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  2. Eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Eigenvalue_algorithm

    Given an n × n square matrix A of real or complex numbers, an eigenvalue λ and its associated generalized eigenvector v are a pair obeying the relation [1] =,where v is a nonzero n × 1 column vector, I is the n × n identity matrix, k is a positive integer, and both λ and v are allowed to be complex even when A is real.l When k = 1, the vector is called simply an eigenvector, and the pair ...

  3. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    Let A be a square n × n matrix with n linearly independent eigenvectors q i (where i = 1, ..., n).Then A can be factored as = where Q is the square n × n matrix whose i th column is the eigenvector q i of A, and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, Λ ii = λ i.

  4. Eigenvalues and eigenvectors - Wikipedia

    en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors

    Admissible solutions are then a linear combination of solutions to the generalized eigenvalue problem = where is the eigenvalue and is the (imaginary) angular frequency. The principal vibration modes are different from the principal compliance modes, which are the eigenvectors of k {\displaystyle k} alone.

  5. Rayleigh–Ritz method - Wikipedia

    en.wikipedia.org/wiki/Rayleigh–Ritz_method

    In numerical linear algebra, the Rayleigh–Ritz method is commonly [12] applied to approximate an eigenvalue problem = for the matrix of size using a projected matrix of a smaller size <, generated from a given matrix with orthonormal columns. The matrix version of the algorithm is the most simple:

  6. Arnoldi iteration - Wikipedia

    en.wikipedia.org/wiki/Arnoldi_iteration

    In numerical linear algebra, the Arnoldi iteration is an eigenvalue algorithm and an important example of an iterative method.Arnoldi finds an approximation to the eigenvalues and eigenvectors of general (possibly non-Hermitian) matrices by constructing an orthonormal basis of the Krylov subspace, which makes it particularly useful when dealing with large sparse matrices.

  7. Fredholm alternative - Wikipedia

    en.wikipedia.org/wiki/Fredholm_alternative

    For each λ ∈ R, either λ is an eigenvalue of K, or the operator K − λ is bijective from X to itself. Let us explore the two alternatives as they play out for the boundary-value problem. Suppose λ ≠ 0. Then either (A) λ is an eigenvalue of K ⇔ there is a solution h ∈ dom(L) of (L + μ 0) h = λ −1 h ⇔ –μ 0 +λ −1 is an ...

  8. Jacobi eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Jacobi_eigenvalue_algorithm

    3. The eigenvalues are not necessarily in descending order. This can be achieved by a simple sorting algorithm. for k := 1 to n−1 do m := k for l := k+1 to n do if e l > e m then m := l endif endfor if k ≠ m then swap e m,e k swap E m,E k endif endfor. 4. The algorithm is written using matrix notation (1 based arrays instead of 0 based). 5.

  9. Nonlinear eigenproblem - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_eigenproblem

    The NLEVP collection of nonlinear eigenvalue problems is a MATLAB package containing many nonlinear eigenvalue problems with various properties. [ 6 ] The FEAST eigenvalue solver is a software package for standard eigenvalue problems as well as nonlinear eigenvalue problems, designed from density-matrix representation in quantum mechanics ...