Edu - "How to Find Eigenvectors of a Matrix: A Step-by-Step Guide

In summary, to find the eigenvalues and eigenvectors corresponding to the matrix A = \left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right], we first find the determinant of (λI-A) and set it equal to 0. Solving for λ, we get two eigenvalues, -2 and 5. To find the eigenvectors corresponding to these eigenvalues, we set up a system of equations and solve for one variable in terms of the other. This gives us the eigenspaces, which are the set of all solutions to the system and form a subspace. The eigenvectors can then be written as a multiple of a
  • #1
roam
1,271
12
1. Find the eigenvalues and the eigenvectors corresponding to eigenvalues of the matrix


A = [tex]\left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right][/tex]



3. The Attempt at a Solution


[tex](\lambda I - A)[/tex] = [tex]\lambda \left[\begin{array}{ccccc} 1 & 0 \\ 0 & 1 \end{array}\right] -[/tex] [tex]\left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right][/tex]

[tex]\left[\begin{array}{ccccc} \lambda - 1 & -3 \\ -4 & \lambda - 2 \end{array}\right][/tex] [tex]\left(\begin{array}{ccc}x\\y\end{ar ray}\right) =[/tex] [tex]\left(\begin{array}{ccc}0\\0\end{ar ray}\right)[/tex]


det(λI - A) = 0
=> (λ-1)(λ-2)-12 = 0
λ2-3λ-10=0
(λ+2)(λ-5) = 0
λ = -2, 5

My problem is how to find the eigenspaces corresponding to these eigenvalues.

We have two cases, the first one is when [tex]\lambda = 5[/tex]. In this case we have the following:

[tex]\left[\begin{array}{ccccc} 4 & -3 \\ -4 & 3 \end{array}\right][/tex] [tex]\left(\begin{array}{ccc}x\\y\end{ar ray}\right) =[/tex] [tex]\left(\begin{array}{ccc}0\\0\end{ar ray}\right)[/tex]

So to find the eigenvectors corresponding to [tex]\lambda = 5[/tex], I think I should solve the system

[tex]\left[\begin{array}{ccccc} 4 & -3 \\ -4 & 3 \end{array}\right][/tex]

4x-3y = 0 ...(1)
-4x+3y = 0 ...(2)

How can I solve this? I'm not sure how this is done (if I minus (1) from (2) to eliminate x then the y would be eliminated as well).

Thanks.

Roam

 
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  • #2
Notice that if (x,y) is an eigenvector with a certain eigenvalue, so is (cx,cy), where c is any constant. So you cannot expect to find unique values for x and y, but only their relative value. Either of your equations will do this for you (and, of course, give the same answer).
 
  • #3
roam said:
1. Find the eigenvalues and the eigenvectors corresponding to eigenvalues of the matrix


A = [tex]\left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right][/tex]



3. The Attempt at a Solution


[tex](\lambda I - A)[/tex] = [tex]\lambda \left[\begin{array}{ccccc} 1 & 0 \\ 0 & 1 \end{array}\right] -[/tex] [tex]\left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right][/tex]

[tex]\left[\begin{array}{ccccc} \lambda - 1 & -3 \\ -4 & \lambda - 2 \end{array}\right][/tex] [tex]\left(\begin{array}{ccc}x\\y\end{ar ray}\right) =[/tex] [tex]\left(\begin{array}{ccc}0\\0\end{ar ray}\right)[/tex]


det(λI - A) = 0
=> (λ-1)(λ-2)-12 = 0
λ2-3λ-10=0
(λ+2)(λ-5) = 0
λ = -2, 5

My problem is how to find the eigenspaces corresponding to these eigenvalues.

We have two cases, the first one is when [tex]\lambda = 5[/tex]. In this case we have the following:

[tex]\left[\begin{array}{ccccc} 4 & -3 \\ -4 & 3 \end{array}\right][/tex] [tex]\left(\begin{array}{ccc}x\\y\end{ar ray}\right) =[/tex] [tex]\left(\begin{array}{ccc}0\\0\end{ar ray}\right)[/tex]

So to find the eigenvectors corresponding to [tex]\lambda = 5[/tex], I think I should solve the system

[tex]\left[\begin{array}{ccccc} 4 & -3 \\ -4 & 3 \end{array}\right][/tex]

4x-3y = 0 ...(1)
-4x+3y = 0 ...(2)

How can I solve this? I'm not sure how this is done (if I minus (1) from (2) to eliminate x then the y would be eliminated as well).

