Fuzzy soft tensor based group decision making approach with application to heterogeneous wireless network evaluation. [PDF]
Bilal M, Chaoqian L, Popa IL.
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Graph-based vision transformer with sparsity for training on small datasets from scratch. [PDF]
Li P, Huang L, Li J, Yan H, Shan D.
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Multiscale performance analysis and optimization of a composite clamp plate for leaf spring assembly considering fiber orientation distribution. [PDF]
Wang F, Luo W, Zou B, Yang J, Huang A.
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Mode-wise principal subspace pursuit and matrix spiked covariance model. [PDF]
Tang R, Yuan M, Zhang AR.
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Towards a hierarchical approach and scalable semi-local centrality for key node identification in weighted complex networks. [PDF]
Kong L, Xie W, Abshirini AA.
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Computational corrections for anisotropic absorption in tensor tomography. [PDF]
Carlsen M, Liebi M.
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Solution Analysis of Tensor Equation $\mathcal{A} \ltimes \mathcal{X} \ltimes \mathcal{B}= \mathcal{C}$ via Semi Tensor Product with t-product [PDF]
Bhawna Garg, Ranjan Kumar Das
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A Survey on Semi-Tensor Product of Matrices
Journal of Systems Science and Complexity, 2007A type \((m,n)\) matrix \(A\) and a type \((p,q)\) matrix \(B\) are said to be satisfying the factor dimension condition if \(n\) devides \(p\) or \(q\) devides \(n\). In this paper a product (called semi-tensor product) of matrices satisfying the factor dimension condition is surveyed. The authors list the basic algebraic properties of the semi-tensor
Daizhan Cheng +2 more
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On Semi-tensor Product of Matrices and Its Applications
Acta Mathematicae Applicatae Sinica, 2003Similarly to the left semi-tensor product the authors introduce the right semi-tensor product. Certain properties are presented. The major differences between the left and the right semi-tensor products are discussed. Then two new applications are investigated. The first one is its application to the connection.
Cheng, Daizhan, Zhang, Lijun
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