What Are the Mistakes in Multiplying Eigenvectors for Homogeneous Systems?

In summary: This way, when you multiply it by e^3t, you will get the correct result of e^3t * (A*sin 4t - B*cos 4t).In summary, when using the eigenvalue method for homogeneous systems, it is important to ensure that the signs are correct when multiplying the eigenvector by the cosine term, and to cancel out the imaginary terms when multiplying the eigenvector by the sine and cosine terms. This will result in the correct equations for x1(t) and x2(t).
  • #1
cue928
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In the context of using the eigenvalue method for homogeneous systems, my eigenvector is: V=[1 -i]^T and I have a lambda value of 3-4i. Here's my setup:
(e^3t)[1 -i](cos 4t - i sin 4t)
For the first equation (real):
ME: x1(t) = e^3t(A cos 4t - B sin 4t)
BOOK: x(t) = e^3t(A cos 4t + B sin 4t)

For the second equation (imaginary):
ME: x2(t) = e^3t[cos 4t - i sin 4t - i cos 4t + sin 4t]
BOOK: x2(t) = e^3t[A sin 4t - B cos 4t]

How am I getting the signs wrong on the first equation and what am I not cancelling on the second eq that I should be?
 
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  • #2
For the first equation, you are getting the signs wrong because you are using a complex eigenvector. The real part of the eigenvector is 1, so the sign should be positive when multiplying it by the cosine term.For the second equation, you are not cancelling out the imaginary terms in the eigenvector with the sin and cos terms. When multiplying an imaginary number times a complex number, you need to ensure that the imaginary terms cancel out. So, for example, multiplying -i * (cos 4t - i sin 4t) should result in i*cos 4t + sin 4t.
 

Related to What Are the Mistakes in Multiplying Eigenvectors for Homogeneous Systems?

What is the purpose of multiplying eigenvectors?

The purpose of multiplying eigenvectors is to find new eigenvectors that represent the same transformation, but with different scaling factors. This can be useful in applications such as data compression, image processing, and solving differential equations.

How do you multiply eigenvectors?

To multiply eigenvectors, you first need to find the eigenvalues of the matrix. Then, you can use the eigenvalues to solve for the new eigenvectors by multiplying them with the original eigenvectors. This can be done through matrix multiplication or by hand.

What happens when you multiply two eigenvectors?

When you multiply two eigenvectors, you are essentially transforming one eigenvector into another. The resulting eigenvector will have a different direction and magnitude, but it will still represent the same transformation as the original eigenvector.

Can you multiply more than two eigenvectors?

Yes, you can multiply more than two eigenvectors. In fact, multiplying multiple eigenvectors can result in a new set of eigenvectors that represent the same transformation, but with different scaling factors. This can be useful in various applications such as finding principal components in data analysis.

What are some real-world applications of multiplying eigenvectors?

Multiplying eigenvectors has many real-world applications, such as in data compression, image processing, principal component analysis, and solving differential equations. It can also be used in engineering, physics, and other fields to simplify complex calculations and understand the behavior of systems.

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