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arXiv · 2212.10832

Numerical range for weighted Moore-Penrose inverse of tensor

Aaisha Be·Vaibhav Shekhar·Debasisha Mishra
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Abstract

This article first introduces the notion of weighted singular value decomposition (WSVD) of a tensor via the Einstein product. The WSVD is then used to compute the weighted Moore-Penrose inverse of an arbitrary-order tensor. We then define the notions of weighted normal tensor for an even-order square tensor and weighted tensor norm. Finally, we apply these to study the theory of numerical range for the weighted Moore-Penrose inverse of an even-order square tensor and exploit its several properties. We also obtain a few new results in the matrix setting that generalizes some of the existing results as particular cases.

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Aaisha Be, Vaibhav Shekhar, Debasisha Mishra. 2022-12-21. Numerical range for weighted Moore-Penrose inverse of tensor. https://doi.org/10.13001/ela.2024.8143

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