I have a general question on how the left eigenvectors and right eigenvectors of a matrix are related to each other.

Background. It is easy to see that the characteristic polynomial of a $A$ and $A^\top$ are the same, hence the "left" and "right" eigenvalues of $A$ are the same. Is there any geometric reason on why this should happen? And moreover, why there should be any relations between the left and right eigenvectors corresponding to the same eigenvalue?

To be more clear, I can prove the following:

Observation. Let $A \in \mathbb{C}^{n\times n}$ have $n$ distinct eigenvalues. Then for an eigenvalue $\lambda$ and corresponding left eigenvector $u^\top$ and right eigenvector $v$, we have $u^\top v \neq 0$.

Proof. Let $J$ be the Jordan canonical form of $A$. Since all the eigenvalues of $A$ are simple, $J$ is diagonal. Let $A = SJS^{-1}$ for some invertible matrix $S$. Observe that for an eigenvalue $\lambda$ there is an $1 \leq i \leq n$ such that the $i$-th column of $S$, $s^i$, is a right eigenvector of $A$ for the eigenvalue $\lambda$, and the $i$-th row of $S^{-1}$, ${s_i}^\top$, is a left eigenvector of $A$ for the eigenvalue $\lambda$. Since $S^{-1} S = I$ we have ${s_i}^\top s^i = 1$. This implies $u^\top v \neq 0$.

Note that this need not be true in general. For example when there isn't a full set of eigenvectors, like in $$\begin{bmatrix} 0 & 1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0\end{bmatrix}.$$ So, I want to make a claim as following:

Claim. If $\lambda$ is an eigenvalue of $A$ where its geometric multiplicity is equal to its algebraic multiplicity, then there are left and right eigenvectors of $A$ corresponding to $\lambda$, respectively $u^\top$ and $v$, such that $u^\top v \neq 0$.

Note that the claim can't be true for all left and right eigenvectors, instead of there is. For example consider the identity matrix.,

So, my questions are:


  1. Is there any geometric reasons that eigenvalues of $A$ and $A^\top$ are equal?

  2. Is there any intuitive way to see why the observation above holds?

  3. Is the claim above true?


Owen Biesel in their comment to this question mentions that left eigenvectors are perpendicular to hyperplanes that are preserved under left multiplication. In that sense, that would mean $u^\top$ and $v$ are perpendicular if $v$ is in the hyperplane perpendicular to $u^\top$. But I can't quite make a connection to prove what I want.

  • 1
    $\begingroup$ Your nice proof of your observation also shows that non-corresponding left and right eigenvectors are orthogonal. So the left eigenvector indicates the hyperplane containing all the other right eigenvectors (by being that hyperplane's normal). $\endgroup$
    – Matt
    Commented Aug 13, 2020 at 19:40
  • $\begingroup$ One part of the answer to "I have a general question on how the left eigenvectors and right eigenvectors of a matrix are related to each other" is that the product of the matrix of right eigenvectors times the matrix of left eigenvectors (be sure to transpose one) is a diagonal matrix. See "Eigenvector" on Mathworld.Wolfram.com. My guess is that if the eigenvectors are orthogonalized first, using the command Orthogonalize in Mathematica, which also makes the vectors unit length, then the above product will be the identity matrix. If so, that would be neat. $\endgroup$ Commented Dec 6, 2021 at 15:05

3 Answers 3


Your claim is true. It suffices to modify the proof in your question slightly. If the algebraic and geometric multiplicities of some eigenvalue $\lambda$ coincide, then $A=P\pmatrix{\lambda I\\ &M}P^{-1}$ for some change-of-basis matrix $P$ and some submatrix $M$ that is the direct sum of the Jordan blocks for other eigenvalues. Therefore $u^T=e_1^TP^{-1}$ and $v=Pe_1$ are respectively a left eigenvector and a right eigenvector corresponding to $\lambda$, with $u^Tv=1\ne0$.

$A$ and $A^T$ have identical eigenvalues basically because a matrix $B$ (take $B=\lambda I-A$ in your case) is singular if and only if its $B^T$ is singular. And the geometric reason for the latter to hold is that every linear map on a finite-dimensional vector space can be decomposed into the product of a number of shears, transpositions and also a scaling function. That is, $B=E_1E_2\ldots E_kDF_\ell\ldots F_2F_1$ where each $E_i$ or $F_j$ is either a shear matrix (an elementary row/column operation for row/column addition) or a transposition matrix (an elementary operation for exchanging two rows/columns) and $D$ is a scaling (diagonal) matrix. As shear matrices are nonsingular, $B$ is singular iff $D$ is singular iff $B^T$ is singular.


I think that the best reason for the $A$ and $A^T$ having the same eigenvalues is the fact that they are similar matrices. This can be seen using the Jordan canonical forms, and the fact conjugating a matrix by an "anti-diagonal" corresponds to rotating the matrix by 180º. So, if $J_{\lambda}$ is a Jordan block then $Y J_{\lambda}^T Y$ is also a Jordan block, where $$ Y = \left[ \begin{array}{ccc} & &1\\ &\cdots & \\ 1 & & \end{array} \right] \ . $$


For geometric intuition, think of the following picture:

          .               /           hyperplane of all
         .               /           other eigenvectors
        .               /
       * . . . . . . . /

We can think of any point in terms of its projection onto the hyperplane (projected in the direction of the eigenvector), and its projection onto the eigenvector (projected parallel to the hyperplane). The dots in the figure indicate these projections for the point shown.

Thinking of the matrix M as a transformation, we know it will scale any point on the "eigenvector direction" line according to the eigenvalue. And in fact, we can see that any point, on that line or not, will have its distance from the hyperplane scaled by that eigenvalue. (Its projected position in the hyperplane will get transformed according to all the other eigenvectors and eigenvalues, but this will not affect its distance from the hyperplane — only this eigenvalue will do that.)

The right eigenvector corresponds to the line in this diagram.
The left eigenvector corresponds to the hyperplane in this diagram.

The eigenvalue tells us how the distance along the line, or the distance from the hyperplane, will be scaled.

(The vector corresponding to a hyperplane $H$ is its normal $\vec{v}$, appearing in the hyperplane's defining equation $H=\left\{\,\vec{x}\;\;\middle|\;\;\vec{v}\cdot\vec{x}=0\,\right\}$.)

From the point of view of this diagram, your observation can be rephrased as "The eigenvector direction line cannot lie in the hyperplane of all other eigenvectors," and it is clearly true.


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