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  2. Orthogonal matrix - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_matrix

    An orthogonal matrix Q is necessarily invertible (with inverse Q −1 = Q T), unitary (Q −1 = Q ∗), where Q ∗ is the Hermitian adjoint (conjugate transpose) of Q, and therefore normal (Q ∗ Q = QQ ∗) over the real numbers. The determinant of any orthogonal matrix is either +1 or −1.

  3. Transpose - Wikipedia

    en.wikipedia.org/wiki/Transpose

    A square matrix whose transpose is equal to its inverse is called an orthogonal matrix; that is, A is orthogonal if =. A square complex matrix whose transpose is equal to its conjugate inverse is called a unitary matrix; that is, A is unitary if

  4. Conjugate transpose - Wikipedia

    en.wikipedia.org/wiki/Conjugate_transpose

    The conjugate transpose of a matrix with real entries reduces to the transpose of , as the conjugate of a real number is the number itself. The conjugate transpose can be motivated by noting that complex numbers can be usefully represented by 2 × 2 {\displaystyle 2\times 2} real matrices, obeying matrix addition and multiplication:

  5. Orthogonal group - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_group

    Equivalently, it is the group of n × n orthogonal matrices, where the group operation is given by matrix multiplication (an orthogonal matrix is a real matrix whose inverse equals its transpose). The orthogonal group is an algebraic group and a Lie group. It is compact. The orthogonal group in dimension n has two connected components.

  6. Matrix (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Matrix_(mathematics)

    An orthogonal matrix is a square matrix with real entries whose columns and rows are orthogonal unit vectors (that is, orthonormal vectors). Equivalently, a matrix A is orthogonal if its transpose is equal to its inverse: =, which entails

  7. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    One can always write = where V is a real orthogonal matrix, is the transpose of V, and S is a block upper triangular matrix called the real Schur form. The blocks on the diagonal of S are of size 1×1 (in which case they represent real eigenvalues) or 2×2 (in which case they are derived from complex conjugate eigenvalue pairs).

  8. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    Specifically, the singular value decomposition of an complex matrix ⁠ ⁠ is a factorization of the form =, where ⁠ ⁠ is an ⁠ ⁠ complex unitary matrix, is an rectangular diagonal matrix with non-negative real numbers on the diagonal, ⁠ ⁠ is an complex unitary matrix, and is the conjugate transpose of ⁠ ⁠. Such decomposition ...

  9. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    For a complex matrix, the transpose is replaced with the conjugate transpose. [12] ... If ⁠ ⁠ is an orthogonal projection matrix, that is, = ...