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Is there a way to relate Eigenvalues to the column space and nullspace of a matrix?

I believe a matrices with different eigenvalues would have a different column spaces and/or nullspace. Is this correct?

I am wondering if you can prove that the Eigenvalues of $A$ and $A^T$ are equal using properties of column spaces and nullspaces.

My thinking is:
If you transform a matrix $A$ into $B$, if the row space of $B$ is orthogonal to the nullspace of $A$, and the column space of $B$ is orthogonal to the left nullspace of $A$, then matrices $A$ and $B$ have the same eigenvalues.

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    @DanRust in order to complete my understanding, "the dimension of the nullspace corresponds to the (*geometric) multiplicity ( **not the multiplicity** of the eigenvalue$0$but the multiplicity of the eigen-vectors that correrspond to zero eigen-values.)". The geometric multiplicity equals to $\mathbb{dim}\mathcal{N}(A)$. So the $\mathbb{dim}\mathcal{N}(A)$ does not offer enough information for the multiplicity of eigen-values. Am I right? You comments and discussion valuable in understanding.2016-09-25

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Matrices with different eigenvalues can have the same column space and nullspace. For a simple example, consider the real 2x2 identity matrix and a 2x2 diagonal matrix with diagonals 2,3. The identity has eigenvalue 1 and the other matrix has eigenvalues 2 and 3, but they both have rank 2 and nullity 0 so their column space is all of $\mathbb{R}^2$ and their nullspace is $\{0\}$.

This is also probably a negative answer to your question about the transpose - the column space and nullspace don't contain enough information about the eigenvalues.

On the other hand, eigenvalues are certainly related to the nullspace of $A-\lambda I$, where $\lambda$ is an eigenvalue of $A$. Namely, every eigenvector must lie in the nullspace of this matrix.

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    OK. Looks like I was overthinking things. Thanks!2012-12-18