## In mathematics::Dimension

### ::concepts

Space::''n'' Style::first Other::point Theory::title System::three Variety::extra**In mathematics**
In mathematics, the dimension of an object is an intrinsic property independent of the space in which the object is embedded. For example, a point on the unit circle in the plane can be specified by two Cartesian coordinates, but a single polar coordinate (the angle) would be sufficient, so the circle is 1-dimensional even though it exists in the 2-dimensional plane. This *intrinsic* notion of dimension is one of the chief ways the mathematical notion of dimension differs from its common usages.

The dimension of Euclidean *n*-space **E**^{n} is *n*. When trying to generalize to other types of spaces, one is faced with the question "what makes **E**^{n} *n*-dimensional?" One answer is that to cover a fixed ball in **E**^{n} by small balls of radius *ε*, one needs on the order of *ε*^{−n} such small balls. This observation leads to the definition of the Minkowski dimension and its more sophisticated variant, the Hausdorff dimension, but there are also other answers to that question. For example, the boundary of a ball in **E**^{n} looks locally like **E**^{n-1} and this leads to the notion of the inductive dimension. While these notions agree on **E**^{n} , they turn out to be different when one looks at more general spaces.

A tesseract is an example of a four-dimensional object. Whereas outside mathematics the use of the term "dimension" is as in: "A tesseract *has four dimensions*", mathematicians usually express this as: "The tesseract *has dimension 4*", or: "The dimension of the tesseract *is* 4".

Although the notion of higher dimensions goes back to René Descartes, substantial development of a higher-dimensional geometry only began in the 19th century, via the work of Arthur Cayley, William Rowan Hamilton, Ludwig Schläfli and Bernhard Riemann. Riemann's 1854 Habilitationsschrift, Schläfli's 1852 *Theorie der vielfachen Kontinuität*, Hamilton's 1843 discovery of the quaternions and the construction of the Cayley algebra marked the beginning of higher-dimensional geometry.

The rest of this section examines some of the more important mathematical definitions of the dimensions.

### Dimension of a vector space

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The dimension of a vector space is the number of vectors in any basis for the space, i.e. the number of coordinates necessary to specify any vector. This notion of dimension (the cardinality of a basis) is often referred to as the *Hamel dimension* or *algebraic dimension* to distinguish it from other notions of dimension.

### Manifolds

A connected topological manifold is locally homeomorphic to Euclidean *n*-space, and the number *n* is called the manifold's dimension. One can show that this yields a uniquely defined dimension for every connected topological manifold.

For connected differentiable manifolds, the dimension is also the dimension of the tangent vector space at any point.

In geometric topology, the theory of manifolds is characterized by the way dimensions 1 and 2 are relatively elementary, the **high-dimensional** cases *n* > 4 are simplified by having extra space in which to "work"; and the cases *n* = 3 and 4 are in some senses the most difficult. This state of affairs was highly marked in the various cases of the Poincaré conjecture, where four different proof methods are applied.

### Varieties

{{#invoke:main|main}} The dimension of an algebraic variety may be defined in various equivalent ways. The most intuitive way is probably the dimension of the tangent space at any regular point. Another intuitive way is to define the dimension as the number of hyperplanes that are needed in order to have an intersection with the variety that is reduced to a finite number of points (dimension zero). This definition is based on the fact that the intersection of a variety with a hyperplane reduces the dimension by one unless if the hyperplane contains the variety.

An algebraic set being a finite union of algebraic varieties, its dimension is the maximum of the dimensions of its components. It is equal to the maximal length of the chains <math>V_0\subsetneq V_1\subsetneq \ldots \subsetneq V_d</math> of sub-varieties of the given algebraic set (the length of such a chain is the number of "<math>\subsetneq</math>").

Each variety can be considered as an algebraic stack, and its dimension as variety agrees with its dimension as stack. There are however many stacks which do not correspond to varieties, and some of these have negative dimension. Specifically, if *V* is a variety of dimension *m* and *G* is an algebraic group of dimension *n* acting on *V*, then the quotient stack [*V*/*G*] has dimension *m*−*n*.<ref>{{#invoke:citation/CS1|citation
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### Krull dimension

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The Krull dimension of a commutative ring is the maximal length of chains of prime ideals in it, a chain of length *n* being a sequence <math>\mathcal{P}_0\subsetneq \mathcal{P}_1\subsetneq \ldots \subsetneq\mathcal{P}_n </math> of prime ideals related by inclusion. It is strongly related to the dimension of an algebraic variety, because of the natural correspondence between sub-varieties and prime ideals of the ring of the polynomials on the variety.

For an algebra over a field, the dimension as vector space is finite if and only if its Krull dimension is 0.

### Lebesgue covering dimension

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For any normal topological space *X*, the Lebesgue covering dimension of *X* is defined to be n if *n* is the smallest integer for which the following holds: any open cover has an open refinement (a second open cover where each element is a subset of an element in the first cover) such that no point is included in more than *n* + 1 elements. In this case dim *X* = *n*. For *X* a manifold, this coincides with the dimension mentioned above. If no such integer *n* exists, then the dimension of *X* is said to be infinite, and one writes dim *X* = ∞. Moreover, *X* has dimension −1, i.e. dim *X* = −1 if and only if *X* is empty. This definition of covering dimension can be extended from the class of normal spaces to all Tychonoff spaces merely by replacing the term "open" in the definition by the term "**functionally open**".

### Inductive dimension

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An inductive definition of dimension can be created as follows. Consider a discrete set of points (such as a finite collection of points) to be 0-dimensional. By dragging a 0-dimensional object in some direction, one obtains a 1-dimensional object. By dragging a 1-dimensional object in a *new direction*, one obtains a 2-dimensional object. In general one obtains an (*n* + 1)-dimensional object by dragging an *n*-dimensional object in a *new* direction.

The inductive dimension of a topological space may refer to the *small inductive dimension* or the *large inductive dimension*, and is based on the analogy that (*n* + 1)-dimensional balls have *n*-dimensional boundaries, permitting an inductive definition based on the dimension of the boundaries of open sets.

### Hausdorff dimension

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For structurally complicated sets, especially fractals, the Hausdorff dimension is useful. The Hausdorff dimension is defined for all metric spaces and, unlike the dimensions considered above, can also attain non-integer real values.<ref name="Hausdorff dimension">Fractal Dimension, Boston University Department of Mathematics and Statistics</ref> The box dimension or Minkowski dimension is a variant of the same idea. In general, there exist more definitions of fractal dimensions that work for highly irregular sets and attain non-integer positive real values. Fractals have been found useful to describe many natural objects and phenomena.<ref>{{#invoke:citation/CS1|citation
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### Hilbert spaces

Every Hilbert space admits an orthonormal basis, and any two such bases for a particular space have the same cardinality. This cardinality is called the dimension of the Hilbert space. This dimension is finite if and only if the space's Hamel dimension is finite, and in this case the above dimensions coincide.

**Dimension sections**

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