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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 106 for “"eigenvector"”.
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A generalized approach for calculation of the eigenvector sensitivity for various eigenvector normalizations
… based optimization problem. Eigenvalue and Eigenvector Sensitivity Analysis has been a major area for more than three decades in structural optimization. An efficient and generalized method is required to do the sensitivity analysis as it can reduce computational time for large industrial …
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Neural Networks on Eigenvector Data
The need to process eigenvectors derived from data arises across numerous domains in computing and the sciences. However, eigenvectors differ from other types of data, as they have particular symmetries; for any eigenvector of a matrix, the negation of that vector is also an eigenvector of the same …
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Three methods of the complete eigenvalue-eigenvector problem
Thesis (M.A.)--Boston University
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Protein structure recognition: from eigenvector analysis to structural threading method
… protein. This correlation was observed in our eigenvector study of protein contact matrix. We believe the correlation is universal, thus it can be used in automatic partition of protein structures into folding domains.;In the third part, we discuss a threading method based on the correlation …
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Identifying the largest component of the dominant eigenvector of a matrix
… it will have only one linearly independent eigenvector, called the dominant eigenvector. In many applications of linear algebra, the components of the dominant eigenvector are important, particularly the largest. First row dominance conditions which guarantee that a given component of the …
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Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
… Laplacian matrix is considered: the first K eigenvectors of the graph Laplacian are pre-selected, e.g., based on domain-specific criteria, and the remaining eigenvectors are then learned from data. One example use case is image coding, where the first eigenvector is pre-chosen to be constant, …
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Discrimination and Enhancement of Fracture Signals on Surface Seismic Data
… included some incoherent noise to the data. The Eigenvector filter performed very well on flat or near-flat reflectors and left the fracture signals almost intact, but introduced some incoherent noise in the presence of steeply dipping reflectors. Harlan's scheme and Radon filtering are very …
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Inference on a genetic model
… along with the corresponding pre- and post-eigenvector matrices are given. The post-eigenvector matrix has elements consisting of Hahn polynomials. The pre-eigenvector matrix is obtained by inverting the post-eigenvector matrix for which an expression is given. The Hahn polynomials form a …
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Arbitrarily Oriented Biaxially Anisotropic Media: Wave Behavior and Microstrip Antennas
… coefficients are used in the formulation of eigenvector dyadic Green's functions. These Green's functions are employed in full-wave analyses of rectangular microstrip antennas printed on biaxial substrates.</p> <p>The general characteristics of electrically biaxially anisotropic (biaxial) …
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Optimal actuation in active vibration control using pole-placement
… chosen that is mathematically orthogonal to an eigenvector associated with a reassigned eigenvalue. Conversely, the optimal input vector was discovered to be mathematically parallel to an eigenvector. This was proven analytically through mathematical proofs and demonstrated with various …
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Self-Sustaining Formation of Packets of Hairpin Vortices in a Turbulent Wall Layer
… of vortices aligns with the local real eigenvector of the velocity gradient tensor field over almost the entire region occupied by the vortex. By using this feature, the backbone of a vortex can be constructed efficiently.
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Data parallel algebraic multigrid
… processing. We apply our solver to accelerate eigenvector computations necessary during spectral partitioning methods and find that performance is limited by multigrid construction. We propose and analyze a novel strategy to accelerate graph Laplacian eigenvector computations by combining …
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Learning on Inhomogeneous Hypergraphs
… I propose an efficient algorithm to compute the eigenvector associated with the second smallest eigenvalue of the 1-Laplacian and then use this eigenvector to cluster vertices in order to achieve better performance than traditional spectral clustering based on the 2-Laplacian.
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Sturm sequences and the eigenvalue distribution of the beta-Hermite random matrix ensemble
… sequence of a random matrix and its shooting eigenvectors. We show using Sturm sequences that, assuming the eigenvector contains no zeros, the number of sign changes in a shooting eigenvector of parameter A is equal to the number of eigenvalues greater than [lambda].
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A general computational tool for structure synthesis
… adjacency matrix, both the eigenvalue and the eigenvector are exploited; specifically the components of the eigenvector have been found very useful in algorithm development. This novel method is called the Eigensystem method. The complexity of the Eigensystem method is equal to that of the …
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Aircraft stability and departure prediction using Eigenvalue Sensitivity analysis
… Sensitivity (SADATES). Using eigenvalue and eigenvector analysis, SADATES is capable of performing a full-envelope stability analysis, returning both quantitative and qualitative data regarding the stability of the airplane at a static reference condition. SADATES not only supplies the …
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Applied stochastic eigen-analysis
… high-dimensionality, sample size constraints and eigenvector misspecification. By interpreting the eigenvalues of the sample covariance matrix as an interacting particle system, the existence of a phase transition phenomenon in the largest (“signal”) eigenvalue is derived using heuristic …
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A utilização do minicomputador para a solução de problemas de Engenharia Civil
… be used: linear and non linear analysis and eigenvector-eigenvalue solution. The internal system structure is discussed next, describing the strategy for handling information in a rational way, in order to optimize the time of access to the disk. Application examples are included, regarding …
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A study of the computation and convergence behavior of eigenvalue bounds for self-adjoint operators
… in quantum mechanics. Examples using the recent eigenvector-free (EVF) method of Beattie and Goerisch are considered. Since the EVF method uses finite element trial functions as approximating vectors, it produces sparse and well-structured coefficient matrices. For these large-order sparse matrix …
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