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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 .
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]
A vector pointing from point A to point B. In mathematics, physics, and engineering, a Euclidean vector or simply a vector (sometimes called a geometric vector [1] or spatial vector [2]) is a geometric object that has magnitude (or length) and direction.
Let K 3 denote the set of all triples x = (x 0, x 1, x 2) of elements of K (a Cartesian product viewed as a vector space). For any nonzero x in K 3, the minimal subspace of K 3 containing x (which may be visualized as all the vectors in a line through the origin) is the subset {:} of K 3.
More precisely, a straight line is said to be a tangent of a curve y = f(x) at a point x = c on the curve if the line passes through the point (c, f(c)) on the curve and has slope f ' (c) where f ' is the derivative of f. A similar definition applies to space curves and curves in n-dimensional Euclidean space.
Also, let Q = (x 1, y 1) be any point on this line and n the vector (a, b) starting at point Q. The vector n is perpendicular to the line, and the distance d from point P to the line is equal to the length of the orthogonal projection of on n. The length of this projection is given by:
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 .
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).