Solve Repeated Eigenvalues: X' = [[9,4,0], [-6,-1,0], [6,4,3]] * X

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In summary, to solve X' = [ [9, 4, 0], [-6, -1, 0], [6, 4, 3]] * X using eigenvalues, you need to set up the characteristic equation to find the eigenvalues. Then, you can find two linearly independent eigenvectors by substituting different values for k3. This eliminates the need to use the Kteλt + Peλt form of the solution to find the second solution.
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XianForce
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Homework Statement


Solve X' = [ [9, 4, 0], [-6, -1, 0], [6, 4, 3]] * X using eigenvalues.


Homework Equations


(A - λI) * K = 0
X = eλt


The Attempt at a Solution


Set up the characteristic equation to find eigenvalues. I found a root of multiplicity 2 of λ=3 and another distinct root λ=5.

When setting up equations to solve for the eigenvectors (setting λ= 3) I found:

6k1 + 4k2 = 0
-6k1 -4k2 = 0
6k1 + 4k2 = 0

So there's only a dependency for k1 and k2. So can't I simply find two linearly independent eigenvectors by substituting different values for k3 such as [2, -3, 1] and [2, -3, 2] and use those as two independent solutions? Or does this mean I still have to walk through the steps of using the Kteλt + Peλt form of the solution to find the second solution for the λ=3 root?
 
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  • #2


Your first method is the right one.
 

What are repeated eigenvalues in a matrix?

Repeated eigenvalues in a matrix refer to the situation where a matrix has at least one eigenvalue that appears more than once. This means that there are multiple eigenvectors associated with that eigenvalue.

How do you solve for repeated eigenvalues in a matrix?

To solve for repeated eigenvalues in a matrix, you first need to find the eigenvalues using the characteristic polynomial. Then, for each repeated eigenvalue, you need to find the corresponding eigenvectors by solving the system of linear equations (A - λI)x = 0.

Why is it important to solve for repeated eigenvalues in a matrix?

Solving for repeated eigenvalues in a matrix is important because it helps us understand the behavior and properties of the matrix. It also allows us to find the corresponding eigenvectors, which are useful in applications such as diagonalization and solving differential equations.

What is the relationship between repeated eigenvalues and diagonalization?

Repeated eigenvalues in a matrix are closely related to diagonalization, as a matrix with repeated eigenvalues is not diagonalizable. This means that it cannot be transformed into a diagonal matrix using a similarity transformation.

Can a matrix have more than one repeated eigenvalue?

Yes, a matrix can have more than one repeated eigenvalue. This means that there are multiple eigenvectors associated with each repeated eigenvalue. In such cases, the matrix is not diagonalizable and has a Jordan canonical form instead of a diagonal form.

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