Real coordinate space

In mathematics, a real coordinate space of dimension , written ( ) or is a coordinate space over the real numbers. This means that it is the set of the -tuples of real numbers (sequences of real numbers). With component-wise addition and scalar multiplication, it is a real vector space.
Typically, the Cartesian coordinates of the elements of a Euclidean space form a real coordinate space. This explains the name of "coordinate space" and the fact that geometric terms are often used when working with coordinate spaces. For example, is a plane.
Coordinate spaces are widely used in geometry and physics, as their elements allow locating points in Euclidean spaces, and computing with them.
Definition and structures.
For any natural number , the set consists of all -tuples of real numbers (). It is called the "-dimensional real space" or the "real -space".
An element of is thus a -tuple, and is written
where each is a real number. So, in multivariable calculus, the domain of a function of several real variables and the codomain of a real vector valued function are subsets of for some .
The real -space has several further properties, notably:
These properties and structures of make it fundamental in almost all areas of mathematics and their application domains, such as statistics, probability theory, and many parts of physics.
The domain of a function of several variables.
Any function of real variables can be considered as a function on (that is, with as its domain). The use of the real -space, instead of several variables considered separately, can simplify notation and suggest reasonable definitions. Consider, for , a function composition of the following form:
where functions and are continuous. If
then is not necessarily continuous. Continuity is a stronger condition: the continuity of in the natural topology (discussed below), also called "multivariable continuity", which is sufficient for continuity of the composition .
Vector space.
The coordinate space forms an -dimensional vector space over the field of real numbers with the addition of the structure of linearity, and is often still denoted . The operations on as a vector space are typically defined by
The zero vector is given by
and the additive inverse of the vector is given by
This structure is important because any -dimensional real vector space is isomorphic to the vector space .
Matrix notation.
In standard matrix notation, each element of is typically written as a column vector
and sometimes as a row vector:
The coordinate space may then be interpreted as the space of all column vectors, or all row vectors with the ordinary matrix operations of addition and scalar multiplication.
Linear transformations from to may then be written as matrices which act on the elements of via left multiplication (when the elements of are column vectors) and on elements of via right multiplication (when they are row vectors). The formula for left multiplication, a special case of matrix multiplication, is:
Any linear transformation is a continuous function (see below). Also, a matrix defines an open map from to if and only if the rank of the matrix equals to .
Standard basis.
The coordinate space comes with a standard basis:
To see that this is a basis, note that an arbitrary vector in can be written uniquely in the form
Geometric properties and uses.
Orientation.
The fact that real numbers, unlike many other fields, constitute an ordered field yields an orientation structure on . Any full-rank linear map of to itself either preserves or reverses orientation of the space depending on the sign of the determinant of its matrix. If one permutes coordinates (or, in other words, elements of the basis), the resulting orientation will depend on the parity of the permutation.
Diffeomorphisms of or domains in it, by their virtue to avoid zero Jacobian, are also classified to orientation-preserving and orientation-reversing. It has important consequences for the theory of differential forms, whose applications include electrodynamics.
Another manifestation of this structure is that the point reflection in has different properties depending on evenness of. For even it preserves orientation, while for odd it is reversed (see also improper rotation).
Affine space.
 understood as an affine space is the same space, where as a vector space acts by translations. Conversely, a vector has to be understood as a "difference between two points", usually illustrated by a directed line segment connecting two points. The distinction says that there is no canonical choice of where the origin should go in an affine -space, because it can be translated anywhere.
Convexity.
In a real vector space, such as , one can define a convex cone, which contains all "non-negative" linear combinations of its vectors. Corresponding concept in an affine space is a convex set, which allows only convex combinations (non-negative linear combinations that sum to 1).
In the language of universal algebra, a vector space is an algebra over the universal vector space of finite sequences of coefficients, corresponding to finite sums of vectors, while an affine space is an algebra over the universal affine hyperplane in this space (of finite sequences summing to 1), a cone is an algebra over the universal orthant (of finite sequences of nonnegative numbers), and a convex set is an algebra over the universal simplex (of finite sequences of nonnegative numbers summing to 1). This geometrizes the axioms in terms of "sums with (possible) restrictions on the coordinates".
Another concept from convex analysis is a convex function from to real numbers, which is defined through an inequality between its value on a convex combination of points and sum of values in those points with the same coefficients.
Euclidean space.
The dot product
defines the norm on the vector space . If every vector has its Euclidean norm, then for any pair of points the distance
is defined, providing a metric space structure on in addition to its affine structure.
As for vector space structure, the dot product and Euclidean distance usually are assumed to exist in without special explanations. However, the real -space and a Euclidean -space are distinct objects, strictly speaking. Any Euclidean -space has a coordinate system where the dot product and Euclidean distance have the form shown above, called "Cartesian". But there are "many" Cartesian coordinate systems on a Euclidean space.
Conversely, the above formula for the Euclidean metric defines the "standard" Euclidean structure on , but it is not the only possible one. Actually, any positive-definite quadratic form defines its own "distance" , but it is not very different from the Euclidean one in the sense that
Such a change of the metric preserves some of its properties, for example the property of being a complete metric space.
