Finding the Diagonalizing Matrix for A

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In summary, when trying to solve for the eigenvalues of a matrix using the eigenvalue equation, one can get a different solution than what is given in the textbook if they use an eigenvector that does not satisfy the equation. However, once one finds an eigenvector that satisfies the equation, the rest of the solution is easy.
  • #1
bennyska
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Homework Statement


find the diagonalizing matrix P of the matrix A
A=:
1 3 0
0 2 0
0 0 2



Homework Equations





The Attempt at a Solution


so i do the whole A-[tex]\lambda[/tex]I3 thing and i find my eigenvalues to be [tex]\lambda = 1, 2[/tex]
when i do [tex]\lambda[/tex] = 1, i get the matrix
0 3 0
0 1 0
0 0 1

which turns into
0 1 0
0 0 1
0 0 0

i guess here's my main question. can i have any eigenvectors from here that satisfy this equation? I'm getting a different solution than the book, but it's been a while since I've taken linear algebra (this is a differential equations class and this parts review), and i seem to recall that this doesn't need to be like the book (i.e. the book presents one possible solution; as long as my solution is linearly independent, it should be valid).
...
okay, so i just plugged it in and found out I'm incorrect.

so how do i find the solution to
0 1 0 | 0
0 0 1 | 0
0 0 0 | 0?
 
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  • #2


What equations correspond to that augmented matrix?
 
  • #3


And the first row of your second matrix should be [-1,0,0].
 
  • #4


Dick said:
And the first row of your second matrix should be [-1,0,0].

why? i thought the whole point of doing the eigenvalue was to get that to be zero? (at least in this part, when the particular eigenvalue cancels that out)
 
  • #5


bennyska said:
why? i thought the whole point of doing the eigenvalue was to get that to be zero? (at least in this part, when the particular eigenvalue cancels that out)

And I thought you were subtracting the eigenvalue from the matrix diagonal.
 
  • #6


the original matrix:
1 3 0
0 2 0
0 0 2

eigenvalues 1, 2, 2

subtracting 1*identity

0 3 0
0 1 0
0 0 1

isn't that right?
 
  • #7


Yes, it is. I was thinking one of your matrices was the one where you subtracted 2*identity. Now you need to find the null space of that matrix.
 
  • #8


I believe that was his original problem - finding the null space.
 
  • #9


bennyska said:

Homework Statement


find the diagonalizing matrix P of the matrix A
A=:
1 3 0
0 2 0
0 0 2
I have never particularly liked that method of finding eigenvectors- I prefer working directly from the definition.

It is obvious that the eigenvalues of A are 1 and 2 with 2 being a double root of the eigenvalue equation.

Any eigevector corresponding to eigenvalue 1 must satify
[tex]\begin{bmatrix}1 & 3 & 0 \\ 0 & 2 & 0 \\ 0 & 2 & 0\end{bmatrix}\begin{bmatrix} x \\ y \\ z\end{bmatrix}= \begin{bmatrix} x \\ y \\ z\end{bmatrix}[/tex]

Multiplying that out we get x+ 3y= x, 2y= y, and 2z= z. The only solutions to the last two equations are y= z= 0 and any value of x then satifies the first. An eigenvector corresponding to eigenvalue 1 is <x, 0, 0>= x<1, 0, 0>.

Any eigenvector corresponding to eigenvalue 2 must satisfy
[tex]\begin{bmatrix}1 & 3 & 0 \\ 0 & 2 & 0 \\ 0 & 2 & 0\end{bmatrix}\begin{bmatrix} x \\ y \\ z\end{bmatrix}= \begin{bmatrix} 2x \\ 2y \\ 2z\end{bmatrix}[/tex]

Multiplying that out, we get the 3 equations x+ 3y= 2x, 2y= 2y, and 2z= 2z. The first equation gives us x= 3y and any values of y and z satisfy the last two. An eigenvector corresponding to eigenvalue 2 must be of the form <3y, y, z> = y<3, 1, 0>+ z<0, 0, 1>. That is, {<1, 0, 0>, <3, 1, 0>, <0, 0, 1>} is a basis for the entire space consisting of eigenvectors of A. If we take P to be the matrix having those vectors as columns:
[tex]P= \begin{bmatrix}1 & 3 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{bmatrix}[/tex]
then P-1AP is a diagonal matrix.



Homework Equations





The Attempt at a Solution


so i do the whole A-[tex]\lambda[/tex]I3 thing and i find my eigenvalues to be [tex]\lambda = 1, 2[/tex]
when i do [tex]\lambda[/tex] = 1, i get the matrix
0 3 0
0 1 0
0 0 1

which turns into
0 1 0
0 0 1
0 0 0

i guess here's my main question. can i have any eigenvectors from here that satisfy this equation? I'm getting a different solution than the book, but it's been a while since I've taken linear algebra (this is a differential equations class and this parts review), and i seem to recall that this doesn't need to be like the book (i.e. the book presents one possible solution; as long as my solution is linearly independent, it should be valid).
...
okay, so i just plugged it in and found out I'm incorrect.

so how do i find the solution to
0 1 0 | 0
0 0 1 | 0
0 0 0 | 0?[/QUOTE]
 

What is a diagonalizing matrix?

A diagonalizing matrix is a square matrix that can be used to transform a given matrix into a diagonal matrix. This means that all the non-zero elements of the diagonalizing matrix are on the main diagonal, and all the other elements are zero. This process is also known as diagonalization.

Why do we need to find the diagonalizing matrix for a matrix A?

Finding the diagonalizing matrix for a matrix A allows us to simplify and solve complex systems of equations. It also helps us identify important properties of the original matrix, such as its eigenvalues and eigenvectors.

How do we find the diagonalizing matrix for a matrix A?

To find the diagonalizing matrix for a matrix A, we need to follow a set of steps, which include finding the eigenvalues and eigenvectors of A, constructing the matrix of eigenvectors, and then multiplying it by a diagonal matrix of the corresponding eigenvalues.

What are the benefits of having a diagonalizing matrix?

Having a diagonalizing matrix allows us to simplify computations and perform operations on a matrix more efficiently. It also helps us better understand the structure and behavior of the original matrix and its relationship with its eigenvalues and eigenvectors.

Can any matrix be diagonalized?

Not all matrices can be diagonalized. A matrix can only be diagonalized if it meets certain criteria, such as being a square matrix and having a complete set of linearly independent eigenvectors. However, even if a matrix cannot be diagonalized, it can still be transformed into a similar matrix that is diagonalizable.

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