How do eigenvalues and eigenvectors relate to matrices?

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Homework Help Overview

The discussion centers around understanding eigenvalues and eigenvectors in the context of 2x2 and 3x3 matrices, specifically for a physics course. The original poster expresses a lack of foundational knowledge in linear algebra and seeks guidance on deriving eigenvalues and eigenvectors for given matrices.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts to find eigenvalues and eigenvectors for specific matrices but struggles with the underlying concepts. Some participants provide definitions and outline the process for finding eigenvalues and eigenvectors, while others clarify the steps involved in deriving eigenvectors from the eigenvalues.

Discussion Status

Participants are actively discussing the methods to find eigenvalues and eigenvectors, with some providing detailed steps and clarifications. There is a recognition of the original poster's confusion regarding the eigenvector derivation process, indicating a productive exchange of ideas and attempts to clarify concepts.

Contextual Notes

The original poster has indicated a lack of prior knowledge in linear algebra and is seeking detailed explanations, which may influence the depth of the discussion. There is also a mention of specific conditions for the matrices being discussed, such as non-equality of certain variables.

S_Flaherty
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I have to be able to figure out eigenvalues and eigenvectors for 2x2 and 3x3 matrices for my physics course, but I have never taken linear algebra so I obviously have no idea what they even are. I need someone to basically teach me how to solve these problems because I have no knowledge of this material and cannot find any useful source to help me.

First I have to find the eigenvalues and vectors for

[a 0 1]
[0 b 0]
[0 0 c] where a =/= b =/= c.

I looked around online and could only find that the answers for the eigenvalues are a, b, and c but I have no idea how that answer is derived. And I have no idea how to solve for eigenvectors except so any help would be great.

Next I have to do the same thing for

[a a 0]
[2a 0 0]
[0 a a] where a =/= 0.

Again I know that the solution for the values is -a, a, and 2a but no clue on how to actually do the problem.

I know nothing of this material except how to figure out the determinant of a matrix. So detailed steps on how to solve for eigenvalues and eigenvectors would be greatly appreciated.
 
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An eigenvalue λ and its eigenvector v are defined by:

Av = λv

That is, you try to find a vector that if you multiply A with it you find the same vector, except for length.

To solve this, you do:
Av - λv = 0
(A - λI)v = 0

This means (A - λI) must be singular, so its determinant must be zero.
det(A - λI)=0 yields a polynomial in λ, that is called the "characteristic polynomial".
Its roots are the eigenvalues of A.

Once you have an eigenvalue, you solve:
(A - λI)v = 0
to find a vector v (or its multiple) that satisfies the equation.
 
Applied to your first matrix you have:
det(A - λI)=0
(a-λ)(b-λ)(c-λ)=0
Roots are a, b, and c.

For the first eigenvalue "a", you get:
A - aI =
[0 0 1]
[0 b 0]
[0 0 c]

The vector v=(1,0,0) satisfies (A - aI)v = 0, meaning that is the eigenvector.
 
I like Serena said:
Applied to your first matrix you have:
det(A - λI)=0
(a-λ)(b-λ)(c-λ)=0
Roots are a, b, and c.

For the first eigenvalue "a", you get:
A - aI =
[0 0 1]
[0 b 0]
[0 0 c]

The vector v=(1,0,0) satisfies (A - aI)v = 0, meaning that is the eigenvector.

So i understand how you get the eigenvalues but I'm still confused with how you are arriving at the answer for the eigenvector.
 
The matrix A - aI can be row reduced to
[tex]\begin{bmatrix}0&0&1\\0&1&0\\0&0&0 \end{bmatrix}[/tex]

This represents the equation (A - aI)x = 0, where x is a column vector with coordinates x1, x2 and x3.

The reduced matrix represents the system
x3 = 0
x2 = 0
with x1 being arbitrary. Since it's arbitrary, it's reasonable to set it to 1, giving the vector <1, 0, 0> as an eigenvector for the eigenvalue a.
 

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