Eigenvectors of Matrix: Solving Basics

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Homework Help Overview

The discussion revolves around finding the eigenvectors of a given 2x2 matrix. Participants are exploring the properties and definitions related to eigenvectors in the context of linear algebra.

Discussion Character

  • Exploratory, Conceptual clarification

Approaches and Questions Raised

  • The original poster attempts to confirm the validity of a potential eigenvector solution and questions whether switching the sign of an existing solution affects its status as an eigenvector.

Discussion Status

Participants have engaged in clarifying the nature of eigenvectors, noting that scalar multiples of eigenvectors are also valid solutions. There is a recognition of the properties of eigenvectors forming a subspace, which has been discussed without reaching a definitive conclusion on the original poster's query.

Contextual Notes

Participants are operating under the assumption that the eigenvalue problem is being solved correctly, and there is an acknowledgment of the properties of eigenvectors related to scalar multiplication and vector addition.

daveyman
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Homework Statement



Find the eigenvectors of the following matrix:

[tex] \[ \left( \begin{array}{ccc}<br /> 1 & 1 \\<br /> 4 & -2 \end{array} \right)\] [/tex]

Homework Equations



N/A

The Attempt at a Solution



I already know how to find the solutions. They are {1 1} and {-1 4}. My question is this: could a solution also be {1 -4} (switching the minus sign)? I don't think it is a solution but I don't see why not...

Any help would be great. Thanks!
 
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Have you tried [tex]\begin{pmatrix}1 \\ -4\end{pmatrix}[/tex] ? All it takes to be a solution is solving the eigenvalue problem for some eigenvalue [tex]\lambda[/tex].

Tom
 
your sol'n times any c in R is fine
 
davyjones said:
your sol'n times any c in R is fine

Oh I guess that's obvious huh. So if c=-1, I'm good.

Okay thanks!
 
Exactly.
 
One important thing you should have learned (or maybe this exercise is in preparation) the set of all eigenvectors (corresponding to a given eigenvalue) forms a subspace: it is closed under both vector addition and scalar multiplication.
 

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