Locally convex topological vector space

In functional analysis and related areas of mathematics, locally convex topological vector spaces or locally convex spaces are examples of topological vector spaces (TVS) which generalize normed spaces. They can be defined as topological vector spaces whose topology is generated by translations of balanced, absorbent, convex sets. Alternatively they can be defined as a vector space with a family of seminorms, and a topology can be defined in terms of that family. Although in general such spaces are not necessarily normable, the existence of a convex local base for the zero vector is strong enough for the Hahn–Banach theorem to hold, yielding a sufficiently rich theory of continuous linear functionals.
Fréchet spaces are locally convex spaces which are metrizable and complete with respect to this metric. They are generalizations of Banach spaces, which are complete vector spaces with respect to a norm.
Contents
Definition
Suppose V is a vector space over K, a subfield of the complex numbers (normally C itself or R). A locally convex space is defined either in terms of convex sets, or equivalently in terms of seminorms.
Convex sets
A subset C in V is called
 Convex if for each x and y in C, tx+(1–t)y is in C for all t in the unit interval, that is whenever 0 ≤ t ≤ 1. In other words, C contains all line segments between points in C.
 Circled if for all x in C, λx is in C if λ=1. If the underlying field K is the real numbers, this means that C is equal to its reflection through the origin. For a complex vector space V, it means for any x in C, C contains the circle through x, centred on the origin, in the onedimensional complex subspace generated by x.
 A cone (when the underlying field is ordered) if for every x in C and 0 ≤ λ ≤ 1, λx is in C.
 Balanced if for all x in C, λx is in C if λ ≤ 1. If the underlying field K is the real numbers, this means that if x is in C, C contains the line segment between x and x. For a complex vector space V, it means for any x in C, C contains the disk with x on its boundary, centred on the origin, in the onedimensional complex subspace generated by x. Equivalently, a balanced set is a circled cone.
 Absorbent or absorbing if the union of tC over all t > 0 is all of V, or equivalently for every x in V, tx is in C for some t > 0. The set C can be scaled out to absorb every point in the space.
 Absolutely convex if it is both balanced and convex.
A locally convex topological vector space is a topological vector space in which the origin has a local base of absolutely convex absorbent sets. Because translation is (by definition of "topological vector space") continuous, all translations are homeomorphisms, so every base for the neighborhoods of the origin can be translated to a base for the neighborhoods of any given vector.
Seminorms
A seminorm on V is a map p : V → R such that
 p is positive or positive semidefinite: p(x) ≥ 0.
 p is positive homogeneous or positive scalable: p(λx) = λ p(x) for every scalar λ. So, in particular, p(0) = 0.
 p is subadditive. It satisfies the triangle inequality: p(x + y) ≤ p(x) + p(y).
If p satisfies positive definiteness, which states that if p(x) = 0 then x = 0, then p is a norm. While in general seminorms need not be norms, there is an analogue of this criterion for families of seminorms, separatedness, defined below.
A locally convex space is then defined to be a vector space V along with a family of seminorms {p_{α}}_{α ∈ A} on V. The space carries a natural topology, the initial topology of the seminorms. In other words, it is the coarsest topology for which all mappings x → p_{α}(x−x_{0}), x_{0} ∈ V, α ∈ A, are continuous. A base of neighborhoods of x_{0} for this topology is obtained in the following way: for every finite subset B of A and every ε > 0, let
That the vector space operations are continuous in this topology follows from properties 2 and 3 above. The resulting TVS is locally convex because each U_{B,ε}(0) is absolutely convex and absorbent.
Equivalence of definitions
Although the definition in terms of a neighborhood base gives a better geometric picture, the definition in terms of seminorms is easier to work with in practice. The equivalence of the two definitions follows from a construction known as the Minkowski functional or Minkowski gauge. The key feature of seminorms which ensures the convexity of their εballs is the triangle inequality.
For an absorbing set C such that if x is in C, then tx is in C whenever 0 ≤ t ≤ 1, define the Minkowski functional of C to be
From this definition it follows that μ_{C} is a seminorm if C is balanced and convex (it is also absorbent by assumption). Conversely, given a family of seminorms, the sets
form a base of convex absorbent balanced sets.