This also implies that any full-rank linear transformation of , or its affine transformation, does not magnify distances more than by some fixed , and does not make distances smaller than times, a fixed finite number times smaller.
The aforementioned equivalence of metric functions remains valid if is replaced with , where is any convex positive homogeneous function of degree 1, i.e. a vector norm (see Minkowski distance for useful examples). Because of this fact that any "natural" metric on is not especially different from the Euclidean metric, is not always distinguished from a Euclidean -space even in professional mathematical works.
In algebraic and differential geometry.
Although the definition of a manifold does not require that its model space should be , this choice is the most common, and almost exclusive one in differential geometry.
On the other hand, Whitney embedding theorems state that any real differentiable -dimensional manifold can be embedded into .
Other appearances.
Other structures considered on include the one of a pseudo-Euclidean space, symplectic structure (even ), and contact structure (odd ). All these structures, although can be defined in a coordinate-free manner, admit standard (and reasonably simple) forms in coordinates.
 is also a real vector subspace of which is invariant to complex conjugation; see also complexification.
Polytopes in R"n".
There are three families of polytopes which have simple representations in spaces, for any , and can be used to visualize any affine coordinate system in a real -space. Vertices of a hypercube have coordinates where each takes on one of only two values, typically 0 or 1. However, any two numbers can be chosen instead of 0 and 1, for example and 1. An -hypercube can be thought of as the Cartesian product of identical intervals (such as the unit interval ) on the real line. As an -dimensional subset it can be described with a system of inequalities:
Each vertex of the cross-polytope has, for some , the coordinate equal to ±1 and all other coordinates equal to 0 (such that it is the th standard basis vector up to sign). This is a dual polytope of hypercube. As an -dimensional subset it can be described with a single inequality which uses the absolute value operation:
but this can be expressed with a system of linear inequalities as well.
The third polytope with simply enumerable coordinates is the standard simplex, whose vertices are standard basis vectors and the origin . As an -dimensional subset it is described with a system of linear inequalities:
Replacement of all "≤" with "<" gives interiors of these polytopes.
Topological properties.
The topological structure of (called standard topology, Euclidean topology, or usual topology) can be obtained not only from Cartesian product. It is also identical to the natural topology induced by Euclidean metric discussed above: a set is open in the Euclidean topology if and only if it contains an open ball around each of its points. Also, is a linear topological space (see continuity of linear maps above), and there is only one possible (non-trivial) topology compatible with its linear structure. As there are many open linear maps from to itself which are not isometries, there can be many Euclidean structures on which correspond to the same topology. Actually, it does not depend much even on the linear structure: there are many non-linear diffeomorphisms (and other homeomorphisms) of onto itself, or its parts such as a Euclidean open ball or the interior of a hypercube).
 has the topological dimension .
An important result on the topology of , that is far from superficial, is Brouwer's invariance of domain. Any subset of (with its subspace topology) that is homeomorphic to another open subset of is itself open. An immediate consequence of this is that is not homeomorphic to if – an intuitively "obvious" result which is nonetheless difficult to prove.
Despite the difference in topological dimension, and contrary to a naïve perception, it is possible to map a lesser-dimensional real space continuously and surjectively onto . A continuous (although not smooth) space-filling curve (an image of ) is possible.
Examples.
"n" ≤ 1.
Cases of do not offer anything new: is the real line, whereas (the space containing the empty column vector) is a singleton, understood as a zero vector space. However, it is useful to include these as trivial cases of theories that describe different .
"n" = 4.
 can be imagined using the fact that points , where each is either 0 or 1, are vertices of a tesseract (pictured), the 4-hypercube (see above).
The first major use of is a spacetime model: three spatial coordinates plus one temporal. This is usually associated with theory of relativity, although four dimensions were used for such models since Galilei. The choice of theory leads to different structure, though: in Galilean relativity the coordinate is privileged, but in Einsteinian relativity it is not. Special relativity is set in Minkowski space. General relativity uses curved spaces, which may be thought of as with a curved metric for most practical purposes. None of these structures provide a (positive-definite) metric on .
Euclidean also attracts the attention of mathematicians, for example due to its relation to quaternions, a 4-dimensional real algebra themselves. See rotations in 4-dimensional Euclidean space for some information.
In differential geometry, is the only case where admits a non-standard differential structure: see exotic R4.
Norms on.
One could define many norms on the vector space . Some common examples are
A really surprising and helpful result is that every norm defined on is equivalent. This means for two arbitrary norms formula_25 and formula_26 on you can always find positive real numbers formula_27, such that
formula_28 for all formula_29.
This defines an equivalence relation on the set of all norms on . With this result you can check that a sequence of vectors in converges with formula_25 if and only if it converges with formula_26.
Here is a sketch of what a proof of this result may look like:
Because of the equivalence relation it is enough to show that every norm on is equivalent to the Euclidean norm formula_32. Let formula_25 be an arbitrary norm on . The proof is divided in two steps: