Minimal Geršgorin tensor eigenvalue inclusion set and its approximation

An algorithm for computing minimal Geršgorin sets

The first algorithms for computing the minimal Geršgorn set were developed by Varga et all. in [17] for the use on small and medium size (dense) matrices. Here, we first discuss the existing methods and present a new approach based on the modified Newton’s method to find zeros of the parameter dependent left-most eigenvalue of a Z-matrix. Additionally, sampling technique used in the original wo...

A new S-type eigenvalue inclusion set for tensors and its applications

In this paper, a new S-type eigenvalue localization set for a tensor is derived by dividing [Formula: see text] into disjoint subsets S and its complement. It is proved that this new set is sharper than those presented by Qi (J. Symb. Comput. 40:1302-1324, 2005), Li et al. (Numer. Linear Algebra Appl. 21:39-50, 2014) and Li et al. (Linear Algebra Appl. 481:36-53, 2015). As applications of the r...

An Approximation of the Minimal Point Set

In this paper, we consider an approximation of the ordering cone by a sequence of convex well-based cones that are contained inside the original cone. We derive related results on approximating minimal point sequences and propose a density result being evocative of the well-known ABB theorem.

A Minimal Set of Constraints for the Trivocal Tensor

An eigenvalue localization set for tensors and its applications

A new eigenvalue localization set for tensors is given and proved to be tighter than those presented by Li et al. (Linear Algebra Appl. 481:36-53, 2015) and Huang et al. (J. Inequal. Appl. 2016:254, 2016). As an application of this set, new bounds for the minimum eigenvalue of [Formula: see text]-tensors are established and proved to be sharper than some known results. Compared with the results...