Finding Eigenvectors for 4x4 Matrix A = 4 2, 0 1 | Homework Help

  • Thread starter Dissonance in E
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So any vector of the form <x, 0> is an eigenvector for eigenvalue 4.How did you find those eigenvalues? Using det(a - lambdaI) = 0 The eigenvalues of a triangular (or diagonal) matrix are just the numbers on the main diagonal. Lambda = 4 gives 0 2 x V1 = 0 0 -3 V2 0v1 + 2v2 = 0 0v1 - 3v2 = 0 Eigenvector = 0 , 0 No, you only have 2v2= 0 and -3v2
  • #1
Dissonance in E
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Homework Statement


I have matrix
A = 4 2
0 1

Whose eigenvalues I found to be 4 & 1

I need to find the eigenvectors for the same matrix

Homework Equations


(A-lambdaI)V=0

The Attempt at a Solution


Lambda = 4 gives
0 2 x V1 = 0
0 -3 V2

0v1 + 2v2 = 0
0v1 - 3v2 = 0

Eigenvector = 0 , 0
How does that give me any choice? Either there's some fundamental rule I am missing or my maple skills need a workout as according to maple the vector is 1, 0
which would give 1+0 = 0 ? No?

Lambda = 1 gives

3 2 x V1 = 0
0 0 V2

3v1 + 2v2 = 0
0v1 + 0 v2 = 0

eigenvector = -2, 3
Ok I get that this works, why couldn't I say that the eigenvector is 2, -3?
wouldnt this give 3(2) + 2(-3) = 0
Whats the difference?

Clarifications greatly appreciated.

P.s: Sorry for the painful format, couldn't figure out how to draw matrices with latex.
 
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  • #2
How did you find those eigenvalues?
 
  • #3
Using det(a - lambdaI) = 0
 
  • #4
[tex]\lambda =4 \Rightarrow V_2=0[/tex] so [tex]V_1[/tex] can take any value, including 1.

[tex]\lambda =1 \Rightarrow 3V_1=-2V_2[/tex]. Therefore the eigenvector you suggested is just as valid as the one you found using Maple.
 
Last edited:
  • #5
Donaldos said:
How did you find those eigenvalues?

Dissonance in E said:
Using det(a - lambdaI) = 0
The eigenvalues of a trianguar (or diagonal) matrix are just the numbers on the main diagonal.

Lambda = 4 gives
0 2 x V1 = 0
0 -3 V2

0v1 + 2v2 = 0
0v1 - 3v2 = 0

Eigenvector = 0 , 0
No, you only have 2v2= 0 and -3v2= 0 which are satisfied as long as v2= 0. v1 can be anything. An eigenvector for eigenvalue 4 is any vector of the form <x, 0>.

The definition of "eigenvalue" is that there exist a non-zero vector such that [itex]Av= \lambda v[/itex]
Here you must have
[tex]\begin{bmatrix}4 & 2 \\ 0 & 1\end{bmatrix}\begin{bmatrix}x \\ y\end{bmatrix}[/tex]
[tex]= \begin{bmatrix}4x+ 2y \\ y\end{bmatrix}= \begin{bmatrix}4x \\ 4y\end{bmatrix}[/tex]
which gives you the two equations 4x+ 2y= 4x and 4y= 4y. The first equation reduces to 2y= 0 which gives y= 0 and the second equation is automatically satisfied. Again, x can be anything.
 
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Related to Finding Eigenvectors for 4x4 Matrix A = 4 2, 0 1 | Homework Help

1. What is an eigenvector?

An eigenvector is a vector that, when multiplied by a given matrix, results in a scalar multiple of itself. This scalar multiple is known as the eigenvalue associated with the eigenvector.

2. Why are eigenvectors important?

Eigenvectors are important because they help us understand the behavior of a linear transformation or a matrix. They also have various applications in fields such as physics, engineering, and data analysis.

3. How do you find eigenvectors?

To find eigenvectors, we first need to find the eigenvalues of the given matrix. This can be done 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.

4. Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors for the same eigenvalue. In fact, the number of eigenvectors corresponding to an eigenvalue is equal to the multiplicity of that eigenvalue.

5. How are eigenvectors used in data analysis?

Eigenvectors are used in data analysis to reduce the dimensionality of a dataset. By finding the eigenvectors of the covariance matrix of the data, we can identify the most important features or variables that contribute to the variations in the data. This can help with visualizing and understanding the data, as well as making predictions and classifications.

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