Finding eigenvalues of a 3 x 3 matrix

In summary, the conversation is about finding the eigenvalues of a 3x3 matrix. The characteristic equation is taken and simplified, but the factoring becomes difficult. The final result can be checked using Wolfram|Alpha and can be factored into a cubic polynomial with roots 4, 1, and -4.
  • #1
dink87522
15
0
I have a 3 x 3 matrix

A =
(0 -1 -3)
(2 3 3)
(-2 1 1)

Let & represent lambda here.

I am trying to find the eigenvalues of A.

I start off by taking the characteristic equation of A and end up with -&[(&-3)(&-1) -3] + (2& - 8) - 3(-2& + 8)
yet can't then get that factored down. Am I missing something basic here?
 
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  • #2
[tex]
\left|\begin{array}{ccc}
-\lambda & -1 & -3 \\

2 & 3 - \lambda & 3 \\

-2 & 1 & 1 - \lambda
\end{array}\right| = 0
[/tex]

[tex]
-\lambda (3 - \lambda) (1 - \lambda) + 6 - 6 - 6 (3 - \lambda) + 3 \lambda + 2 (1 - \lambda) = 0
[/tex]

[tex]
-\lambda(3 - 4 \lambda + \lambda^{2}) - 18 + 6 \lambda + 3 \lambda + 2 - 2 \lambda = 0
[/tex]

[tex]
-\lambda^{3} + 4 \lambda^{2} - 3 \lambda + 7 \lambda - 16 = 0
[/tex]

[tex]
\lambda^{3} - 4 \lambda^{2} - 4 \lambda + 16 = 0
[/tex]

This can be factored.
 

Related to Finding eigenvalues of a 3 x 3 matrix

1. What are eigenvalues and why are they important in matrix analysis?

Eigenvalues are scalar values that represent the scaling factor of a vector when a linear transformation is applied. In matrix analysis, eigenvalues are important because they provide information about the behavior of a system and can be used to solve various mathematical problems.

2. How do you find the eigenvalues of a 3 x 3 matrix?

To find the eigenvalues of a 3 x 3 matrix, you can use the characteristic polynomial method or the diagonalization method. The characteristic polynomial method involves finding the roots of the characteristic polynomial, while the diagonalization method involves finding the diagonal matrix that has the same eigenvalues as the original matrix.

3. What is the significance of the determinant when finding eigenvalues?

The determinant of a matrix is the product of its eigenvalues. This means that the determinant can help determine the eigenvalues of a matrix and vice versa. Additionally, the determinant can also provide information about the stability and invertibility of a matrix.

4. Can a matrix have complex eigenvalues?

Yes, a matrix can have complex eigenvalues. This occurs when the matrix has complex entries or when the characteristic polynomial has complex roots. In such cases, the eigenvalues will be complex conjugate pairs.

5. How can eigenvalues be applied in real-world problems?

Eigenvalues have various applications in real-world problems, such as in physics, engineering, and economics. They can be used to solve differential equations, analyze dynamic systems, and find optimal solutions for certain problems. In data analysis, eigenvalues are used in principal component analysis to reduce the dimensionality of a dataset and identify important features.

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