Q1: Does A and its transpose have the same eigenspace?

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SUMMARY

The discussion confirms that a matrix \( A \) and its transpose \( A^T \) do not generally share the same eigenspaces, despite having identical eigenvalues due to their characteristic polynomials being the same. The distinction between right eigenvectors, which satisfy \( A\mathbf{x} = \lambda \mathbf{x} \), and left eigenvectors, which satisfy \( \mathbf{x}A = \lambda \mathbf{x} \), is emphasized. It is established that left and right eigenvectors coincide only when \( A \) is symmetric, i.e., \( A = A^T \). Furthermore, the conjecture regarding the relationship between left and right eigenvectors is clarified as being valid only under specific conditions.

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  • Understanding of eigenvalues and eigenvectors
  • Familiarity with matrix transposition
  • Knowledge of characteristic polynomials
  • Concept of symmetric matrices
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  • Study the properties of symmetric matrices and their eigenvectors
  • Learn about the relationship between left and right eigenvectors in linear algebra
  • Explore canonical forms of matrices and their implications on eigenvalues
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Mathematicians, linear algebra students, and anyone studying matrix theory or eigenvalue problems will benefit from this discussion.

Howers
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So I've shown that A and A^T have the same char. polynomials => same eigenvalues, using the fact that detA = detA^T. I still can't see any way I could possibly show or disprove that the eigenspace is the same.
 
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How about
<br /> \left(\begin{array}{ll}<br /> 1 &amp; 1\\<br /> 0 &amp; 0<br /> \end{array}\right)\ \ <br /> \left(\begin{array}{ll}<br /> 1 &amp; 0\\<br /> 1 &amp; 0<br /> \end{array}\right)<br />
Do they have the same eigenspaces?
 
A and A^T will not have the same eigenspaces, i.e. eigenvectors, in general.

Remember that there are in fact two "eigenvectors" for every eigenvalue \lambda. The right eigenvector satisfying A\mathbf{x} = \lambda \mathbf{x} and a left eigenvector (eigenrow?) satisfying \mathbf{x}A = \lambda \mathbf{x}. In general these are not equal.

Also, I believe that the set of left eigenvectors is the inverse matrix of the set of right eigenvectors, but I am not about sure of this. If this is indeed the case then the set of left eigenvectors will "coincide" with the set of right eigenvectors only when the set of right eigenvectors is orthonormal, i.e. when A is symmetric A=A^T.

EDIT: In fact, the conjecture above is not true unless you select specially scaled sets of eigenvectors and eigenrows. Is there a way of selecting eigenvectors of canonical lengths?
 
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