Eigenvector Woes Homework Solution

In summary, the conversation discusses finding eigenvectors for a given matrix. The correct eigenvectors for lambda equals 2 and 6 are found, but there is confusion about why the eigenvector is "hat j" when lambda equals 3. It is pointed out that the third row of the matrix implies that x equals 0, so there are no constraints on y. This clarifies the confusion.
  • #1
kq6up
368
13

Homework Statement



Find the eigenvectors of: ##
\newcommand{\Bold}[1]{\mathbf{#1}}\left(\begin{array}{rrr}

5 & 0 & \sqrt{3} \\

0 & 3 & 0 \\

\sqrt{3} & 0 & 3

\end{array}\right)

##

Homework Equations



##(\mathbf{A}-\lambda\mathbf{I})\cdot\mathbf{x}=0##

The Attempt at a Solution



I get the correct eigenvectors for ##\lambda=2,6##, but I don't understand why the eigenvector is ##\hat{j}## when ##\lambda=3##.

When ##\lambda=3##, the matrix becomes ##
\newcommand{\Bold}[1]{\mathbf{#1}}\left(\begin{array}{rrr}

2 & 0 & \sqrt{3} \\

0 & 0 & 0 \\

\sqrt{3} & 0 & 0

\end{array}\right)

##. The first row yields a function ##2x-\sqrt{3}z=0##. The points that satisfy this equation do not lay along ##\hat{j}##. What am I missing?

Thanks,
Chris
 
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  • #2
The third row implies ##x=0##. Do you see that?
 
  • #3
kq6up said:
The first row yields a function 2x− 3√ z=0
The first row yields the equation 2x + 3 √ z=0.

As vela notes, the third row implies that x = 0. Neither equation involves y, so there are no constraints on y.
 
  • #4
Ah, that makes sense.

Thanks,
Chris
 

1. What is an eigenvector?

An eigenvector is a vector that does not change its direction when a linear transformation is applied to it. It is an important concept in linear algebra and is often used in various fields of science and engineering.

2. Why is finding the eigenvector important?

Finding the eigenvector is important because it allows us to understand the behavior of a linear transformation. It helps us identify the directions along which a transformation stretches or compresses the space.

3. How do you find the eigenvector of a matrix?

To find the eigenvector of a matrix, we first need to find the eigenvalues of the matrix. Then, we can plug these eigenvalues into the equation (A - λI)x = 0, where A is the original matrix, λ is the eigenvalue, and x is the eigenvector. Solving this equation will give us the eigenvector.

4. Can a matrix have more than one eigenvector?

Yes, a matrix can have multiple eigenvectors. In fact, most matrices have more than one eigenvector. However, each eigenvector will correspond to a different eigenvalue.

5. How is the eigenvector used in real-world applications?

The eigenvector has many applications in various fields such as physics, engineering, and data analysis. It is used to identify important features in data, reduce dimensionality, and solve differential equations, among other things.

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