Further definitions and properties
 A family of seminorms {p_{α}}_{α} is called total or separated or is said to separate points if whenever p_{α}(x) = 0 holds for every α then x is necessarily 0. A locally convex space is Hausdorff if and only if it has a separated family of seminorms. Many authors take the Hausdorff criterion in the definition.
 A pseudometric is a generalisation of a metric which does not satisfy the condition that d(x,y) = 0 only when x = y. A locally convex space is pseudometrisable, meaning that its topology arises from a pseudometric, if and only if it has a countable family of seminorms. Indeed, a pseudometric inducing the same topology is then given by

 As with any topological vector space, a locally convex space is also a uniform space. Thus one may speak of uniform continuity, uniform convergence, and Cauchy sequences.
 A Cauchy net in a locally convex space is a net {x_{κ}}_{κ} such that for every ε > 0 and every seminorm p_{α}, there exists a κ such that for every λ, μ > κ, p_{α}(x_{λ}–x_{μ}) < ε. In other words, the net must be Cauchy in all the seminorms simultaneously. The definition of completeness is given here in terms of nets instead of the more familiar sequences because unlike Fréchet spaces which are metrisable, general spaces may be defined by an uncountable family of pseudometrics. Sequences, which are countable by definition, cannot suffice to characterize convergence in such spaces. A locally convex space is complete if and only if every Cauchy net converges.
 A family of seminorms becomes a preordered set under the relation p_{α} ≤ p_{β} if and only if there exists an M > 0 such that for all x, p_{α}(x) ≤ Mp_{β}(x). One says it is a directed family of seminorms if the family is a directed set with addition as the join, in other words if for every α and β, there is a γ such that p_{α} + p_{β} ≤ p_{γ}. Every family of seminorms has an equivalent directed family, meaning one which defines the same topology. Indeed, given a family {p_{α}}_{α∈I}, let Φ be the set of finite subsets of I, then for every F in Φ, define q_{F}=∑_{α∈F} p_{α}. One may check that {q_{F}}_{F∈Φ} is an equivalent directed family.
 If the topology of the space is induced from a single seminorm, then the space is seminormable. Any locally convex space with a finite family of seminorms is seminormable. Moreover, if the space is Hausdorff (the family is separated), then the space is normable, with norm given by the sum of the seminorms. In terms of the open sets, a locally convex topological vector space is seminormable if and only if 0 has a bounded neighborhood.
Examples and nonexamples
Examples of locally convex spaces
 Every normed space is a Hausdorff locally convex space, and much of the theory of locally convex spaces generalises parts of the theory of normed spaces. The family of seminorms can be taken to be the single norm. Every Banach space is a complete Hausdorff locally convex space, in particular, the L^{p} spaces with p ≥ 1 are locally convex.
 More generally, every Fréchet space is locally convex. A Fréchet space can be defined as a complete locally convex space with a separated countable family of seminorms.
 The space R^{ω} of real valued sequences with the family of seminorms given by i ∈ N. The countable family of seminorms is complete and separable, so this is a Fréchet space, which is not normable. Note that this is also the limit topology of the spaces R^{n}, embedded in R^{ω} in the natural way, by completing finite sequences with infinitely many 0.
 Given any vector space V and a collection F of linear functionals on it, V can be made into a locally convex topological vector space by giving it the weakest topology making all linear functionals in F continuous. This is known as the weak topology or the initial topology determined by F. The collection F may be the algebraic dual of V or any other collection. The family of seminorms in this case is given by p_{ƒ}(x) = ƒ(x) for all ƒ in F.
 Spaces of differentiable functions give other nonnormable examples. Consider the space of smooth functions ƒ : R^{n} → C such that sup_{x} x^{a}D^{b}ƒ < ∞, where a and b are multiindices. The family of seminorms define by p_{a,b}(ƒ) = sup_{x} x^{a}D^{b}ƒ(x) is separated, and countable, and the space is complete, so this metrisable space is a Fréchet space. It is known as the Schwartz space, or the space of functions of rapid decrease, and its dual space is the space of tempered distributions.
