What are the eigenvectors for the given matrix A = [1 0 0; -2 1 3; 1 1 -1]?

You should find that the eigenvector you found for eigenvalue -2 is also an eigenvector for eigenvalue 1.Nope, that's not it. As a check, your eigenvalue \lambda and eigenvector x should satisfy the equation Ax = \lambdax, or equivalently, (A - \lambdaI)x = 0.In summary, the conversation discusses finding an invertible matrix X and a diagonal matrix D such that A = XDX^-1, where A is a given matrix. The user has found the eigenvalues of A to be -2, 2, and 1, but is struggling to find the corresponding eigenvectors. After some corrections and checks, it
  • #1
arkturus
27
0

Homework Statement



Given the matrix A = [1 0 0
-2 1 3
1 1 -1]

Find an invertable matrix X and a diagonal matrix D such that A = XDX^-1

Homework Equations


A = XDX^-1

The Attempt at a Solution


I've found that the eigenvalues are -2, 2, and 1, but I'm having issues finding the specific eigenvectors.

For example, with eigenvalue = -2 I get the matrix down to [3 0 0
-2 0 0
0 1 1]

Am I correct in saying that x1 = 0, x2 = 0, and x3 = t, thus the corresponding eigenvector is (0,0,1)^T
 
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  • #2
arkturus said:

Homework Statement



Given the matrix A = [1 0 0
-2 1 3
1 1 -1]

Find an invertable matrix X and a diagonal matrix D such that A = XDX^-1

Homework Equations


A = XDX^-1


The Attempt at a Solution


I've found that the eigenvalues are -2, 2, and 1, but I'm having issues finding the specific eigenvectors.

For example, with eigenvalue = -2 I get the matrix down to [3 0 0
-2 0 0
0 1 1]
This matrix is not completely reduced. The first row should be divided by 3, and then used to eliminate the second row. I would then swap the second and third rows.
arkturus said:
Am I correct in saying that x1 = 0, x2 = 0, and x3 = t, thus the corresponding eigenvector is (0,0,1)^T
Nope, that's not it. As a check, your eigenvalue [itex]\lambda[/itex] and eigenvector x should satisfy the equation Ax = [itex]\lambda[/itex]x, or equivalently, (A - [itex]\lambda[/itex]I)x = 0.
 
  • #3
Thanks. I'm now down to:

1 0 0
0 1 1
0 0 0

It looks like x3 is a free variable, so x3 = t. In that case, x2 = -t and x1 = 0.

Good to go?
 
  • #4
Personally, to find eigenvectors, I prefer to use the basic definition:
Saying that -2 is an eigenvalue means that there exist x, y, z, not all 0, such that
[tex]\begin{bmatrix}1 & 0 & 0 \\ -2 & 1 & 3 \\ 1 & 1 & -1\end{bmatrix}\begin{bmatrix}x \\ y \\ z\end{bmatrix}= \begin{bmatrix}-2x \\ -2y \\ -2z\end{bmatrix}[/tex]
which is the same as the three equations
x= -2x
-2x+ y+ 3z= -2y
x+ y- z= -2z

The first equation, of course, just says that x= 0. The second and third then become
3y+ 3z= 0 and y+ z= 0, both of which reduce to z= -y. Yes, any vector of the form (0, y, -y)= y(0, 1, -1) is an eigenvector corresponding to eigenvalue -2.

Notice that your equations for eigenvalues 2 and 1 are almost the same:
x= 2x
-2x+ y+ 3z= 2y
x+ y- z= 2z
and
x= x
-2x+ y+ 3z= y
x+ y- z= z
 
  • #5
arkturus said:
Thanks. I'm now down to:

1 0 0
0 1 1
0 0 0

It looks like x3 is a free variable, so x3 = t. In that case, x2 = -t and x1 = 0.

Good to go?
See my previous post for how you can check whether this is correct.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are fundamental concepts in linear algebra. Eigenvalues represent the scaling factor of a vector when multiplied by a transformation matrix, while eigenvectors are the corresponding vectors that do not change direction after the transformation.

2. How are eigenvalues and eigenvectors used in real-world applications?

Eigenvalues and eigenvectors have numerous applications in fields such as physics, engineering, and data analysis. They are used to solve systems of differential equations, analyze the stability of systems, and reduce the dimensionality of data for easier analysis.

3. What is the difference between eigenvalues and eigenvectors?

The main difference between eigenvalues and eigenvectors is their mathematical representation. Eigenvalues are scalars, while eigenvectors are vectors. Eigenvectors represent the direction of the vector, while eigenvalues represent the magnitude or scaling factor.

4. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, you need to solve the characteristic equation det(A-λI)=0, where A is the transformation matrix and λ is the eigenvalue. Once you have the eigenvalues, you can find the corresponding eigenvectors by solving the equation (A-λI)x=0.

5. Can a matrix have more than one eigenvalue and eigenvector?

Yes, a square matrix can have multiple eigenvalues and corresponding eigenvectors. In fact, the number of eigenvalues and eigenvectors is equal to the size of the matrix. However, each eigenvalue can have multiple eigenvectors associated with it.

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