Finding Eigenvector for 3x3 Matrix: Step-by-Step Guide

In summary, you may be trying to solve for the eigenvalue of a matrix using the eigenvector method, but you may not be following the correct steps.
  • #1
flash123
7
0
any1 can please tell me the eigen vector for following matrix:

[0 0 a
0 0 0
0 0 0]

please elaborate ur answer!
 
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  • #2
Welcome to Physics Forums, flash123!

Have you checked out the rules? Pretty cool stuff there!

In particular, we cannot provide answers to your questions. We can (and will, to the best of our ability) help you find it. So, you have to show us some effort from your side.

If you know nothing about eigenvectors, Google is a good place to start.
 
  • #3
hey sourabh, i did try to solve the question.
i got eigen values as 0, 0 , 0
and after using [A-lambdaI]X=0
i am getting 0X1+0X2+aX3=0
which makes eigen vector as [0 0 0 ]
whereas the answer is [0 0 a]
 
  • #4
[0, 0, 0] cannot an eigenvector. But [0 0 a] is also not correct. The definition of "eigenvalue" is that [itex]\lambda[/itex] is an eigenvalue of A if and only if there exist a non-zero vector, v, such that [itex]Av= \lambda v[/itex]. Yes, the only eigenvalue is 0. Applying the given matrix to a vector [x, y, z] gives [az, 0, 0] and that must be equal 0[x, y, z]= [0, 0, 0] so we must have z= 0. We have NO information about x or y so they can be anything.
 
  • #5
sorry hallsofevy, the correct answer is [a 0 0]
but after solving the eqn [A-lamdaI][X]=0 or AX=lamdaX
i got az+0+0 = 0
so how to proceed further and arrive at the correct answer?
 
  • #6
What do you mean "the answer is ..."? Do you understand what an eigenvector is? If a given vector is an eigenvector so is any multiple of it. You cannot just say "the eigenvector" is any specific vector. I said before, "we must have z= 0. We have NO information about x or y so they can be anything."

That includes your [a, 0, 0], taking x= a, y= 0. It also includes [1, 0, 0], [0, 0 1], and any linear combination x[1, 0, 0]+ y[0, 1, 0]= [x, y, 0].

For any x, y,
[tex]\begin{bmatrix}0 & 0 & a \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{bmatrix}\begin{bmatrix}x \\ y \\ 0\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0\end{bmatrix}= 0\begin{bmatrix}x \\ y\\ 0\end{bmatrix}[/tex]
 
Last edited by a moderator:
  • #7
thanks hallsofevy that was helpful!

since x and y could be anything
so the possible eigen vectors corresponding to eigen value 0 are
[1 0 0],[0 1 0] or [1 1 0]

please tell me am i correct now?
 

Related to Finding Eigenvector for 3x3 Matrix: Step-by-Step Guide

1. What is an eigenvector?

An eigenvector is a vector that, when multiplied by a square matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector remains unchanged after the transformation.

2. What is the importance of finding eigenvectors for a 3x3 matrix?

Finding eigenvectors for a 3x3 matrix is important because it allows us to understand how the matrix transforms space. It also helps us to identify important patterns and relationships within the data represented by the matrix.

3. How do you find the eigenvalues and eigenvectors for a 3x3 matrix?

To find the eigenvalues and eigenvectors for a 3x3 matrix, we first need to find the characteristic polynomial of the matrix. This polynomial will have a degree of 3 and can be solved to find the eigenvalues. Then, for each eigenvalue, we can solve the system of equations represented by the matrix to find the corresponding eigenvector.

4. What do the eigenvalues and eigenvectors represent in a 3x3 matrix?

The eigenvalues represent the scaling factor for each eigenvector. In other words, when multiplied by the eigenvector, the eigenvalue determines how much the vector will be scaled. The eigenvectors represent the directions along which the matrix has a special effect, such as stretching or rotating.

5. Are there any real-life applications of finding eigenvectors for a 3x3 matrix?

Yes, there are many real-life applications of finding eigenvectors for a 3x3 matrix. Some examples include image and signal processing, quantum mechanics, and population dynamics in ecology. It is also commonly used in machine learning and data analysis to identify patterns and relationships in high-dimensional data sets.

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