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

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SUMMARY

The discussion centers on proving that a linear map A: V -> V, where dim V = n and h1,...,hk are distinct eigenvalues with geometric multiplicities summing to n, cannot have additional eigenvalues. The proof employs contradiction, demonstrating that if an additional eigenvalue h exists, it would imply the existence of n+1 independent eigenvectors, which contradicts the established dimension of V. The key property utilized is that eigenvectors corresponding to distinct eigenvalues are linearly independent.

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  • Understanding of linear maps and vector spaces
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  • Familiarity with geometric multiplicity in linear algebra
  • Concept of linear independence in vector spaces
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  • Explore the concept of linear independence in greater detail
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Students of linear algebra, mathematicians focusing on eigenvalue problems, and educators teaching concepts of vector spaces and linear transformations.

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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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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.
 

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