Finding the eigenvectors (and behavior of solution) around the

In summary, the conversation discusses finding the eigenvectors and behavior of solutions around critical points in the given matrix. The determinant of the matrix is calculated and the eigenvalues are found to be +20 and -20. The process for finding the corresponding eigenvectors is explained and a solution is found. The conversation ends with the individual successfully solving the problem.
  • #1
Somefantastik
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0
finding the eigenvectors (and behavior of solution) around the critical points found in this thread: https://www.physicsforums.com/showthread.php?t=258349&referrerid=110346

[tex]D_{f} = \[\begin{pmatrix}32x & 18y \\ 32x & -32y\end{pmatrix}\][/tex]

[tex]D_{f}(1,1) = \[\begin{pmatrix}32 & 18 \\ 32 & -32\end{pmatrix}\] [/tex]

[tex]= \[\begin{pmatrix}16 & 9 \\ 16 & -16 \end{pmatrix}\][/tex]

[tex]det(A-\lambda I) =\[\begin{pmatrix} 16-\lambda & 9 \\ 16 & -16- \lambda \end{pmatrix}\] [/tex]

[tex] = -256 + \lambda^{2} - 146 \ => \ \lambda = ^{+}_{-}20 [/tex]

[tex] \lambda_{1} = 20: [/tex]

[tex] (A-\lambda_{1} I)\xi^{(1)} = 0 \ => \ \[\begin{pmatrix} -4 & 9 \\ 16 & -36 \end{pmatrix}\]\xi^{(1)} = 0 [/tex]

I can't get LaTeX to cooperate with me, that's supposed to say [-4 9; 16 -36]ξ(1) = 0

[tex] => \ \xi^{(1)} = \left[^{9}_{4} \right] [/tex]

Having trouble finding [tex] \xi^{(2)}[/tex] when [tex]\lambda_{2} = -20 [/tex].

Keeps coming out to be [0 0]T.

Any suggestions?
 
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  • #2


Looks like you can't reduce the matrix before you do det(A - tI). Figured it out; thanks for looking.
 

FAQ: Finding the eigenvectors (and behavior of solution) around the

What are eigenvectors and why are they important in finding solutions?

Eigenvectors are special vectors that do not change direction when multiplied by a specific matrix. They are important in finding solutions because they provide insight into the behavior of a system or process, allowing us to understand how it will evolve over time.

How do you find eigenvectors?

To find eigenvectors, we must first determine the eigenvalues of the given matrix. Then, we can use the eigenvalues to solve for the corresponding eigenvectors by setting up and solving a system of equations.

What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related, as the eigenvalues determine the scaling factor for the corresponding eigenvector. In other words, the eigenvalues tell us how much the eigenvectors are stretched or compressed when multiplied by the matrix.

How can eigenvectors be used to determine the behavior of a solution?

Eigenvectors can be used to determine the behavior of a solution by providing information about the direction and magnitude of the solution's movement. This can help us understand how the solution will change over time and make predictions about its behavior.

Can eigenvectors be complex numbers?

Yes, eigenvectors can be complex numbers. In fact, complex eigenvectors are often used in systems with oscillatory behavior, such as in quantum mechanics or circuit analysis. However, real eigenvectors are more common in simpler systems.

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