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Singular Value Decomposition of Spatial Matrices
Современные информационные технологии и IT-образование, 2022 Singular value decomposition is a basic building block which is used in solution of many different problems. In cases when dimensionality of a problem exceeds two, a generalization of a singular value decomposition – tensor decompositions – are used ...
Pavel Iljin, Tatiana Samoilova
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Energy-Based Adaptive CUR Matrix Decomposition
IEEE Access, 2023 CUR decompositions are interpretable data analysis tools that express a data matrix in terms of a small number of actual columns and/or actual rows of the data matrix.
Liwen Xu, Xuejiao Zhao, Yongxia Zhang
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Decomposition of Matrix under Neutrosophic Environment [PDF]
Neutrosophic Sets and Systems, 2019 Matrices help for the effective representation of systems of linear equations and analyzing any sort of data. The decomposition of any matrix allows for the efficient implementation of matrix-based algorithms.
Muhammad Kashif +3 more
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Materials & Design, 2022 Microstructure evolution, phase determination, phase decomposition and transition of mechanically alloyed Al0.8Co0.5Cr1.5CuFeNi during 950–1150 ℃ high temperature sintering, and the effect of sintered microstructures on mechanical properties were ...
Minjie Huang +4 more
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A New Method of Kronecker Product Decomposition
Journal of Mathematics, 2023 Kronecker product decomposition is often applied in various fields such as particle physics, signal processing, image processing, semidefinite programming, quantum computing, and matrix time series analysis.
Yi Wu
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Scientific Reports, 2023 Because the traditional Cholesky decomposition algorithm still has some problems such as computational complexity and scattered structure among matrices when solving the GNSS ambiguity, it is the key problem to further improve the computational ...
Yingxiang Jiao +5 more
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Two New Matrix Classes Related to the CMP Inverse: CMP and Co-CMP Matrices
Mathematics, 2023 This paper focuses on two new matrix classes related to the CMP inverse of a square matrix, called the CMP and co-CMP matrices. Some of their characterizations are identified based on the core-EP decomposition and subspace operations.
Yinlan Chen, Jiale Gao, Kezheng Zuo
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On decomposition of k-tridiagonal ℓ-Toeplitz matrices and its applications
Special Matrices, 2015 We consider a k-tridiagonal ℓ-Toeplitz matrix as one of generalizations of a tridiagonal Toeplitz matrix. In the present paper, we provide a decomposition of the matrix under a certain condition.
Ohashi A., Sogabe T., Usuda T.S.
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IEEE Access, 2022 In this paper, in order to improve the reliability of Multi-User Multiple-Input Multiple-Output Visible Light Communication (MU-MIMO-VLC) system, we propose a precoding scheme that based on Singular Value Decomposition (SVD) and Geometric Mean ...
Wenduo Qiu, Yan Feng, You Zhang
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Scalability of k-Tridiagonal Matrix Singular Value Decomposition
Mathematics, 2021 Singular value decomposition has recently seen a great theoretical improvement for k-tridiagonal matrices, obtaining a considerable speed up over all previous implementations, but at the cost of not ordering the singular values.
Andrei Tănăsescu +3 more
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