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A Euclidean vector may possess a definite initial point and terminal point; such a condition may be emphasized calling the result a bound vector. [12] When only the magnitude and direction of the vector matter, and the particular initial or terminal points are of no importance, the vector is called a free vector .
In linear algebra, the outer product of two coordinate vectors is the matrix whose entries are all products of an element in the first vector with an element in the second vector. If the two coordinate vectors have dimensions n and m , then their outer product is an n × m matrix.
A Euclidean vector space is a finite-dimensional inner product space over the real numbers. [6] A Euclidean space is an affine space over the reals such that the associated vector space is a Euclidean vector space. Euclidean spaces are sometimes called Euclidean affine spaces to distinguish them from Euclidean vector spaces. [6]
The self dot product of a complex vector =, involving the conjugate transpose of a row vector, is also known as the norm squared, = ‖ ‖, after the Euclidean norm; it is a vector generalization of the absolute square of a complex scalar (see also: Squared Euclidean distance).
The cross product with respect to a right-handed coordinate system. In mathematics, the cross product or vector product (occasionally directed area product, to emphasize its geometric significance) is a binary operation on two vectors in a three-dimensional oriented Euclidean vector space (named here ), and is denoted by the symbol .
A sequence (x n) converges to a point x if for every ε > 0 there is an integer N such that for all n > N, d(x n, x) < ε. Convergence of sequences in a topological space is defined as follows: A sequence (x n) converges to a point x if for every open set U containing x there is an integer N such that for all n > N, .
The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...
Every vector a in three dimensions is a linear combination of the standard basis vectors i, j and k.. In mathematics, the standard basis (also called natural basis or canonical basis) of a coordinate vector space (such as or ) is the set of vectors, each of whose components are all zero, except one that equals 1. [1]