Eigenvalues and vectors - finding original matrix

In summary, the conversation discusses determining the original matrix that yields specific eigenvalues and eigenvectors. The suggested method is to represent the matrix as A=PDP^{-1}, where D is a diagonal matrix with the eigenvalues and P is the corresponding eigenvectors. The conversation also mentions finding the inverse of P to fully determine the original matrix.
  • #1
Caeder
13
0
How do I determine what the original matrix was that yielded these two eigenvalues with the corresponding eigenvectors:

[tex]\lambda_1 = -3[/tex] Eigenvector: [0,1]

[tex]\lambda_2 = 2[/tex] Eigenvector: [1,0]

I've played around with det(A-lambda I) but can't find the matrix! I even just did some trial and error matrices in Maple trying to figure it out. If anyone can find me the matrix I'd be very impressed.
 
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  • #2
Well if you have the eigenvectors of a matrix A then A can be represented as:
A=PDP[itex]^{-1}[/itex]
where D is a diagonal matrix with the diagonal elements as [itex]\lambda_1 \ and \ \lambda_2[/itex]

and P is the eigenvectors of the eigenvalues...

so from your problem:

D would be the matrix:
[-3 0]
[0 2]

and the first column for P would be the eigenvector for -3 and the 2nd column would be the eigenvector for 2...so you now have P..find P[itex]^{-1}[/itex] and multiply out
 
  • #3
What does the matrix
a11 a12
a21 a22

map (1,0) and (0,1) to?
 

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe the behavior of a matrix when it is applied to a vector. Eigenvalues are scalar values that represent the scaling factor of the eigenvector, while eigenvectors are the vectors that remain in the same direction after being multiplied by the matrix.

2. How do I find the eigenvalues and eigenvectors of a matrix?

To find the eigenvalues and eigenvectors of a matrix, you first need to find the characteristic polynomial of the matrix. This is done by subtracting the variable lambda from the main diagonal elements of the matrix and then finding the determinant of the resulting matrix. The eigenvalues are the solutions to this polynomial. Once you have the eigenvalues, you can find the corresponding eigenvectors by solving the equation (A - λI)x = 0, where A is the original matrix and λ is the eigenvalue.

3. Why is finding eigenvalues and eigenvectors important?

Finding eigenvalues and eigenvectors can be used to solve a variety of problems in mathematics and science. For example, they can be used to find the principal components of a dataset, which can help reduce the dimensionality of the data and make it easier to analyze. They are also used in quantum mechanics to describe the behavior of particles, and in engineering to study the stability of systems.

4. Can all matrices have eigenvalues and eigenvectors?

No, not all matrices have eigenvalues and eigenvectors. Only square matrices have eigenvalues and eigenvectors. Additionally, the matrix must be diagonalizable, meaning it can be transformed into a diagonal matrix by similarity transformation. If a matrix is not diagonalizable, it does not have eigenvalues and eigenvectors.

5. Are eigenvalues and eigenvectors unique?

Yes, eigenvalues and eigenvectors are unique for a given matrix. However, different matrices can have the same eigenvalues and eigenvectors, as long as they are similar matrices. Similar matrices have the same eigenvalues and eigenvectors, but they may have different eigendecompositions.

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