Matrix Ordering through Spectral and Nilpotent Structures in Totally Ordered Complex Number Fields

TL;DR

This paper introduces a new spectral and nilpotent ordering for matrices over complex fields, enabling comparison and analysis of non-Hermitian matrices beyond traditional real-valued eigenvalue orderings.

Contribution

It develops a total ordering for complex numbers, introduces the Spectral and Nilpotent Ordering (SNO) for matrices, and extends majorization and Schur--Ostrowski criteria to complex matrices, advancing matrix analysis.

Findings

Defined a total ordering for complex eigenvalues.

Established the Spectral and Nilpotent Ordering (SNO) for matrices.

Extended majorization and Schur--Ostrowski criteria to complex matrices.

Abstract

Matrix inequalities play a pivotal role in mathematics, generalizing scalar inequalities and providing insights into linear operator structures. However, the widely used L\"owner ordering, which relies on real-valued eigenvalues, is limited to Hermitian matrices, restricting its applicability to non-Hermitian systems increasingly relevant in fields like non-Hermitian physics. To overcome this, we develop a total ordering relation for complex numbers, enabling comparisons of the spectral components of general matrices with complex eigenvalues. Building on this, we introduce the Spectral and Nilpotent Ordering (SNO), a partial order for arbitrary matrices of the same dimensions. We further establish a theoretical framework for majorization ordering with complex-valued functions, which aids in refining SNO and analyzing spectral components. An additional result is the extension of the Schur--Ostrowski criterion to the complex domain. Moreover, we characterize Jordan blocks of matrix functions using a generalized dominance order for nilpotent components, facilitating systematic analysis of non-diagonalizable matrices. Finally, we derive monotonicity and convexity conditions for functions under the SNO framework, laying a new mathematical foundation for advancing matrix analysis.