Eigenvalues and Eigenvectors: Finding the Roots of a Matrix

In summary, the conversation discusses finding the eigenvalues and eigenvectors of a given matrix. The method of using the characteristic equation is suggested, but the resulting equation does not match the expected one. The person seeking help is unsure if they are expanding the determinant correctly and asks for clarification on whether to apply cofactors to every row or just the coefficients of the 2x2 matrix determinants. The respondent advises calculating the determinant as the sum of six products and reminds the person to add "-λ" to the main diagonal elements. A link to a resource on determinants is also provided.
  • #1
kev.thomson96
13
0

Homework Statement


we have this matrix
6 - 1 0
-1 -1 -1
0 -1 1
We need to find it's eigenvalues and eigenvectors

Homework Equations

The Attempt at a Solution

[/B]

I wrote the characteristic equation - det(A- λxunit matrix) to find the roots and got (-λ^3)+8(λ^2)+λ-6 instead of -λ(^3)+6(λ^2)+3λ-13, which restricts me from getting the eigenvalues and vectors in the end. I don't think I'm expanding the determinant correctly, even though I know the -1 on r1, c2 turns into a +.
Do I have to apply cofactors to every row, or just to the coefficients of the 2x2 matrix determinants (6 -(-1) and 0)

These are the supposed answers
 
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  • #2
Hi kev:

You need to calculate the determinant as the sum of six products, each with an appropriate +/- sign. Each product includes one element from each row and each column.

See https://en.wikipedia.org/wiki/Determinant .

Also, you may have forgotten that the cells along the main diagonal all have a "-λ" added to the numerical value in the cell.

Hope this helps.

Regards,
Buzz
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to describe the behavior of linear transformations. They are used to understand how a transformation affects a vector in a particular direction and how the vector is scaled by the transformation.

2. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they allow us to simplify complex linear transformations and understand their behavior. They are also used in various applications such as image processing, data compression, and machine learning.

3. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated using various methods such as the characteristic polynomial, power iteration method, or the QR algorithm. The method used depends on the size and type of the matrix.

4. Can a matrix have more than one eigenvalue and eigenvector?

Yes, a matrix can have multiple eigenvalues and eigenvectors. The number of eigenvalues and eigenvectors depends on the size and type of the matrix. For example, a 2x2 matrix can have two eigenvalues and two eigenvectors, while a 3x3 matrix can have three eigenvalues and three eigenvectors.

5. What is the relationship between eigenvalues and eigenvectors?

The eigenvalues and eigenvectors of a matrix are related in that the eigenvalues determine the direction and scale of the eigenvectors. Eigenvalues can also be used to find the eigenvectors associated with them. Additionally, eigenvectors with different eigenvalues are linearly independent.

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