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  2. Hermitian matrix - Wikipedia

    en.wikipedia.org/wiki/Hermitian_matrix

    In signal processing, Hermitian matrices are utilized in tasks like Fourier analysis and signal representation. [2] The eigenvalues and eigenvectors of Hermitian matrices play a crucial role in analyzing signals and extracting meaningful information. Hermitian matrices are extensively studied in linear algebra and numerical analysis.

  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

    For a Hermitian matrix, the norm squared of the jth component of a normalized eigenvector can be calculated using only the matrix eigenvalues and the eigenvalues of the corresponding minor matrix, |, | = (()) (), where is the submatrix formed by removing the jth row and column from the original matrix.

  5. Eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Eigenvalue_algorithm

    The eigenvalues of a Hermitian matrix are real, since (λ − λ)v = (A * − A)v = (A − A)v = 0 for a non-zero eigenvector v. If A is real, there is an orthonormal basis for R n consisting of eigenvectors of A if and only if A is symmetric. It is possible for a real or complex matrix to have all real eigenvalues without being Hermitian.

  6. Weyl's inequality - Wikipedia

    en.wikipedia.org/wiki/Weyl's_inequality

    Let be a matrix with .Its singular values are the positive eigenvalues of the (+) (+) Hermitian augmented matrix [].Therefore, Weyl's eigenvalue perturbation inequality for Hermitian matrices extends naturally to perturbation of singular values. [1]

  7. Divide-and-conquer eigenvalue algorithm - Wikipedia

    en.wikipedia.org/wiki/Divide-and-conquer...

    As with most eigenvalue algorithms for Hermitian matrices, divide-and-conquer begins with a reduction to tridiagonal form. For an matrix, the standard method for this, via Householder reflections, takes floating point operations, or if eigenvectors are needed as well.

  8. Schur–Horn theorem - Wikipedia

    en.wikipedia.org/wiki/Schur–Horn_theorem

    Although this theorem requires that and be non-increasing, it is possible to reformulate this theorem without these assumptions.. We start with the assumption . The left hand side of the theorem's characterization (that is, "there exists a Hermitian matrix with these eigenvalues and diagonal elements") depends on the order of the desired diagonal elements , …, (because changing their order ...

  9. Pauli matrices - Wikipedia

    en.wikipedia.org/wiki/Pauli_matrices

    Wolfgang Pauli (1900–1958), c. 1924. Pauli received the Nobel Prize in Physics in 1945, nominated by Albert Einstein, for the Pauli exclusion principle.. In mathematical physics and mathematics, the Pauli matrices are a set of three 2 × 2 complex matrices that are traceless, Hermitian, involutory and unitary.