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In the mathematical fields of linear algebra and functional analysis, the orthogonal complement of a subspace of a vector space equipped with a bilinear form is the set of all vectors in that are orthogonal to every vector in .
The column space of a matrix A is the set of all linear combinations of the columns in A. If A = [a 1 ⋯ a n], then colsp(A) = span({a 1, ..., a n}). Given a matrix A, the action of the matrix A on a vector x returns a linear combination of the columns of A with the coordinates of x as coefficients; that is, the columns of the matrix generate ...
Visual understanding of multiplication by the transpose of a matrix. If A is an orthogonal matrix and B is its transpose, the ij-th element of the product AA T will vanish if i≠j, because the i-th row of A is orthogonal to the j-th row of A. An orthogonal matrix is the real specialization of a unitary matrix, and thus always a normal matrix.
If V is a closed subspace of H, then V ⊥ is called the orthogonal complement of V. In fact, every x ∈ H can then be written uniquely as x = v + w, with v ∈ V and w ∈ V ⊥. Therefore, H is the internal Hilbert direct sum of V and V ⊥. The linear operator P V : H → H that maps x to v is called the orthogonal projection onto V.
In the branch of mathematics called functional analysis, a complemented subspace of a topological vector space, is a vector subspace for which there exists some other vector subspace of , called its (topological) complement in , such that is the direct sum in the category of topological vector spaces.
1. Orthogonal complement: If W is a linear subspace of an inner product space V, then denotes its orthogonal complement, that is, the linear space of the elements of V whose inner products with the elements of W are all zero. 2.
A matrix, has its column space depicted as the green line. The projection of some vector onto the column space of is the vector . From the figure, it is clear that the closest point from the vector onto the column space of , is , and is one where we can draw a line orthogonal to the column space of .
A set of vectors in an inner product space is called pairwise orthogonal if each pairing of them is orthogonal. Such a set is called an orthogonal set (or orthogonal system). If the vectors are normalized, they form an orthonormal system. An orthogonal matrix is a matrix whose column vectors are orthonormal to each other.