  Consider $n$ linearly independent vectors in $\mathbb{C}^n$ which form
columns of a matrix $A$. The recursive evaluation of eigen directions
(normalized eigenvectors) of $A$ is the solution of an eigenvalue problem of
the form $A_iX_i=X_i\Lambda_i$ with $i=0,1,2 \dots$; and here $\Lambda_i$ is
the diagonal matrix of eigenvalues and columns of $X_i$ are the eigenvectors.
Note that $A_{i+1}=\phi(X_i)$ where $\phi$ normalizes all eigenvectors to unit
$\mathcal{L}_2$ norm such that all diagonal elements
$[\phi(X)^\dagger\phi(X)]_{jj}=1$. It is to be proven that for any matrix $A_o$
and $n \leq 7$, the limiting set of matrices $A_i$ with $i \to \infty$ is the
set of unitary matrices $U(n)$ with $X_i^\dagger X_i \to I$. Interestingly,
this problem also represents a recursive map that maximizes some average
distance among a set of $n$ points on the unit $n$-sphere. We first formally
pose this conjecture, present extensive numerical results highlighting it, and
prove it for special cases.
