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In linear algebra, an orthogonal matrix, or orthonormal matrix, is a real square matrix whose columns and rows are orthonormal vectors. One way to express this is Q T Q = Q Q T = I , {\displaystyle Q^{\mathrm {T} }Q=QQ^{\mathrm {T} }=I,} where Q T is the transpose of Q and I is the identity matrix .
A unit vector means that the vector has a length of 1, which is also known as normalized. Orthogonal means that the vectors are all perpendicular to each other. A set of vectors form an orthonormal set if all vectors in the set are mutually orthogonal and all of unit length. An orthonormal set which forms a basis is called an orthonormal basis.
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.
An orthonormal basis can be derived from an orthogonal basis via normalization. The choice of an origin and an orthonormal basis forms a coordinate frame known as an orthonormal frame. For a general inner product space , an orthonormal basis can be used to define normalized orthogonal coordinates on .
Transformations with reflection are represented by matrices with a determinant of −1. This allows the concept of rotation and reflection to be generalized to higher dimensions. In finite-dimensional spaces, the matrix representation (with respect to an orthonormal basis) of an orthogonal transformation is an orthogonal matrix.
An orthogonal matrix is a matrix whose column vectors are orthonormal to each other. An orthonormal basis is a basis whose vectors are both orthogonal and normalized (they are unit vectors ). A conformal linear transformation preserves angles and distance ratios, meaning that transforming orthogonal vectors by the same conformal linear ...
Orthogonal matrix: A matrix whose inverse is equal to its transpose, A −1 = A T. They form the orthogonal group. Orthonormal matrix: A matrix whose columns are orthonormal vectors. Partially Isometric matrix: A matrix that is an isometry on the orthogonal complement of its kernel. Equivalently, a matrix that satisfies AA * A = A.
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.