What is the method for finding the eigenvector for A-0I?

In summary: There are infinitely many possible eigenvectors corresponding to lambda=0, but we only need 2, so i would suggest picking 2 different values for x1, x3 and finding the corresponding x2 and then expressig the solutions in the form given by vela.
  • #1
ganondorf29
54
0

Homework Statement


A = [2,1,2;2,1,2;2,1,2]

Find the Eigenvectors of A



Homework Equations





The Attempt at a Solution



First I found the eigenvalues of A
[tex]
det(A - \lambda I) = 0
[/tex]


[tex]
\lambda = 0,5
[/tex]

__________________________________________
A-5I

[-3,1,2;2,-4,2;2,1,-3] --(rref)--> [1,0,-1;0,1,1;0,0,0]

x1 = x3
x2 = -x3

v1 = [1;-1;1]

Is this the right eigenvector for A-5I?
_______________________________________________

A-0I

[2,1,2;2,1,2;2,1,2] --(rref)--> [1,1/2,1;0,0,0;0,0,0]

x1 + x2/2 + x3 = 0


I'm not sure what to do in order to find the eigenvector for A-0I
 
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  • #2
the characteristic equation gives you a bit more help than that
[tex] A = \begin{pmatrix}
2 & 1 & 2 \\
2 & 1 & 2 \\
2 & 1 & 2
\end{pmatrix}
[/tex]

[tex] A - \lambda I = \begin{pmatrix}
2- \lambda & 1 & 2 \\
2 & 1- \lambda & 2 \\
2 & 1 & 2- \lambda
\end{pmatrix}
[/tex]

taking the determinant simplifies to
[tex] \lambda^2(5 -\lambda) [/tex]

giving eigenvalues:
5 with algebraic multiplicity of 1
0 with algebraic multiplicity of 2

the goemtric multiplicity (number of linearly independent eignevectors) is always less than or equal to the algebraic multiplicity

this means there will be one eignevector corresponding to [itex] \lambda = 5 [\itex] and upto 2 corresponding to [itex] \lambda = 0 [\itex]
 
Last edited:
  • #3
ganondorf29 said:
A-5I

[-3,1,2;2,-4,2;2,1,-3] --(rref)--> [1,0,-1;0,1,1;0,0,0]

x1 = x3
x2 = -x3

v1 = [1;-1;1]

Is this the right eigenvector for A-5I?
_______________________________________________

to check if its right try multiplying the vector by the matrix

[tex] A.v_1 =
\begin{pmatrix} 2 & 1 & 2 \\2 & 1 & 2 \\2 & 1 & 2 \end{pmatrix}
\begin{pmatrix} 1 \\-1 \\1 \end{pmatrix}
= \begin{pmatrix}3 \\3 \\3 \end{pmatrix}
\neq 5 v_1
[/tex]

so this is not correct
 
  • #4
For A-5I
x1 = x3
x2 = x3

so [1;1;1] is the eigenvector for [tex] \lambda = 5 [/tex]

[2,1,2;2,1,2;2,1,2] * [1;1;1] = [5;5;5]


But I'm still not sure how to find the eigenvectors for A-0I
 
  • #5
You have x1 + x2/2 + x3 = 0. You can solve for anyone of the variables in terms of the other two, so you have two degrees of freedom. So let x2=s and x3=t. Express the solutions in the form (x1, x2, x3) = s(some vector) + t(some vector). Those two vectors will be the eigenvectors.
 
  • #6
ollowing on from Vela
x1 + x2/2 + x3 = 0
is the equaton of a plane through the origin, which can be spanned by any two linearly independent vectors lying in that plane, solving for x2 in term so of x1 & x3, gives
x2 = -2(x1 + x3)

note vela suggested solving for x1 first, which is fine, but i like the symmetry soving for x2

then let x1=s, x3=t and follow on, note this is equivalent to solving the 2 cases below
a) x1=1, x3=0
b) x1=0, x3=1
whih may be easier to see first time round
 

1. What are eigenvectors and why are they important in linear algebra?

Eigenvectors are special vectors that, when multiplied by a square matrix, produce a scalar multiple of itself. This scalar multiple is known as the eigenvalue. Eigenvectors are important in linear algebra because they represent the directions along which a linear transformation acts by simply scaling the vector. This makes them useful for understanding and solving systems of linear equations, as well as for studying the properties of matrices.

2. How do you find eigenvectors of a matrix?

To find the eigenvectors of a matrix, we first need to find the eigenvalues by solving the characteristic equation det(A-λI)=0, where A is the matrix and λ is the eigenvalue. Once we have the eigenvalues, we can plug them back into the equation (A-λI)x=0 and solve for x to find the corresponding eigenvectors.

3. Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors associated with the same eigenvalue. This is because the eigenvectors of a matrix are not unique and can be scaled by any non-zero constant and still be considered eigenvectors. However, each eigenvalue will have at least one corresponding eigenvector.

4. What is the significance of the eigenvectors with zero eigenvalues?

Eigenvectors with zero eigenvalues have no meaningful direction, as multiplying them by the matrix will result in the zero vector. However, they can still provide useful information about the matrix, such as the null space or kernel of the matrix.

5. How are eigenvectors used in data analysis and machine learning?

Eigenvectors are commonly used in data analysis and machine learning for dimensionality reduction. By only keeping the eigenvectors with the largest eigenvalues, we can reduce a high-dimensional dataset into a lower-dimensional one while still retaining most of the important information. This can help with visualizing and understanding complex datasets, as well as improving the efficiency and accuracy of machine learning algorithms.

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