NANAFeb 16, 2019

On Wielandt-Mirsky's conjecture for matrix polynomials

arXiv:1811.032271.23 citationsh-index: 5
Originality Synthesis-oriented
AI Analysis

Provides theoretical results for spectral variation of matrix polynomials, which is an incremental contribution to matrix analysis.

The paper studies extensions of the Hoffman-Wielandt inequality and weaker versions of the Wielandt-Mirsky conjecture from matrices to matrix polynomials, proving that the Frobenius distance between spectra is bounded by the Frobenius norm of the difference for certain classes of matrix polynomials.

In matrix analysis, the \textit{Wielandt-Mirsky conjecture} states that $$ dist(σ(A), σ(B)) \leq \|A-B\|, $$ for any normal matrices $ A, B \in \mathbb C^{n\times n}$ and any operator norm $\|\cdot \|$ on $C^{n\times n}$. Here $dist(σ(A), σ(B))$ denotes the optimal matching distance between the spectra of the matrices $A$ and $B$. It was proved by A.J. Holbrook (1992) that this conjecture is false in general. However it is true for the Frobenius distance and the Frobenius norm (the Hoffman-Wielandt inequality). The main aim of this paper is to study the Hoffman-Wielandt inequality and some weaker versions of the Wielandt-Mirsky conjecture for matrix polynomials.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes