In mathematics, n-dimensional Euclidean space (also called Cartesian space or n-space) refers to the space of ordered n-tuples of real numbers along with the associated operations of component-wise addition and scalar multiplication which make it into a vector space, and the dot product which makes it into an inner product space and a normed space with the Euclidean norm. Euclidean space is a generalization of the 2- and 3-dimensional metric spaces studied by Euclid. The generalization applies Euclid's concept of distance, and the related concepts of length and angle, to a coordinate system in any number of dimensions. It is the "standard" example of a finite-dimensional, real, inner product space.

A Euclidean space enables the investigation of topological properties such as compactness. An inner product space is a generalization of a Euclidean space. Both inner product spaces and metric spaces are explored within functional analysis.

Euclidean space plays a part in the definition of a manifold which embraces the concepts of both Euclidean and non-Euclidean geometry. One mathematical motivation for defining a distance function is the ability to define an open ball around points in the space. This fundamental concept justifies a differential calculus between a Euclidean space and other manifolds. Differential geometry brings such a differential calculus into play, together with a technique of launching a mobile, local Euclidean space, to explore the properties of non-Euclidean manifolds.

Real coordinate space

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Let R denote the field of real numbers. For any non-negative integer n, the space of all n-tuples of real numbers forms an n-dimensional vector space over R sometimes called real coordinate space and denoted Rⁿ.

An element of Rⁿ is written x = (x₁, x₂, …, x_n) where each x_i is a real number. The vector space operations on Rⁿ are defined by

$\backslash mathbf\{x\}\; +\; \backslash mathbf\{y\}\; =\; (x\_1\; +\; y\_1,\; x\_2\; +\; y\_2,\; \backslash ldots,\; x\_n\; +\; y\_n)$

$a\backslash ,\backslash mathbf\{x\}\; =\; (a\; x\_1,\; a\; x\_2,\; \backslash ldots,\; a\; x\_n)$

Real coordinate space Rⁿ comes with a standard basis:

$\backslash mathbf\{e\}\_1\; =\; (1,\; 0,\; \backslash ldots,\; 0)$

$\backslash mathbf\{e\}\_2\; =\; (0,\; 1,\; \backslash ldots,\; 0)$

$\backslash vdots$

$\backslash mathbf\{e\}\_n\; =\; (0,\; 0,\; \backslash ldots,\; 1)$

An arbitrary vector in Rⁿ can then be written in the form

$\backslash mathbf\{x\}\; =\; \backslash sum\_\{i=1\}^n\; x\_i\; \backslash mathbf\{e\}\_i$

Real coordinate space is the prototypical example of a real n-dimensional vector space. In fact, every real n-dimensional vector space V is isomorphic to Rⁿ. This isomorphism is not canonical however. A choice of isomorphism is equivalent to a choice of basis for V (by looking at the image of the standard basis for Rⁿ in V). The reason for working with arbitrary vector spaces instead of Rⁿ is that it is often preferable to work in a coordinate-free manner (i.e. without choosing a preferred basis).

Euclidean structure

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Euclidean space is more than just real coordinate space. In order to do Euclidean geometry one needs to be able to talk about the distance between points and the angles between lines or vectors. The natural way in which to do this is to introduce what is called an inner product or dot product on Rⁿ. This product is defined by

$\backslash mathbf\{x\}\backslash cdot\backslash mathbf\{y\}\; =\; \backslash sum\_\{i=1\}^n\; x\_iy\_i\; =\; x\_1y\_1+x\_2y\_2+\backslash cdots+x\_ny\_n.$

The dot product of any two vectors x and y gives a real number. This product allows us to define the "length" of a vector x in the following way

$\backslash |\backslash mathbf\{x\}\backslash |\; =\; \backslash sqrt\{\backslash mathbf\{x\}\backslash cdot\backslash mathbf\{x\}\}\; =\; \backslash sqrt\{\backslash sum\_\{i=1\}^\{n\}(x\_i)^2\}$

This length function satisfies the required properties of a norm and is called the Euclidean norm on Rⁿ. The (interior) angle θ between x and y is then given by

$\backslash theta\; =\; \backslash cos^\{-1\}\backslash left(\backslash frac\{\backslash mathbf\{x\}\backslash cdot\backslash mathbf\{y\}\}\{\backslash |\backslash mathbf\{x\}\backslash |\backslash |\backslash mathbf\{y\}\backslash |\}\backslash right)$

where cos⁻¹ is the arccosine function. Finally, one can use the norm to define a distance function (or metric) on Rⁿ in the following manner

$d(\backslash mathbf\{x\},\; \backslash mathbf\{y\})\; =\; \backslash |\backslash mathbf\{x\}\; -\; \backslash mathbf\{y\}\backslash |\; =\; \backslash sqrt\{\backslash sum\_\{i=1\}^n\; (x\_i\; -\; y\_i)^2\}.$

The form of this distance function is based on the Pythagorean theorem, and is called the Euclidean metric.

Real coordinate space together with the above Euclidean structure (dot product and the associated norm and metric) is called Euclidean space often denoted by Eⁿ. (Many authors refer to Rⁿ itself as Euclidean space, with the Euclidean structure being understood). The Euclidean structure on Eⁿ gives it the structure of an inner product space (in fact a Hilbert space), a normed vector space, and a metric space.

Alternative definition

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In fact, Euclidean space Eⁿ is a real n-dimensional affine space such as its corresponding linear or vector space ( isomorphic to the linear or vector space Rⁿ ) has an inner product.

Euclidean topology

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Since Euclidean space is a metric space it is also a topological space with the natural topology induced by the metric. The metric topology on Eⁿ is called the Euclidean topology. A set is open in the Euclidean topology if and only if it contains an open ball around each of its points. The Euclidean topology turns out to be equivalent to the product topology on Rⁿ considered as a product of n copies of the real line R (with its standard topology).

An important result on the topology of Rⁿ, that is far from superficial, is Brouwer's invariance of domain. Any subset of Rⁿ (with its subspace topology) which is homeomorphic to another open subset of Rⁿ is itself open. An immediate consequence of this is that R^m is not homeomorphic to Rⁿ if m ≠ n — an intuitively "obvious" result which is nonetheless difficult to prove.

Euclidean n-space is the prototypical example of an n-manifold, in fact, a smooth manifold. For n ≠ 4, any differentiable n-manifold that is homeomorphic to Rⁿ is also diffeomorphic to it. The surprising fact that this is not also true for n = 4 was proved by Simon Donaldson in 1982; the counterexamples are called exotic (or fake) 4-spaces.

Euclidean space is also known as a linear manifold. An m-dimensional linear submanifold of Rⁿ is a Euclidean space of m dimensions embedded in it (as an affine subspace). For example, any straight line in some higher-dimensional Euclidean space is a 1-dimensional linear submanifold of that space.

See also

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• Euclidean geometry
• Euclidean distance
• Minkowski space

Euclidean geometry | Linear algebra | Topological spaces | Norm

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