# Recursive eigen extrusion: Expanding eigenbasis conjecture

## TL;DR

This paper investigates a recursive eigenvector normalization process, conjecturing that for matrices of size up to 7, the process converges to unitary matrices, with implications for eigenbasis expansion and point distribution on spheres.

## Contribution

It formally states and numerically explores a conjecture about the convergence of a recursive eigenvector normalization process to unitary matrices, providing proofs for special cases.

## Key findings

- Numerical results support the conjecture for matrices up to size 7.
- The process converges to unitary matrices with orthonormal eigenvectors.
- The problem relates to maximizing average distances among points on a sphere.

## Abstract

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.