Examples of using Eigenvectors in English and their translations into Russian
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The 128 largest eigenvectors are used for description.
Eigenvalue decomposition finding eigenvalues and eigenvectors.
Eigenvectors can be found by exploiting the Cayley-Hamilton theorem.
These eigenvalue algorithms may also find eigenvectors.
Eigenvectors of distinct eigenvalues of a normal matrix are orthogonal.
Some algorithms also produce sequences of vectors that converge to the eigenvectors.
Eigenvalues and eigenvectors of square positive matrices are described by the Perron-Frobenius theorem.
The main parameters of the wave process are described by eigenvectors of the transition matrix.
Moreover, the first few eigenvectors can often be interpreted in terms of the large-scale physical behavior of the system.
If the compactness assumption is removed,it is not true that every self-adjoint operator has eigenvectors.
In this special case, the columns of U∗ are eigenvectors of both A and B and form an orthonormal basis in Cn.
In practice, the covariance(and sometimes the correlation) matrix of the data is constructed and the eigenvectors on this matrix are computed.
Dorodnicyn Close Calculation of Eigenvalues and Eigenvectors of Symmetric Matrices on Case of Self-Conjugate Discrete Operators.
For Hermitian matrices,the Divide-and-conquer eigenvalue algorithm is more efficient than the QR algorithm if both eigenvectors and eigenvalues are desired.
The invertibility of P{\displaystyle P} also suggests that the eigenvectors are linearly independent and form a basis of F n{\displaystyle F^{n.
This gives new eigenvectors, which we can call K1 which is the sum of the two states of opposite strangeness, and K2, which is the difference.
For more general matrices, the QR algorithm yields the Schur decomposition first, from which the eigenvectors can be obtained by a backsubstitution procedure.
The eigenvectors that are relevant are the ones that correspond to smallest several eigenvalues of the Laplacian except for the smallest eigenvalue which will have a value of 0.
The Jacobi eigenvalue algorithm is an iterative method for the calculation of the eigenvalues and eigenvectors of a real symmetric matrix a process known as diagonalization.
The eigenvectors that correspond to the largest eigenvalues(the principal components) can now be used to reconstruct a large fraction of the variance of the original data.
However, in practical large-scale eigenvalue methods, the eigenvectors are usually computed in other ways, as a byproduct of the eigenvalue computation.
The critical points of the equation(where d V/ d log ξ d U/ d log ξ 0{\displaystyle dV/d\log\xi=dU/d\log\xi =0})and the eigenvalues and eigenvectors of the Jacobian matrix are tabulated below.
Equivalently, these singular vectors are the eigenvectors corresponding to the p largest eigenvalues of the sample covariance matrix of the input vectors.
We proof that there are no strongly continuous anduniformly bounded periodic one-parameter group of operators in Banach space which eigenvectors are cross-frame.
If eigenvectors are needed as well,the similarity matrix may be needed to transform the eigenvectors of the Hessenberg matrix back into eigenvectors of the original matrix.
The general approach to spectral clustering is to use a standardclustering method(there are many such methods, k-means is discussed below) on relevant eigenvectors of a Laplacian matrix of A{\displaystyle A.
The asymptotic estimates of eigenvalue, eigenvectors, spectral estimation of equiconvergence applications for the test operator and the operator of multiplication by a sequence a: Z→ C.
In mathematics, spectral graph theory is the study of the properties of a graph in relationship to the characteristic polynomial,eigenvalues, and eigenvectors of matrices associated with the graph, such as its adjacency matrix or Laplacian matrix.
For computational efficiency, these eigenvectors are often computed as the eigenvectors corresponding to the largest several eigenvalues of a function of the Laplacian.
Determinant of the matrix and algorithm of its calculation, the characteristic polynomial of the matrix and the eigenvalues of matrix,canonical form of characteristic matrix, eigenvectors of matrix, elementary divisors of the characteristic matrix, etc.