}
\begin{bmatrix}
1 & 2  & 0 \\
0 & 3  & 0 \\
2 & -4 & 2 \end{bmatrix}
\begin{bmatrix}
-1 & 0 & -1 \\
-1 & 0  & 0 \\
2 & 1 & 2 \end{bmatrix} =
\begin{bmatrix}
3 & 0 & 0 \\
0 & 2 & 0 \\
0 & 0 & 1\end{bmatrix}.
Note that the eigenvalues \lambda_k appear in the diagonal matrix.


Alternative Method

Starting with:  PD = AP , where  P = [\vec{p_1}, \vec{p_2}] , and the Diagonalization matrix  D  is:
D=\begin{bmatrix}
d_{11} & 0 \\
0 & d_{22} \end{bmatrix}
Distribute  A  into the column vectors of  P .
 PD = [A\vec{p_1},A\vec{p_2}]
Then  D  can be broken down to its column vectors as follows:
 P[d_{11}\vec{e_1},d_{22}\vec{e_2}] = [A\vec{p_1},A\vec{p_2}]
Multiplying  P  on the left side of the equation gives:
 [\vec{d_{11}}\vec{p_1}, \vec{d_{22}}\vec{p_2}] = [A\vec{p_1},A\vec{p_2}]
Setting each entry of the matrix to its corresponding entry:
 \vec{d_{11}}\vec{p_1} = A\vec{p_1}
 \vec{d_{22}}\vec{p_2} = A\vec{p_2}
Then the equations can be solved as follows, using the same process for both:
 (d_{11}I)\vec{p_1} = A\vec{p_1}
 (d_{11}I-A)\vec{p_1} = 0
and it solves for  d_{11} , which is the first entry in the diagonal matrix, and also the first eigenvalue.

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