Normed vector space

In mathematics, a normed vector space or normed space is a vector space over the real or complex numbers, on which a norm is defined.[1] A norm is the formalization and the generalization to real vector spaces of the intuitive notion of "length" in the real world. A norm is a real-valued function defined on the vector space that is commonly denoted and has the following properties:

  1. It is nonnegative, that is for every vector x, one has
  2. It is positive on nonzero vectors, that is,
  3. For every vector x, and every scalar one has
  4. The triangle inequality holds; that is, for every vectors x and y, one has
Hierarchy of mathematical spaces. Normed vector spaces are a superset of inner product spaces and a subset of metric spaces, which in turn is a subset of topological vector space.

A norm induces a distance by the formula

which make the normed vector space into a metric space and a topological vector space. If this metric is complete then the normed space is called a Banach space. Every normed vector space can be "uniquely extended" to a Banach space, which makes normed spaces intimately related to Banach spaces. The study of normed spaces and Banach spaces is a fundamental part of functional analysis, which is a major subfield of mathematics.

An inner product space becomes a normed space when the norm of a vector is the square root of the inner product of the vector by itself. The Euclidean distance in a Euclidean space is related to the norm of the associated vector space (which is an inner product space) by the formula

DefinitionEdit

A normed vector space is a pair   where   is a vector space and   a norm on  .

A seminormed vector space is a pair   where   is a vector space and   a seminorm on  .

We often omit   or   and just write   for a space if it is clear from the context what (semi) norm we are using.

In a more general sense, a vector norm can be taken to be any real-valued function[clarification needed] that satisfies the three properties above.

A useful variation of the triangle inequality is

  for any vectors x and y.

This also shows that a vector norm is a continuous function.

Note that property 2 depends on a choice of norm   on the field of scalars. When the scalar field is   (or more generally a subset of  ), this is usually taken to be the ordinary absolute value, but other choices are possible. For example, for a vector space over   one could take   to be the p-adic norm, which gives rise to a different class of normed vector spaces.

Topological structureEdit

If (V, ‖·‖) is a normed vector space, the norm ‖·‖ induces a metric (a notion of distance) and therefore a topology on V. This metric is defined in the natural way: the distance between two vectors u and v is given by ‖u − v‖. This topology is precisely the weakest topology which makes ‖·‖ continuous and which is compatible with the linear structure of V in the following sense:

  1. The vector addition + : V × VV is jointly continuous with respect to this topology. This follows directly from the triangle inequality.
  2. The scalar multiplication · : K × V → V, where K is the underlying scalar field of V, is jointly continuous. This follows from the triangle inequality and homogeneity of the norm.

Similarly, for any semi-normed vector space we can define the distance between two vectors u and v as ‖u − v‖. This turns the seminormed space into a pseudometric space (notice this is weaker than a metric) and allows the definition of notions such as continuity and convergence. To put it more abstractly every semi-normed vector space is a topological vector space and thus carries a topological structure which is induced by the semi-norm.

Of special interest are complete normed spaces called Banach spaces. Every normed vector space V sits as a dense subspace inside a Banach space; this Banach space is essentially uniquely defined by V and is called the completion of V.

Two norms on the same vector space are called equivalent if they define the same topology. On a finite-dimensional vector space, all norms are equivalent but this is not true for infinite dimensional vector spaces.

All norms on a finite-dimensional vector space are equivalent from a topological viewpoint as they induce the same topology (although the resulting metric spaces need not be the same).[2] And since any Euclidean space is complete, we can thus conclude that all finite-dimensional normed vector spaces are Banach spaces. A normed vector space V is locally compact if and only if the unit ball B = {x : ‖x‖ ≤ 1} is compact, which is the case if and only if V is finite-dimensional; this is a consequence of Riesz's lemma. (In fact, a more general result is true: a topological vector space is locally compact if and only if it is finite-dimensional. The point here is that we don't assume the topology comes from a norm.)

The topology of a seminormed vector space has many nice properties. Given a neighbourhood system   around 0 we can construct all other neighbourhood systems as

 

with

 .

