Eigenvalue and Eigenvector problem

In summary, the conversation discussed a problem involving finding eigenvalues and eigenvectors of a given matrix. The correct eigenvalues were found to be -1, 5, and 5, with the eigenvalue of 5 having a multiplicity of two. The process of finding eigenvectors for each eigenvalue was also discussed, with the first eigenvector for lambda=-1 being [1+2i, 0, -1]^T. The conversation also touched on the process of row reducing the matrix to find eigenvectors, with uncertainty about the results obtained.
  • #1
Mathman23
254
0
Hi

Given a 3x3 matrix


[tex]A = \[ \left[ \begin{array}{ccc} 0 & 0 & 1+2i \\ 0 & 5 & 0 \\ 1-2i & 0 & 4 \end{array} \right][/tex]

I need to a another 3x3 which satisfacies

D = U^-1 A U

Step 1.

Finding the eigenvalues

[tex]0 = det(A- \lambda I ) = (0- \lambda)(\lambda - 5) (\lambda -4 ), \lambda = 5,4,0[/tex]

step 2.

Finding the eigenvectors.

A vector which satisfies (A-\lambda I) v = 0

For \lambda = 5


p(\lambda = 5) = [tex] \[ \left[ \begin{array}{ccc} -5 & 0 & 1+2i \\ 0 & 0 & 0 \\ 1-2i & 0 & -1 \end{array} \right][/tex] ~ [tex]\[ \left[ \begin{array}{ccc} 1 & 0 & -1/5-2/5i \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array} \right][/tex]

How do I read the eigenvector from the reduced matrix ?

Sincerely Fred
 
Last edited:
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  • #2
Your eigenvalues are wrong. Find the characteristic equation by working it out completely, and you'll find eigenvalues of -1, 5, and 5.
 
  • #3
Is 5 then a what is called a double root ?

/Fred

daveb said:
Your eigenvalues are wrong. Find the characteristic equation by working it out completely, and you'll find eigenvalues of -1, 5, and 5.
 
  • #4
Kind of. The eigenvalue with the value of five is said to have a multiplicity of two.
 
  • #5
I found the first eigenvector for lambda = -1 to be [1+2i, 0, -1]^T

To find for lambda = 5, do I row reduce the matrix A-5I ?

If I put that matrix into reduced echelon form, I get [0,0,0], but that can't be right?

/Fred

Hammie said:
Kind of. The eigenvalue with the value of five is said to have a multiplicity of two.
 
Last edited:

What is the definition of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra that are used to describe the behavior of a matrix when it is multiplied by a vector. An eigenvector is a non-zero vector that, when multiplied by a square matrix, results in a scalar multiple of itself. This scalar multiple is called the corresponding eigenvalue.

What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are used to understand the behavior of linear transformations and systems of linear equations. They are also used in a variety of fields such as physics, engineering, and data analysis to help solve complex problems and make predictions.

How do you find eigenvalues and eigenvectors?

To find the eigenvalues and eigenvectors of a matrix, one can solve the characteristic equation det(A-λI)=0, where A is the matrix, λ is the eigenvalue, and I is the identity matrix. The solutions to this equation are the eigenvalues, and the corresponding eigenvectors can be found by substituting the eigenvalues back into the equation (A-λI)x=0 and solving for x.

What is the relationship between eigenvectors and eigenspaces?

Eigenspaces are the set of all eigenvectors associated with a particular eigenvalue. Each eigenvalue has its own eigenspace, and the dimension of the eigenspace is equal to the multiplicity of the eigenvalue. This means that there can be multiple eigenvectors associated with the same eigenvalue.

Can a matrix have complex eigenvalues and eigenvectors?

Yes, a matrix can have complex eigenvalues and eigenvectors. This is because the characteristic equation can result in complex solutions, and the corresponding eigenvectors can also be complex numbers. In fact, for matrices with real entries, complex eigenvalues and eigenvectors often occur in pairs.

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