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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.
A matrix = [] belongs to the orthogonal group if AQA T = Q, that is, a 2 – ωb 2 = 1, ac – ωbd = 0, and c 2 – ωd 2 = –ω. As a and b cannot be both zero (because of the first equation), the second equation implies the existence of ε in F q , such that c = εωb and d = εa .
The RQ decomposition transforms a matrix A into the product of an upper triangular matrix R (also known as right-triangular) and an orthogonal matrix Q. The only difference from QR decomposition is the order of these matrices. QR decomposition is Gram–Schmidt orthogonalization of columns of A, started from the first column.
The indefinite special orthogonal group, SO(p, q) is the subgroup of O(p, q) consisting of all elements with determinant 1. Unlike in the definite case, SO(p, q) is not connected – it has 2 components – and there are two additional finite index subgroups, namely the connected SO + (p, q) and O + (p, q), which has 2 components – see ...
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).
Thus a real symmetric matrix A can be decomposed as =, where Q is an orthogonal matrix whose columns are the real, orthonormal eigenvectors of A, and Λ is a diagonal matrix whose entries are the eigenvalues of A.
Stated in terms of numerical linear algebra, we convert M to an orthogonal matrix, Q, using QR decomposition. However, we often prefer a Q closest to M, which this method does not accomplish. For that, the tool we want is the polar decomposition (Fan & Hoffman 1955; Higham 1989).
In finite-dimensional spaces, the matrix representation (with respect to an orthonormal basis) of an orthogonal transformation is an orthogonal matrix. Its rows are mutually orthogonal vectors with unit norm, so that the rows constitute an orthonormal basis of V. The columns of the matrix form another orthonormal basis of V.