Eigenvalues & Eigenvectors of A & A+rI

In summary: And what can you say about the eigenvalues of A and A+rI?The eigenvalues of A+rI will be equal to the eigenvalues of A plus r. So if the eigenvalues of A are \lambda_1, \lambda_2, ..., \lambda_n, then the eigenvalues of A+rI will be \lambda_1+r, \lambda_2+r, ..., \lambda_n+r.In summary, the eigenvectors of A and A+rI are the same, and the eigenvalues of A+rI are equal to the eigenvalues of A plus r.
  • #1
mlarson9000
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0

Homework Statement


Let A be an nxn matrix and let I be the nxn identity matrix. Compare the eigenvectors and eigenvalues of A with those of A+rI for a scalar r.


Homework Equations





The Attempt at a Solution


I think I should be doing something like this:
det(A-[tex]\lambda[/tex]I), and

det((A+rI)-[tex]\lambda[/tex]I)=det(A-([tex]\lambda[/tex]-r)I).

The eigenvalues would be [tex]\lambda[/tex] where the det(A-[tex]\lambda[/tex]I)=0
and det(A-([tex]\lambda[/tex]-r)I)=0.

So does that mean the eigen values for the first matrix are [tex]\lambda[/tex] =n and the second will be n+r?
 
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  • #2
An eigenvector of A is a nonzero vector x such that (A - [itex]\lambda[/itex])x = 0, and [itex]\lambda[/itex] is the eigenvalue for that eigenvector.

Can you say something in this vein for the eigenvectors of A + rI?
 
  • #3
[/B][/B]
Mark44 said:
An eigenvector of A is a nonzero vector x such that (A - [itex]\lambda[/itex])x = 0, and [itex]\lambda[/itex] is the eigenvalue for that eigenvector.

Can you say something in this vein for the eigenvectors of A + rI?

The eigenvectors are the solution to:
(A-([tex]\lambda[/tex]-r)I)x=0
What do you suppose is meant by "compare the eigenvectors and eigenvalues?"
 
  • #4
So now put together what I wrote and what you wrote.
An eigenvalue of A is a number [itex]\lambda[/itex] such that (A - [itex]\lambda[/itex]I)x = 0.
An eigenvalue of A + rI is a number ? such that (A - ([itex]\lambda[/itex] - r)I)x = 0.

(Fill in the question mark.)
What can you say about the values of x in either case?
 
  • #5
I don't see why I should be assuming that [tex]\lambda[/tex] in the first equation should be equal to [tex]\lambda[/tex] in the second equation. The same goes for x. It's really giving me trouble as I work with this.
 
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  • #6
lambda is an eigenvalue corresponding to eigenvector x if Ax=lambda*x, right? Then (A+rI)x=Ax+rx=(lambda+r)x, also right? I haven't changed any lambda's or x's. What does (A+rI)x=(lambda+r)x tell you about eigenstuff of A+rI?
 
  • #7
(A+rI)x=([tex]\lambda[/tex]+r)x
(A+rI)x-([tex]\lambda[/tex]+r)x=0
(A+rI-[tex]\lambda[/tex]I-rI)x=0
(A-[tex]\lambda[/tex]I)x=0

So the eigenvectors of A and A+rI are the same, right?
 
  • #8
mlarson9000 said:
(A+rI)x=([tex]\lambda[/tex]+r)x
(A+rI)x-([tex]\lambda[/tex]+r)x=0
(A+rI-[tex]\lambda[/tex]I-rI)x=0
(A-[tex]\lambda[/tex]I)x=0

So the eigenvectors of A and A+rI are the same, right?

Right.
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra. Eigenvalues are the values that, when multiplied by a given square matrix, result in the same vector as the original vector. Eigenvectors are the corresponding vectors that only change in scale when multiplied by the matrix.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they allow us to understand the behavior of a linear transformation or matrix. They can also be used to simplify complex calculations and find patterns in data.

3. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, you must first set up and solve an equation known as the characteristic equation. This equation involves finding the determinant of the given matrix minus a scalar value, known as lambda. The resulting values of lambda are the eigenvalues. To find the corresponding eigenvectors, you must plug the eigenvalues back into the original equation and solve for the eigenvectors.

4. What is the relationship between eigenvalues and eigenvectors?

The relationship between eigenvalues and eigenvectors is that each eigenvalue corresponds to a specific eigenvector. When a matrix is multiplied by its corresponding eigenvector, the resulting vector is simply a scaled version of the original eigenvector.

5. How are eigenvalues and eigenvectors used in real-world applications?

Eigenvalues and eigenvectors are used in many areas of science and engineering, including signal processing, image compression, and quantum mechanics. They are also used in data analysis and machine learning to identify patterns and reduce the dimensionality of data.

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