Solving Eigenvalue Problems: Choosing Eigenvectors

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In summary, there may be an ambiguity in choosing the components of an eigenvector corresponding to a given eigenvalue, as any scalar multiple of an eigenvector is also an eigenvector with the same eigenvalue. However, this ambiguity does not affect the overall solution as all eigenvectors will have the same direction and only differ in magnitude.
  • #1
WannabeNewton
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I have a rather basic question about solving eigenvalue problems. Once you actually find all the eigenvalues for a given operator in some basis and you go about finding the respective eigenvectors through the components and run into a situation like this:
[tex]\mid \omega = 1 > [/tex] [tex]\Rightarrow \begin{bmatrix}
1 & 0 & 1\\
0 & 1 & 0 \\
1 & 0 & 1
\end{bmatrix} \begin{bmatrix}
v_{1}\\
v_{2}\\
v_{3}
\end{bmatrix} = 0 [/tex]

and you put it into the form
[tex]v_{1} + v_{3} = 0 [/tex]
[tex]v_{2} = 0[/tex]
[tex]v_{1} + v_{3} = 0[/tex]

isn't there an ambiguity as to whether you choose to set [tex]v_{1} = -v_{3}[/tex] as opposed to [tex]v_{3} = -v_{1}[/tex] for the eigenvector(normalized or not) corresponding to the eigenvalue? Does it not make a difference regarding sign for the respective components of the eigenvector? Sorry if this is a petty question I just wasn't sure.
 
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  • #2
If there is an eigenvector then any scalar multiple of it is also an eigenvector with the same eigenvalue. In your example, one eigenvector can be obtained from another by multiplication by -1.

If you imagine that you have the matrix in its diagonal form (or "almost diagonal" form), then it becomes obvious that scalar multiplication doesn't matter.
 
  • #3
Oh of course. Thank you very much; at least now I know why I kept getting an answer -1 times what the key had. =D
 

Related to Solving Eigenvalue Problems: Choosing Eigenvectors

1. What is an eigenvalue and eigenvector?

An eigenvalue is a scalar that represents the magnitude of a linear transformation on a vector. An eigenvector is a vector that remains in the same span after being transformed by the linear transformation.

2. Why is it important to solve eigenvalue problems?

Solving eigenvalue problems allows us to understand important properties of linear transformations, such as stretching or rotating, and their effects on vectors. This can have practical applications in fields such as physics, engineering, and computer science.

3. How do you choose eigenvectors to solve an eigenvalue problem?

The eigenvectors chosen for an eigenvalue problem must be linearly independent and span the vector space. This can be achieved by using a variety of methods, such as the Gram-Schmidt process or the diagonalization method.

4. What is the relationship between eigenvalues and eigenvectors?

The eigenvalues and eigenvectors of a linear transformation are closely related. Each eigenvalue corresponds to a specific eigenvector, and the eigenvectors form a basis for the vector space. The eigenvalues determine the stretching or rotating factor of the linear transformation on the eigenvectors.

5. Are there any real-world applications of solving eigenvalue problems?

Yes, there are many real-world applications of solving eigenvalue problems. For example, in physics, eigenvalue problems can be used to analyze the behavior of quantum particles and determine their energy states. In computer science, they can be used in algorithms for data compression and signal processing. In engineering, they can be used to study the stability and vibrations of structures.

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