Eigenvalues and eigenvectors

In summary: So, if ##\lambda = -1##, and x = (-5,2), what is Ax? Is it equal to ##\lambda x = (-1)(-5,2) = (5,-2)##? Similarly, what is Ax if ##\lambda = 6## and x = (1,1)? Is it equal to ##\lambda x = (6,6)##?
  • #1
says
594
12

Homework Statement


Given the linear transformation l : R 2 → R 2 defined below, find characteristic equation, real eigenvalues and corresponding eigenvectors. a) l(x, y) = (x + 5y, 2x + 4y)

Homework Equations


characteristic equation = det (A-λI) = 0

The Attempt at a Solution


l(x, y) = (x + 5y, 2x + 4y)

A =
[ 1 + 5 ]
[ 2 + 4 ]

det (A-λI) = 0 =
[ 1-λ + 5 ]
[ 2 + 4-λ ]

determinant works out to be

D =
2 - 5λ - 6) = 0

so eigenvalues are -1 and 6.

D =
[-1 0]
[ 0 6]

to get eigenvectors we multiply the original matrix A by D

[1 5 ] [ -1 0]
[ 2 4] [ 0 -1]

=
[ -1 -5 ]
[ -2 -4 ]

This is where I'm stuck

=
[ -1 -5 ] * [x] = 0
[ -2 -4 ] [y]

-x-5y = 0
-2x-4y = 0

I can't see how x in the first equation can = x in the second equation. The same with both y's. How can I find the eigenvector here? I'm going back through my steps now to see if I made an error somewhere. I'm having the same trouble finding eigenvectors with the second eigenvalue of 6 as well.
 
Last edited:
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  • #2
says said:

Homework Statement


Given the linear transformation l : R 2 → R 2 defined below, find characteristic equation, real eigenvalues and corresponding eigenvectors. a) l(x, y) = (x + 5y, 2x + 4y)

Homework Equations


characteristic equation = det (A-λI) = 0

The Attempt at a Solution


l(x, y) = (x + 5y, 2x + 4y)

A =
[ 1 + 5 ]
[ 2 + 4 ]

det (A-λI) = 0 =
[ 1-λ + 5 ]
[ 2 + 4-λ ]

determinant works out to be

D =
2 - 5λ - 6) = 0

so eigenvalues are -1 and 6.

D =
[-1 0]
[ 0 6]

to get eigenvectors we multiply the original matrix A by D
No.
To get the eigenvector for ##\lambda = -1## solve this matrix equation:
##\begin{bmatrix} 1- (-1) & 5 \\
2 & 4 - \lambda\end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix} = \begin{bmatrix} 0 \\ 0\end{bmatrix}##
says said:
[1 5 ] [ -1 0]
[ 2 4] [ 0 -1]

= [ -1 -5 ]
[ -2 -4 ]

This is where I'm stuck

= [ -1 -5 ] * [x] = 0
[ -2 -4 ] [y]

-x-5y = 0
-2x-4y = 0

I can't see how x in the first equation can = x in the second equation. The same with both y's. How can I find the eigenvector here? I'm going back through my steps now to see if I made an error somewhere. I'm having the same trouble finding eigenvectors with the second eigenvalue of 6 as well.
 
Last edited:
  • #3
Sorry, how did you get that matrix? Should the 6 be a 5?
 
  • #4
I got an eigenvector for λ=−1 of (-5,2)
 
  • #5
eigenvector for λ=6 of (1,1)
 
  • #6
says said:
Sorry, how did you get that matrix? Should the 6 be a 5?
Yes, I must have hit the wrong key. I have fixed it in my earlier post.

says said:
I got an eigenvector for λ=−1 of (-5,2)
Yes.

says said:
eigenvector for λ=6 of (1,1)
Yes.

You can check these yourself. If ##\lambda## is an eigenvalue associated with an eigenvector of x, it must be true that Ax = λx.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to analyze linear transformations. An eigenvalue is a scalar value that represents how the transformation stretches or compresses in a particular direction, while an eigenvector is a vector that remains in the same direction after the transformation.

2. How are eigenvalues and eigenvectors calculated?

To calculate eigenvalues and eigenvectors, we first need to find the characteristic polynomial of the transformation matrix. Then, we solve the characteristic polynomial to find the eigenvalues, which will be the roots of the polynomial. Finally, we plug in the eigenvalues into the equation (A - λI)v = 0 to find the corresponding eigenvectors.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important in many fields of mathematics and science, such as physics, engineering, and computer science. They can help us understand the behavior of linear transformations, identify key features of a dataset or system, and simplify complex calculations.

4. Can a matrix have more than one eigenvalue and eigenvector?

Yes, a matrix can have multiple eigenvalues and corresponding eigenvectors. In fact, the number of eigenvalues is equal to the dimension of the matrix. However, not all matrices have distinct eigenvalues and eigenvectors.

5. How are eigenvalues and eigenvectors used in data analysis?

Eigenvalues and eigenvectors are commonly used in data analysis to reduce the dimensionality of a dataset and identify the most important features or patterns. They are also used in machine learning algorithms, such as principal component analysis, to extract meaningful information from high-dimensional data.

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