Yes, that's exactly right! If you could solve the two equations for a single value of x and y, then there would be only one possible solution- and x= 0, y= 0 obviously satisfies it. The whole point of an "eigenvalue" is that there exist "non-trivial" (i.e. not x=0, y=0) solutions. And once that is true, there exist an infinite number of solutions: the set of all solutions to such an equation for an eigenvalue forms a subspace, the "eigen space".
What you can do is solve for one variable in term of the other. Here, for examply 4x= 3y so y= (4/3)x. Any vector of the form (x, (4/3)x) is in the eigenspace. You can also write that as x(1, 4/3) showing that any such vector is a multiple of (1, 4/3): the subspace is of dimension 1 and a basis for it is {(1, 4/3)}. Of course, if you don't like fractions, you could just factor a "3" out of that x and write (x/3)(3, 4). Since x could be any number, so can "x/3" and {(3, 4)} is a basis for the eigenspace corresponding to eigenvalue 5.

Similarly, for eigenvalue -2x you have
[tex]\left[\begin{array}{cc} 1 & 3 \\ 4 & 2 \end{array}\right]\left[\begin{array}{c} x \\ y \end{array}\right]= left[\begin{array}{c}-2x \\ -2y\end{array}\right][/tex]
That is the same as x+ 3y= -2x or 3x+ 3y= 0 and 4x+ 2y= -2y or 4x+ 4y= 0. What x and y satisfy that? Again, you cannot solve for specific x or y but you can solve for y in terms of x or viceversa.
 
  • #4
...yes!

4x-3y = 0
-4x+3y = 0

x = 3/4y ( y is free)

x = [tex]\left(\begin{array}{ccc}x\\y\end{ar ray}\right) =[/tex] [tex]\left(\begin{array}{ccc}3/4 y\\y\end{ar ray}\right) =[/tex] [tex]y \left(\begin{array}{ccc}3/4\\1\end{ar ray}\right) [/tex]


Ax = λx

Ax = [tex]\left[\begin{array}{ccccc} 1 & 3 \\ 4 & 2 \end{array}\right][/tex] [tex]\left(\begin{array}{ccc}3/4 y\\y\end{ar ray}\right) =[/tex] [tex]5 \left(\begin{array}{ccc}3/4 y\\y\end{ar ray}\right) \Rightarrow[/tex] [tex]\left(\begin{array}{ccc}15/4 y\\5 y\end{ar ray}\right)[/tex]

= 5x

(I know how to do the computations for the second case, [tex]\lambda = -2[/tex] )

I understand that system (λI-A) = 0 has non-trivial solutions.

Thanks very much for explaining, Best wishes.
 

1. What are eigenvectors and why are they important?

Eigenvectors are special vectors that represent the directions along which a linear transformation acts by stretching or compressing a vector. They are important because they provide a simplified representation of a complex transformation, making it easier to analyze and understand.

2. How do you find eigenvectors?

To find eigenvectors, one needs to first calculate the eigenvalues of a given matrix. These eigenvalues are then used to solve for the corresponding eigenvectors by plugging them into the characteristic equation and solving for the variables.

3. What are the applications of eigenvectors?

Eigenvectors have various applications in different fields such as physics, engineering, and computer science. They are used to analyze physical systems, image processing, data compression, and in machine learning algorithms.

4. Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors for a given eigenvalue. In fact, a matrix can have an infinite number of eigenvectors if the eigenvalue has a multiplicity greater than 1.

5. How do eigenvectors relate to eigenvalues?

Eigenvectors and eigenvalues are closely related. Eigenvectors represent the directions along which a linear transformation acts, while eigenvalues represent the scale at which the transformation acts along those directions. Together, they provide a complete understanding of the transformation.

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