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In mathematics, a metric space is a set together with a notion of distance between its elements, usually called points. The distance is measured by a function called a metric or distance function. [1] Metric spaces are the most general setting for studying many of the concepts of mathematical analysis and geometry.
The distance formula on the plane follows from the Pythagorean theorem. In analytic geometry, geometric notions such as distance and angle measure are defined using formulas . These definitions are designed to be consistent with the underlying Euclidean geometry .
The distance from a point to a plane in three-dimensional Euclidean space [8] The distance between two lines in three-dimensional Euclidean space [9] The distance from a point to a curve can be used to define its parallel curve, another curve all of whose points have the same distance to the given curve. [10]
A unit distance graph with 16 vertices and 40 edges. In mathematics, particularly geometric graph theory, a unit distance graph is a graph formed from a collection of points in the Euclidean plane by connecting two points whenever the distance between them is exactly one.
A metric or distance function is a function d which takes pairs of points or objects to real numbers and satisfies the following rules: The distance between an object and itself is always zero. The distance between distinct objects is always positive. Distance is symmetric: the distance from x to y is always the same as the distance from y to x.
The Canberra distance is a numerical measure of the distance between pairs of points in a vector space, introduced in 1966 [1] and refined in 1967 [2] by Godfrey N. Lance and William T. Williams. It is a weighted version of L ₁ (Manhattan) distance . [ 3 ]
Distance geometry is the branch of mathematics concerned with characterizing and studying sets of points based only on given values of the distances between pairs of points. [ 1 ] [ 2 ] [ 3 ] More abstractly, it is the study of semimetric spaces and the isometric transformations between them.
The definition of the Hausdorff distance can be derived by a series of natural extensions of the distance function (,) in the underlying metric space M, as follows: [7] Define a distance function between any point x of M and any non-empty set Y of M by d ( x , Y ) = inf { d ( x , y ) ∣ y ∈ Y } . {\displaystyle d(x,Y)=\inf\{d(x,y)\mid y\in Y\}.}