Results 271 to 280 of about 308,320 (313)

A Semi-Tensor Product of Tensors and Applications

East Asian Journal on Applied Mathematics, 2022
Summary: A semi-tensor product of matrices is proposed as a generalization of usual matrix product in the case where the dimensions of two factor matrices do not match. The properties of the semi-tensor product of tensors and swap tensors based on the Einstein product are studied.
Liu, Wei-Hui, Xie, Ze-Jia, Jin, Xiao-Qing   +2 more
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Eshelby Tensor as a Tensor of Free Enthalpy

Journal of Elasticity, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Buratti, Giovanni, Huo, Yongzhong, Müller, Ingo   +2 more
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Time-Aware Tensor Decomposition for Sparse Tensors

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dawon Ahn, Jun-Gi Jang, U Kang
openaire   +3 more sources

On Q-tensors

Journal of Optimization Theory and Applications
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thiruvankatachari Parthasarathy, Gomatam Ravindran, Sunil Kumar   +2 more
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Tensors and Tensor Operators

1994
Abstract WE are accustomed to deal with certain physical quantities such as mass and energy which are in no way connected with the orientation of our coordinate system and which have no directional properties. Such quantities a.re sea.la.rs, or tensors of rank zero.
D M Brink, G R Satchler
openaire   +1 more source

Tensor Operator Methods and the Tensor Force

Physical Review, 1956
Tensor operator methods are applied to find the matrix elements of the two-nucleon tensor force between states of two inequivalent nucleons in $\mathrm{LS}$ coupling. The results are used to obtain the direct and exchange terms arising from a tensor-force interaction between states of a shell closed except for a single vacancy and external inequivalent
Hope, J., Longdon, L. W.
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The Derivation of Tensors from Tensor Functions

American Journal of Mathematics, 1931
Einfaches Nachrechnen ergibt den Satz: gegeben sei ein Tensor (bzw. eine Invariante) als Funktion eines zweiten Tensors. Differenziert man seine Komponenten nach denjenigen des unabhängigen, so erhält man wiederum einen Tensor, und zwar ergibt die Differentiation nach einem kovarianten Index einen neuen kontravarianten Index und umgekehrt.
openaire   +1 more source

Tensor SOM and tensor GTM: Nonlinear tensor analysis by topographic mappings

Neural Networks, 2016
In this paper, we propose nonlinear tensor analysis methods: the tensor self-organizing map (TSOM) and the tensor generative topographic mapping (TGTM). TSOM is a straightforward extension of the self-organizing map from high-dimensional data to tensorial data, and TGTM is an extension of the generative topographic map, which provides a theoretical ...
Tohru Iwasaki, Tetsuo Furukawa
openaire   +4 more sources

Smooth Tensor Product for Tensor Completion

IEEE Transactions on Image Processing
Low-rank tensor completion (LRTC) has shown promise in processing incomplete visual data, yet it often overlooks the inherent local smooth structures in images and videos. Recent advances in LRTC, integrating total variation regularization to capitalize on the local smoothness, have yielded notable improvements.
Tongle Wu, Jicong Fan 0001
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On Tensor-Train Ranks of Tensorized Polynomials

2020
Discretization followed by tensorization (mapping from low-dimensional to high-dimensional data) can be used to construct low-parametric approximations of functions. For example, a function f defined on [0, 1] may be mapped to a d-dimensional tensor \(A \in \mathbb {R}^{b\times \dots \times b}\) with elements \(A(i_1,\dots ,i_d) = f(i_1b^{-1} + \dots +
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