Abstract
The main aim of this paper is to generalize matrix numerical ranges to the tensor case based on tensor norms. We show that the basic properties of matrix numerical ranges such as compactness and convexity are valid for tensor numerical ranges. We make use of convexity property to propose an algorithm for approximating tensor numerical ranges in which tensor eigenvalues are contained. Also we consider tensor numerical ranges based on inner products, however, they may not be convex in general.
Original language | English |
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Pages (from-to) | 100-132 |
Number of pages | 33 |
Journal | Linear Algebra and Its Applications |
Volume | 508 |
DOIs | |
Publication status | Published - 1 Nov 2016 |
Scopus Subject Areas
- Algebra and Number Theory
- Numerical Analysis
- Geometry and Topology
- Discrete Mathematics and Combinatorics
User-Defined Keywords
- Approximation
- Convexity
- Eigenvalues
- Numerical range
- Tensors