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  2. Biorthogonal system - Wikipedia

    en.wikipedia.org/wiki/Biorthogonal_system

    An example is the pair of sets of respectively left and right eigenvectors of a matrix, indexed by eigenvalue, if the eigenvalues are distinct. [1] A biorthogonal system in which = and ~ = ~ is an orthonormal system.

  3. Quadratic eigenvalue problem - Wikipedia

    en.wikipedia.org/wiki/Quadratic_eigenvalue_problem

    Quadratic eigenvalue problems arise naturally in the solution of systems of second order linear differential equations without forcing: ″ + ′ + = Where (), and ,,.If all quadratic eigenvalues of () = + + are distinct, then the solution can be written in terms of the quadratic eigenvalues and right quadratic eigenvectors as

  4. Diffusion map - Wikipedia

    en.wikipedia.org/wiki/Diffusion_map

    Download QR code; Print/export ... are the biorthogonal left and right eigenvectors respectively. Due to the spectrum decay of the eigenvalues, only a few terms are ...

  5. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    Top: The action of M, indicated by its effect on the unit disc D and the two canonical unit vectors e 1 and e 2. Left: The action of V ⁎, a rotation, on D, e 1, and e 2. Bottom: The action of Σ, a scaling by the singular values σ 1 horizontally and σ 2 vertically.

  6. Eigenvalues and eigenvectors - Wikipedia

    en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors

    Points in the top half are moved to the right, and points in the bottom half are moved to the left, proportional to how far they are from the horizontal axis that goes through the middle of the painting. The vectors pointing to each point in the original image are therefore tilted right or left, and made longer or shorter by the transformation.

  7. 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.

  8. Jacobi eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Jacobi_eigenvalue_algorithm

    A round would consist of each processor first calculating (,) for its rotation, and then applying the rotation from the left (rotating between rows). Next, the processors synchronise before applying the transpose rotation from the right (rotating between columns), and finally synchronising again. A matrix element may be accessed by two ...

  9. Linear dynamical system - Wikipedia

    en.wikipedia.org/wiki/Linear_dynamical_system

    Download QR code; Print/export ... by a linear combination of the right and left eigenvectors ... of the individual solutions for the right eigenvectors = ...