Proving a property of the dimension of eigenspaces in a finite dimensional space

In summary, if A is a linear map on a vector space V with dimension n, and h1,...,hk are pairwise different eigenvalues of A whose geometric multiplicities sum to n, then A does not have any other eigenvalues. This can be proven by contradiction, as the existence of another eigenvalue would result in more than n independent eigenvectors, which is not possible.
  • #1
cloverforce
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Homework Statement



Prove that if A: V - >V is a linear map, dim V = n, and h1,...,hk (where 1,...,k are subscripts) are pairwise different eigenvalues of A such that their geometric multiplicities sum to n, then A does not have any other eigenvalues.

Homework Equations


Note sure if this is relevant or not, but if hj and hk are distinct eigenvalues, then the intersection of V(hj) and V(hk) is {0}.

The Attempt at a Solution


My attempt has been by contradiction. Suppose that there exists an eigenvalue h distinct from the k terms already given. In that case, I want to show that dim(V(h)) = 0, which would mean that its basis is the empty set and thus no such eigenvalue can exist. I'm not sure why this would have to be the case, though.
 
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  • #2
One important property you will need is that eigenvectors corresponding to distinct eigenvalues are independent.

If "their geometric multiplicities sum to n" then, by definition of "geometric multiplicity" you already have n independent vectors. If there existed another eigenvalue, distinct from any of the previous ones, then an eigenvector corresponding to it would be independent of the first n, giving you n+1 independent eigenvectors.
 

Related to Proving a property of the dimension of eigenspaces in a finite dimensional space

1. What is the definition of eigenspace?

An eigenspace is a subspace of a vector space that consists of all the eigenvectors corresponding to a specific eigenvalue.

2. How do you find the dimension of an eigenspace?

The dimension of an eigenspace can be found by counting the number of linearly independent eigenvectors corresponding to a specific eigenvalue. This can be done by solving the characteristic equation and finding the nullity of the corresponding matrix.

3. Can the dimension of an eigenspace be greater than the dimension of the vector space?

No, the dimension of an eigenspace can never be greater than the dimension of the vector space. It can only be equal to or less than the dimension of the vector space.

4. How does the dimension of an eigenspace relate to the multiplicity of its corresponding eigenvalue?

The dimension of an eigenspace is equal to the multiplicity of its corresponding eigenvalue. This means that the number of linearly independent eigenvectors corresponding to a specific eigenvalue is equal to the number of times that eigenvalue appears in the characteristic equation.

5. Can the dimension of an eigenspace change?

Yes, the dimension of an eigenspace can change if the eigenvalue or the vector space changes. However, the dimension will always be equal to the multiplicity of the eigenvalue and can never exceed the dimension of the vector space.

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