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  2. Hermitian matrix - Wikipedia

    en.wikipedia.org/wiki/Hermitian_matrix

    Hermitian matrices also appear in techniques like singular value decomposition (SVD) and eigenvalue decomposition. In statistics and machine learning, Hermitian matrices are used in covariance matrices, where they represent the relationships between different variables. The positive definiteness of a Hermitian covariance matrix ensures the well ...

  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. 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 ...

  5. 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 ...

  6. Transpose - Wikipedia

    en.wikipedia.org/wiki/Transpose

    A square complex matrix whose transpose is equal to the matrix with every entry replaced by its complex conjugate (denoted here with an overline) is called a Hermitian matrix (equivalent to the matrix being equal to its conjugate transpose); that is, A is Hermitian if = ¯.

  7. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    When ⁠ ⁠ is a positive-semidefinite Hermitian matrix, ⁠ ⁠ and ⁠ ⁠ are both equal to the unitary matrix used to diagonalize ⁠. ⁠ However, when ⁠ M {\displaystyle \mathbf {M} } ⁠ is not positive-semidefinite and Hermitian but still diagonalizable , its eigendecomposition and singular value decomposition are distinct.

  8. Self-adjoint operator - Wikipedia

    en.wikipedia.org/wiki/Self-adjoint_operator

    If V is finite-dimensional with a given orthonormal basis, this is equivalent to the condition that the matrix of A is a Hermitian matrix, i.e., equal to its conjugate transpose A ∗. By the finite-dimensional spectral theorem, V has an orthonormal basis such that the matrix of A relative to this basis is a diagonal matrix with entries in the ...

  9. Pauli matrices - Wikipedia

    en.wikipedia.org/wiki/Pauli_matrices

    In mathematical physics and mathematics, the Pauli matrices are a set of three 2 × 2 complex matrices that are traceless, Hermitian, involutory and unitary. Usually indicated by the Greek letter sigma ( σ ), they are occasionally denoted by tau ( τ ) when used in connection with isospin symmetries.