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  2. Hermitian matrix - Wikipedia

    en.wikipedia.org/wiki/Hermitian_matrix

    Here, we offer another useful Hermitian matrix using an abstract example. If a square matrix A {\displaystyle A} equals the product of a matrix with its conjugate transpose, that is, A = B B H , {\displaystyle A=BB^{\mathsf {H}},} then A {\displaystyle A} is a Hermitian positive semi-definite matrix .

  3. Conjugate transpose - Wikipedia

    en.wikipedia.org/wiki/Conjugate_transpose

    In mathematics, the conjugate transpose, also known as the Hermitian transpose, of an complex matrix is an matrix obtained by transposing and applying complex conjugation to each entry (the complex conjugate of + being , for real numbers and ).

  4. Cholesky decomposition - Wikipedia

    en.wikipedia.org/wiki/Cholesky_decomposition

    In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.

  5. Skew-Hermitian matrix - Wikipedia

    en.wikipedia.org/wiki/Skew-Hermitian_matrix

    In linear algebra, a square matrix with complex entries is said to be skew-Hermitian or anti-Hermitian if its conjugate transpose is the negative of the original matrix. [1] That is, the matrix A {\displaystyle A} is skew-Hermitian if it satisfies the relation

  6. Definite matrix - Wikipedia

    en.wikipedia.org/wiki/Definite_matrix

    In mathematics, a symmetric matrix with real entries is positive-definite if the real number is positive for every nonzero real column vector, where is the row vector transpose of . [1] More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positive-definite if the real number is positive for every nonzero complex column vector , where denotes the ...

  7. Sylvester's criterion - Wikipedia

    en.wikipedia.org/wiki/Sylvester's_criterion

    In mathematics, Sylvester’s criterion is a necessary and sufficient criterion to determine whether a Hermitian matrix is positive-definite. Sylvester's criterion states that a n × n Hermitian matrix M is positive-definite if and only if all the following matrices have a positive determinant: the upper left 1-by-1 corner of M,

  8. Commuting matrices - Wikipedia

    en.wikipedia.org/wiki/Commuting_matrices

    The property of two matrices commuting is not transitive: A matrix may commute with both and , and still and do not commute with each other. As an example, the identity matrix commutes with all matrices, which between them do not all commute. If the set of matrices considered is restricted to Hermitian matrices without multiple eigenvalues ...

  9. Inner product space - Wikipedia

    en.wikipedia.org/wiki/Inner_product_space

    The general form of an inner product on is known as the Hermitian form and is given by , = † = † ¯, where is any Hermitian positive-definite matrix and † is the conjugate transpose of . For the real case, this corresponds to the dot product of the results of directionally-different scaling of the two vectors, with positive scale factors ...