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Numerical Linear Algebra with Applications
Article . 2026 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2025
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Preprint . 2025
Data sources: DBLP
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On the Computation of Tensor Functions under Tensor‐Tensor Multiplications with Linear Maps

Authors: Jeong‐Hoon Ju; Susana López‐Moreno;

On the Computation of Tensor Functions under Tensor‐Tensor Multiplications with Linear Maps

Abstract

ABSTRACT In this paper, we study the computation of both algebraic and non‐algebraic tensor functions under the tensor‐tensor multiplication with linear maps. In the case of algebraic tensor functions, we prove that the asymptotic exponent of both the tensor‐tensor multiplication and the tensor polynomial evaluation problem under this multiplication is the same as that of the matrix multiplication, unless the linear map is injective. As for non‐algebraic functions, we define the tensor geometric mean and the tensor Wasserstein mean for pseudo‐positive‐definite tensors under the tensor‐tensor multiplication with invertible linear maps, and we show that the tensor geometric mean can be calculated by solving a specific Riccati tensor equation. Furthermore, we show that the tensor geometric mean does not satisfy the resultantal (determinantal) identity in general, which the matrix geometric mean always satisfies. Then we define a pseudo‐SVD for the injective linear map case, and we apply it to image and video data compression.

Related Organizations
Keywords

FOS: Computer and information sciences, Numerical Analysis, Computational Complexity, Commutative Algebra, 68Q17, 15A69, 14N07, 94A08, 47A64, FOS: Mathematics, Numerical Analysis (math.NA), Computational Complexity (cs.CC), Commutative Algebra (math.AC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Top 10%
Average
Average
Green
hybrid