How can x1 be free if its coefficient is zero?

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In summary, the conversation was about finding the eigenvector of a given matrix using the equation Ax=λx. The discussion included solving for x and determining the associated eigenvector, as well as understanding the concept of a free variable in an augmented matrix. The conclusion was that the eigenvector for λ=2 is (1,0,0).
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dietcookie
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Homework Statement


Find the Eigenvector of the matrix.

Homework Equations



Axx

A=

[2 0 1]
[0 3 4]
[0 0 1]

The Attempt at a Solution



Ok I'm just having a major brain fart here, been doing this all day. For λ=2, I solved for x and get this solution,

[0 1 0][x1] [0]
[0 0 1][x2]=[0]
[0 0 0][x3] [0]

The associated eigenvector from the book for λ=2 is (1,0,0) which I know is correct but did I forget how to read a matrix but the augmented matrix I RREFed is as follows,

[0 1 0 0]
[0 0 1 0]
[0 0 0 0]

So x1 and x2 is zero, how is x1=1?
 
Last edited:
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Ok maybe I just figured it out, since x2 and x3 are equal to zero, does that mean that x1 is free? That seems to make sense, but how can it be free when it's coefficient is zero?
 
  • #3
dietcookie said:
Ok maybe I just figured it out, since x2 and x3 are equal to zero, does that mean that x1 is free? That seems to make sense, but how can it be free when it's coefficient is zero?

As you wrote,
[tex]
\left( \begin{array}{ccc}
0 & 1 & 0 \\
0 & 0 & 1 \\
0 & 0 & 0 \end{array} \right)
\left( \begin{array}{c}
x_1 \\
x_2 \\
x_3 \end{array} \right)
=
\left( \begin{array}{c}
0 \\
0 \\
0 \end{array} \right)
[/tex]
This gives you three equations:
[tex]\begin{eqnarray}
0 \cdot x_1 &=& 0 \\
x_2 &=& 0 \\
x_3 &=& 0
\end{eqnarray}[/tex]
However, the first equation tells you nothing about x1. That equation is true regardless of what you set x1 equal to. To 'parameterize' (although it's a little silly in this case), you can let x1 = t, where t is an arbitrary real number. Then, the vector
[tex]\left( \begin{array}{c}
x_1 \\
x_2 \\
x_3 \end{array} \right)
=
\left( \begin{array}{c}
t \\
0 \\
0 \end{array} \right)[/tex]
is a solution for any t. Set t=1 for simplicity, and your eigenvector is
[tex]
\left( \begin{array}{c}
1 \\
0 \\
0 \end{array} \right)[/tex]
 
Last edited:

FAQ: How can x1 be free if its coefficient is zero?

What is an eigenvector?

An eigenvector is a vector that when multiplied by a square matrix produces a scaled version of itself. In other words, the direction of the vector remains unchanged, but its magnitude is multiplied by a scalar value known as the eigenvalue.

Why is finding eigenvectors important?

Finding eigenvectors is important because they represent the directions of maximum variability in a dataset. This can be useful in many applications, such as data compression, image processing, and machine learning.

How can I find eigenvectors?

To find eigenvectors, you first need to calculate the eigenvalues of the matrix. Then, for each eigenvalue, you solve the equation (A - λI)v = 0, where A is the matrix, λ is the eigenvalue, and v is the eigenvector. This will give you a system of linear equations that can be solved to find the eigenvector.

What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related. Eigenvectors give the directions of maximum variability in a dataset, while eigenvalues represent the scaling factor for each eigenvector. The larger the eigenvalue, the more important the eigenvector is in representing the data.

Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors. In fact, it typically has as many eigenvectors as its dimension. However, each eigenvector will have a unique eigenvalue associated with it.

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