Results 11 to 20 of about 197,057 (337)
Applied Sciences, 2023 The calculation of accurate arbitrary-order sensitivities of eigenvalues and eigenvectors is crucial for structural analysis applications, including topology optimization, system identification, finite element model updating, damage detection, and fault ...
Juan C. Velasquez-Gonzalez +5 more
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DTI Brain Template Construction Based on Gaussian Averaging
Chinese Journal of Magnetic Resonance, 2022 The tensor data of subjects are usually averaged linearly over multiple channels to obtain the tensor template. However, linear averaging ignores the vector information in the tensor. Additionally, it will render the interface between the gray matter and
DENG Lan, WANG Yuan-jun
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Eigenvalue Expansion of Nonsymmetric Linear Compact Operators in Hilbert Space
Communications in Advanced Mathematical Sciences, 2021 For a symmetric linear compact resp. symmetric densely defined linear operator with compact inverse, expansion theorems in series of eigenvectors are known.
Ludwig Kohaupt
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First-order Perturbation Theory for Eigenvalues and Eigenvectors [PDF]
SIAM Review, 2019 We present first-order perturbation analysis of a simple eigenvalue and the corresponding right and left eigenvectors of a general square matrix, not assumed to be Hermitian or normal.
A. Greenbaum, Ren-Cang Li, M. Overton
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Eigenvalues and Jordan Forms of Dual Complex Matrices [PDF]
Communication on Applied Mathematics and Computation, 2023 Dual complex matrices have found applications in brain science. There are two different definitions of the dual complex number multiplication. One is noncommutative. Another is commutative. In this paper, we use the commutative definition.
Liqun Qi, Chunfeng Cui
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MASALAH EIGEN DAN EIGENMODE MATRIKS ATAS ALJABAR MIN-PLUS
Barekeng, 2021 Eigen problems and eigenmode are important components related to square matrices. In max-plus algebra, a square matrix can be represented in the form of a graph called a communication graph.
Eka Widia Rahayu +2 more
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Surface Registration with Eigenvalues and Eigenvectors
IEEE Transactions on Visualization and Computer Graphics, 2020 This paper presents a novel surface registration technique using the spectrum of the shapes, which can facilitate accurate localization and visualization of non-isometric deformations of the surfaces.
Hajar Hamidian +3 more
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Expansions for eigenfunction and eigenvalues of large-$n$ Toeplitz matrices [PDF]
Papers in Physics, 2010 This paper constructs methods for finding convergent expansions for eigenvectors and eigenvalues of large-$n$ Toeplitz matrices based on a situation in which the analogous infinite-$n$ matrix would be singular. It builds upon work done by Dai, Geary, and
Leo P. Kadanoff
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The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices [PDF]
, 2011 Florent Benaych-Georges +1 more
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Eigenvectors from Eigenvalues Sparse Principal Component Analysis [PDF]
Journal of Computational And Graphical Statistics, 2020 We present a novel technique for sparse principal component analysis. This method, named eigenvectors from eigenvalues sparse principal component analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix ...
H. R. Frost
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