Diagonally forced systems and the spectral signature of matrix cycles

TL;DR

This paper analytically determines the minimal scaling factor for a matrix with negative diagonal elements to have a zero eigenvalue, linking spectral properties to feedback cycles and system stability.

Contribution

It introduces an analytical relation for finding the critical scaling factor and connects the roots of the characteristic polynomial to feedback cycles in the matrix.

Findings

Derived a relation for minimal sigma ensuring zero eigenvalue.

Linked eigenvalues to feedback cycles within the matrix.

Identified a spectral signature based on matrix element signs and sizes.

Abstract

Given any square matrix, $\mathbf{M}$, whose diagonal elements are negative, and which is multiplied by a variable, $\sigma$, we wish to find the minimal $\sigma$ such that the eigenvalue of $\mathbf{M}_{\sigma}$ is exactly zero. By Gershgorin, we know that $\mathbf{M}_{\sigma}$ can be made stable by making $\sigma$ large enough. We prove a relation which analytically determines when and how we are able to find the value of $\sigma$ such that the maximal eigenvalue is exactly zero. In so doing, we prove the equivalence of the roots of the characteristic polynomial of $\mathbf{M}_{\sigma}$ and the eigenvalues that arise from a scaling operation on $\mathbf{M}$. Further, through the characteristic polynomial, we are able to isolate the dominant feedback cycles comprising the elements of the matrix which, under the action of $\sigma$, ensures the stability of the system. We then explore, using the stabilising and destabilising cycles within the coefficients of the characteristic polynomial, an intrinsic spectral signature associated with any matrix based on the size and sign (or zero) of its respective elements.