Moreover, there exists a neighbourhood basis for 0 consisting of absorbing and convex sets. As this property is very useful in functional analysis, generalizations of normed vector spaces with this property are studied under the name locally convex spaces.

Normable spacesEdit

A topological vector space   is called normable if there exists a norm   on X such that the canonical metric   induces the topology   on X. The following theorem is due to Kolmagoroff:[3]

Theorem A Hausdorff topological vector space is normable if and only if there exists a convex, von Neumann bounded neighborhood of  .

A product of a family of normable spaces is normable if and only if only finitely many of the spaces are non-trivial (i.e.  ).[3] Furthermore, the quotient of a normable space X by a closed vector subspace C is normable and if in addition X's topology is given by a norm   then the map   given by   is a well defined norm on X/C that induces the quotient topology on X/C.[4]

If X is a Hausdorff locally convex topological vector space then the following are equivalent:

  1. X is normable.
  2. X has a bounded neighborhood of the origin.
  3. the strong dual   of X is normable.[5]
  4. the strong dual   of X is metrizable.[5]

Furthermore, X is finite dimensional if and only if   is normable (here   denotes   endowed with the weak-* topology).

Linear maps and dual spacesEdit

The most important maps between two normed vector spaces are the continuous linear maps. Together with these maps, normed vector spaces form a category.

The norm is a continuous function on its vector space. All linear maps between finite dimensional vector spaces are also continuous.

An isometry between two normed vector spaces is a linear map f which preserves the norm (meaning ‖f(v)‖ = ‖v‖ for all vectors v). Isometries are always continuous and injective. A surjective isometry between the normed vector spaces V and W is called an isometric isomorphism, and V and W are called isometrically isomorphic. Isometrically isomorphic normed vector spaces are identical for all practical purposes.

When speaking of normed vector spaces, we augment the notion of dual space to take the norm into account. The dual V ' of a normed vector space V is the space of all continuous linear maps from V to the base field (the complexes or the reals) — such linear maps are called "functionals". The norm of a functional φ is defined as the supremum of |φ(v)| where v ranges over all unit vectors (i.e. vectors of norm 1) in V. This turns V ' into a normed vector space. An important theorem about continuous linear functionals on normed vector spaces is the Hahn–Banach theorem.

Normed spaces as quotient spaces of seminormed spacesEdit

The definition of many normed spaces (in particular, Banach spaces) involves a seminorm defined on a vector space and then the normed space is defined as the quotient space by the subspace of elements of seminorm zero. For instance, with the Lp spaces, the function defined by

 

is a seminorm on the vector space of all functions on which the Lebesgue integral on the right hand side is defined and finite. However, the seminorm is equal to zero for any function supported on a set of Lebesgue measure zero. These functions form a subspace which we "quotient out", making them equivalent to the zero function.

Finite product spacesEdit

Given n seminormed spaces Xi with seminorms qi we can define the product space as

 

with vector addition defined as

 

and scalar multiplication defined as

 .

We define a new function q

 

for example as

 .

which is a seminorm on X. The function q is a norm if and only if all qi are norms.

More generally, for each real p≥1 we have the seminorm:

 

For each p this defines the same topological space.

A straightforward argument involving elementary linear algebra shows that the only finite-dimensional seminormed spaces are those arising as the product space of a normed space and a space with trivial seminorm. Consequently, many of the more interesting examples and applications of seminormed spaces occur for infinite-dimensional vector spaces.

See alsoEdit

ReferencesEdit

  1. ^ Callier, Frank M. (1991). Linear System Theory. New York: Springer-Verlag. ISBN 0-387-97573-X.
  2. ^ Kedlaya, Kiran S. (2010), p-adic differential equations, Cambridge Studies in Advanced Mathematics, 125, Cambridge University Press, CiteSeerX 10.1.1.165.270, ISBN 978-0-521-76879-5, Theorem 1.3.6
  3. ^ a b Schaefer 1999, p. 41.
  4. ^ Schaefer 1999, p. 42.
  5. ^ a b Trèves 2006, pp. 136–149, 195–201, 240–252, 335–390, 420–433.

BibliographyEdit

External linksEdit