Finding eigenbasis and diagonalization

In summary, the problem is to find the eigenbasis and diagonalize a given matrix. The first step is to find the characteristic equation by finding the determinant of the matrix minus the identity matrix multiplied by the variable lambda. This results in a cubic equation, which can be factored to find the roots. Although there is no easier way to find the characteristic polynomial or cubic roots, it is important to carefully check for any possible factorization before proceeding with manual calculations.
  • #1
TheFerruccio
220
0

Homework Statement



Find the eigenbasis and diagonalize.

Homework Equations



[tex]\mathbf{A} = \left[ {\begin{array}{ccc}
5& \frac{8}{3} & \frac{-2}{3} \\
2 & \frac{2}{3}& \frac{4}{3} \\
-4 & \frac{-4}{3} & \frac{-8}{3}\\
\end{array} } \right][/tex]

The Attempt at a Solution



I find the characteristic equation by finding the determinant of [tex]\mathbf{A} - \lambda \mathbf{I}[/tex][tex]\left|\mathbf{A} - \lambda \mathbf{I}\right| = \left| {\begin{array}{ccc}
5 - \lambda & \frac{8}{3} & \frac{-2}{3} \\
2 & \frac{2}{3} - \lambda & \frac{4}{3} \\
-4 & \frac{-4}{3} & \frac{-8}{3} - \lambda\\
\end{array} } \right|[/tex] = 0

This gets me the cubic equation:
[tex]-\lambda^3 + 3\lambda^2 + 18\lambda = 0[/tex]

So, here's the question: Are there any nice, fast ways to get the roots of the cubic equation? Furthermore, is there any faster way to find the characteristic polynomial that doesn't include such a high probability of arithmetic error when doing it by hand?

All of this is done by hand, no calculator, pencil and paper.
 
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  • #2


You are doing ok so far. You've got the right characteristic equation. Now just factor it. It's really not so bad for a cubic. You can factor out lambda right away and that leaves you with a quadratic. I don't think there is any easier way to find the characteristic polynomial in general than what you did. And there's no easier way to find cubic roots than to factor. If it doesn't factor, it's really hard.
 
  • #3


Dick said:
You are doing ok so far. You've got the right characteristic equation. Now just factor it. It's really not so bad for a cubic. You can factor out lambda right away and that leaves you with a quadratic. I don't think there is any easier way to find the characteristic polynomial in general than what you did. And there's no easier way to find cubic roots than to factor. If it doesn't factor, it's really hard.
Oh, duh! I wasn't thinking. I was looking at the equation too fast and didn't see the obvious factorization right there.

Thanks for the help. I managed to figure this out after a few pages of number crunching.
 
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1. What is the purpose of finding eigenbasis and diagonalization?

The purpose of finding eigenbasis and diagonalization is to simplify and transform a matrix into a more useful and easily understandable form. By finding the eigenbasis, we can identify the eigenvectors and eigenvalues of a matrix, which can help us understand the behavior of the matrix better. Diagonalization, on the other hand, allows us to represent a matrix as a diagonal matrix, making it easier to perform calculations and solve problems involving the matrix.

2. How do you find the eigenbasis of a matrix?

To find the eigenbasis of a matrix, we first need to identify the eigenvalues of the matrix. This can be done by solving the characteristic equation det(A - λI) = 0, where A is the matrix and λ is the eigenvalue. Once we have the eigenvalues, we can find the corresponding eigenvectors by solving the equation (A - λI)x = 0. These eigenvectors form the basis of the eigenspace, which is the eigenbasis of the matrix.

3. What is the significance of diagonalization?

Diagonalization is significant because it allows us to simplify a matrix and make it easier to work with. By transforming a matrix into a diagonal matrix, we can easily perform operations such as multiplication and inversion. Diagonal matrices are also useful in solving systems of linear equations and finding the power of a matrix, which can be used in various applications such as in physics and engineering.

4. Can every matrix be diagonalized?

No, not every matrix can be diagonalized. A matrix can only be diagonalized if it has a complete set of linearly independent eigenvectors. If the matrix does not have enough eigenvectors to form a basis, it cannot be diagonalized. Additionally, some matrices may have repeated eigenvalues, which can also prevent diagonalization.

5. How is diagonalization related to eigenvalues and eigenvectors?

Diagonalization is closely related to eigenvalues and eigenvectors. The eigenvalues of a matrix are the values that remain unchanged when a matrix is multiplied by its corresponding eigenvectors. These eigenvectors then form the basis of the eigenspace, and by finding a complete set of eigenvectors, we can transform the matrix into a diagonal matrix. In other words, diagonalization is the process of finding the eigenbasis of a matrix.

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