NANAApr 18, 2017

A tighter $Z$-eigenvalue localization set for tensors and its applications

arXiv:1704.03707h-index: 12
Originality Synthesis-oriented
AI Analysis

Provides an incremental improvement in eigenvalue localization for tensor analysis, benefiting researchers in tensor computation and related fields.

The paper proposes a tighter Z-eigenvalue localization set for tensors, which improves upon previous bounds, and derives a sharper upper bound for the Z-spectral radius of weakly symmetric nonnegative tensors, with numerical verification.

A new $Z$-eigenvalue localization set for tensors is given and proved to be tighter than those presented by Wang \emph{et al}. (Discrete and Continuous Dynamical Systems Series B 22(1): 187-198, 2017) and Zhao (J. Inequal. Appl., to appear, 2017). As an application, a sharper upper bound for the $Z$-spectral radius of weakly symmetric nonnegative tensors is obtained. Finally, numerical examples are given to verify the theoretical results.

Foundations

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

Your Notes