Geometric Approaches to Matrix Normalization and Graph Balancing

TL;DR

This paper introduces a non-normal energy functional on matrices and graphs, demonstrating its well-behaved gradient descent dynamics to find normal matrices and balanced graphs, with applications in topology and spectral preservation.

Contribution

It defines a new energy functional with proven convergence properties for normal matrices and graph balancing, linking geometric concepts with practical matrix and graph problems.

Findings

Gradient descent leads to normal matrices and balanced graphs.

The space of unit norm normal matrices is simply connected for all dimensions.

Gradient flow preserves spectra and realness of matrices and graph weights.

Abstract

Normal matrices, or matrices which commute with their adjoints, are of fundamental importance in pure and applied mathematics. In this paper, we study a natural functional on the space of square complex matrices whose global minimizers are normal matrices. We show that this functional, which we refer to as the non-normal energy, has incredibly well-behaved gradient descent dynamics: despite it being non-convex, we show that the only critical points of the non-normal energy are the normal matrices, and that its gradient descent trajectories fix matrix spectra and preserve the subset of real matrices. We also show that, even when restricted to the subset of unit Frobenius norm matrices, the gradient flow of the non-normal energy retains many of these useful properties. This is applied to prove that low-dimensional homotopy groups of spaces of unit norm normal matrices vanish; for example, we show that the space of $d \times d$ complex unit norm normal matrices is simply connected for all $d \geq 2$. Finally, we consider the related problem of balancing a weighted directed graph -- that is, readjusting its edge weights so that the weighted in-degree and out-degree is the same at each node. We adapt the non-normal energy to define another natural functional whose global minima are balanced graphs and show that gradient descent of this functional always converges to a balanced graph, while preserving graph spectra and realness of the weights. Our results were inspired by concepts from symplectic geometry and Geometric Invariant Theory, but we mostly avoid invoking this machinery and our proofs are generally self-contained.