Eigenvalue - geometric multiplicity proof

In summary, to show that there is an eigenvalue of matrix A whose geometric multiplicity is n-1, we can use the general eigenvalue/vector equations and start by finding the characteristic polynomial. This can be done by considering "1 by 1", "2 by 2", and "3 by 3" matrices and finding a pattern. Additionally, we can find the corresponding eigenvectors to fully solve the problem.
  • #1
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Homework Statement



Given matrix A:

a 1 1 ... 1
1 a 1 ... 1
1 1 a ... 1
.. . .. ... 1
1 1 1 ... a

Show there is an eigenvalue of A whose geometric multiplicity is n-1. Express its value in terms of a.

Homework Equations



general eigenvalue/vector equations

The Attempt at a Solution



My problem is I'm not sure how to start it off.
I can state A is square, symmetric and hermitian so I know it has to do with one or more of those. I tried going through using a determinant but it didn't seem to work nicely, I have a feeling that it might have to do with a property of symmetric matrices but am not sure how to go about the proof (or which property)
 
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  • #2
Well, first, what is the eigenvalue in question? To do that, of course, you will need to find the characteristic polynomial. I recommend starting with "1 by 1", "2 by 2", and "3 by 3" matrices to see if you can find a pattern. Do the same thing to find the eigenvectors corresponding to that eigenvalue.
 

1. What is an eigenvalue?

An eigenvalue is a scalar value that represents how a given linear transformation affects a vector. It is often denoted by the Greek letter lambda (λ).

2. What is the geometric multiplicity of an eigenvalue?

The geometric multiplicity of an eigenvalue is the number of linearly independent eigenvectors associated with that eigenvalue. It represents how many different directions a vector can be transformed into by the linear transformation.

3. How is the geometric multiplicity of an eigenvalue calculated?

The geometric multiplicity of an eigenvalue can be calculated by finding the nullity (dimension of the nullspace) of the matrix A-λI, where A is the original matrix and λ is the eigenvalue in question.

4. What is the significance of the geometric multiplicity in eigenvalue proofs?

The geometric multiplicity is important in eigenvalue proofs because it helps determine the dimension of the eigenspace associated with a particular eigenvalue. It also plays a role in determining the diagonalizability of a matrix.

5. Can a matrix have more than one eigenvalue with the same geometric multiplicity?

Yes, a matrix can have multiple eigenvalues with the same geometric multiplicity. This means that there are multiple linearly independent eigenvectors associated with each of those eigenvalues.

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