How to Prove the Second Part of a Multi-Part Question on Complex Eigenvalues?

In summary, the conversation discusses a second order differential equation with a given solution x and eigenvector \epsilon corresponding to eigenvalue r^{2} of matrix A. Part (b) involves showing that (A - r^{2}I)\epsilon = 0 using the substitution \epsilon = e^{-rt}x and substituting into the left side of the equation. The individual is seeking suggestions to move forward in the problem.
  • #1
rocketboy
243
1

Homework Statement



This is the second part of a multi-part question. Part (a) shows that:

x'' = Ax = [tex]\left(\stackrel{-2}{4/3}\stackrel{3/2}{-3}\right)[/tex]x

Part (b): Assume x = [tex]\epsilon[/tex]e[tex]^{rt}[/tex] and show that (A - r[tex]^{2}[/tex]I)[tex]\epsilon[/tex] = 0

x is the solution to the second order differential equation above, and [tex]\epsilon[/tex] is an eigenvector corresponding to the eigenvalue r[tex]^{2}[/tex] of A.

The Attempt at a Solution



Part (b): Alright, so given the above, I stated that [tex]\epsilon[/tex] = e[tex]^{-rt}[/tex]x

I then substituted everything into the left side of the equation I'm trying to prove to obtain:

(A - r[tex]^{2}[/tex]I)[tex]\epsilon[/tex] = [tex]\left(\stackrel{-2-r^{2}}{4/3}\stackrel{3/2}{-3-r^{2}}\right)[/tex][tex]\left(\stackrel{x_{1}}{x_{2}}\right)[/tex]e[tex]^{-rt}[/tex]

from here, I can see that I am not going in the right direction... any suggestions to get me moving along?

Thank-you!
-J

EDIT: for clarification, the above are 2x2 matrices... latex put the entries fairly close together. The matrix A has entries (-2 3/2) on the top and (4/3 -3) on the bottom.
 
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  • #2
What is [itex]\epsilon[/itex]? A vector? But since you also don't tell us what x is, what see no way to understand x= [itex]\epsilon e^{rt}[/itex].
 
  • #3
HallsofIvy said:
What is [itex]\epsilon[/itex]? A vector? But since you also don't tell us what x is, what see no way to understand x= [itex]\epsilon e^{rt}[/itex].

Sorry, thanks, I'll edit my above post to be more complete.

Note: for some reason my [tex]\epsilon[/tex] appear to be superscripts... nowhere should this be the case.
 
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What is the definition of complex eigenvalues?

Complex eigenvalues are a concept in linear algebra that refers to the solutions of the characteristic equation of a square matrix. They are complex numbers that represent the scaling factor of certain vectors when multiplied by the matrix.

What is the proof for the existence of complex eigenvalues?

The proof for the existence of complex eigenvalues is based on the fundamental theorem of algebra, which states that every polynomial equation of degree n has n complex roots, counting multiplicities. This theorem can be applied to the characteristic polynomial of a square matrix to show that it has complex solutions, which are the complex eigenvalues.

How are complex eigenvalues related to eigenvectors?

Complex eigenvalues and eigenvectors are closely related. Eigenvectors are the corresponding vectors to the complex eigenvalues, and they represent the direction of the transformation when multiplied by the matrix. The complex eigenvalues determine the magnitude of the scaling factor for each eigenvector.

Can a matrix have both real and complex eigenvalues?

Yes, a matrix can have both real and complex eigenvalues. The characteristic polynomial of a square matrix can have a mix of real and complex solutions, depending on the values of the matrix's entries. This means that the matrix can have both real and complex eigenvalues.

What is the significance of complex eigenvalues in real-world applications?

Complex eigenvalues have many applications in various fields, including physics, engineering, and computer science. They can be used to analyze and understand the behavior of systems, such as electrical circuits and mechanical structures. They are also essential in solving differential equations and in the study of quantum mechanics.

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