 An important function space in functional analysis is the space D(U) of smooth functions with compact support in U ⊆ R^{n}. A more detailed construction is needed for the topology of this space because the space C^{∞}_{0}(U) is not complete in the uniform norm. The topology on D(U) is defined as follows: for any fixed compact set K ⊂ U, the space C^{∞}_{0}(K) of functions ƒ ∈ C^{∞}_{0}(U) with supp(ƒ) ⊂ K is a Fréchet space with countable family of seminorms ƒ_{m} = sup_{x} D^{m}ƒ(x) (these are actually bona fide norms, and the space C^{∞}_{0}(K) with the ·_{m} norm is a Banach space D^{m}(K)) . Given any collection {K_{λ}}_{λ} of compact sets, directed by inclusion and such that ∪_{λ}K_{λ} = U, then the C^{∞}_{0}(K_{λ}) form a direct system, and D(U) is defined to be the limit of this system. Such a limit of Fréchet spaces is known as an LF space. More concretely, D(U) is the union of all the C^{∞}_{0}(K_{λ}) with the final topology which makes each inclusion map C^{∞}_{0}(K_{λ})↪D(U) continuous. This space is locally convex and sequentially complete, but not complete (this means that all Cauchy sequences converge, but not necessarily more general Cauchy nets). The space is not metrisable, and so it is not a Fréchet space. The dual space of D(R^{n}) is the space of distributions on R^{n}.
 More abstractly, given a topological space X, the space C(X) of continuous (not necessarily bounded) functions on X can be given the topology of uniform convergence on compact sets. This topology is defined by seminorms φ_{K}(ƒ) = max { ƒ(x) : x ∈ K } (as K varies over the directed set of all compact subsets of X). When X is locally compact (e.g. an open set in R^{n}) the StoneWeierstrass theorem applies  in the case of realvalued functions, any subalgebra of C(X) that separates points and contains the constant functions (e.g., the subalgebra of polynomials) is dense.
Examples of spaces lacking local convexity
Many topological vector spaces are locally convex. Examples of spaces that lack local convexity include the following:
 The spaces L^{p}([0, 1]) for 0 < p < 1 are equipped with the Fnorm
they are not locally convex, since the only convex neighborhood of zero is the whole space. More generally the spaces L^{p}(μ) with an atomless, finite measure μ and 0 < p < 1 are not locally convex.
 The space of measurable functions on the unit interval [0, 1] (where we identify two functions that are equal almost everywhere) has a vectorspace topology defined by the translationinvariant metric (which induces the convergence in measure of measurable functions; for random variables, convergence in measure is convergence in probability). This space is often denoted L_{0}.
Both examples have the property that any continuous linear map to the real numbers is 0. In particular, their dual space is trivial, that is, it contains only the zero functional.
 The sequence space ℓ^{p}, 0 < p < 1, is not locally convex.
Continuous linear mappings
Because locally convex spaces are topological spaces as well as vector spaces, the natural functions to consider between two locally convex spaces are continuous linear maps. Using the seminorms, a necessary and sufficient criterion for the continuity of a linear map can be given that closely resembles the more familiar boundedness condition found for Banach spaces.
Given locally convex spaces V and W with families of seminorms {p_{α}}_{α} and {q_{β}}_{β} respectively, a linear map T from V to W is continuous if and only if for every β there exist α_{1}, α_{2}, ... , α_{n} and exists an M>0 such that for all x in X
In other words, each seminorm of the range of T is bounded above by some finite sum of seminorms in the domain. If the family {p_{α}}_{α} is a directed family, and it can always be chosen to be directed as explained above, then the formula becomes even simpler and more familiar:
The class of all locally convex topological vector spaces forms a category with continuous linear maps as morphisms.
References
 Conway, John (1990). A Course in Functional Analysis. Springer. ISBN 0387972455.
 Rudin, Walter (January 1991). Functional Analysis. McGrawHill Science/Engineering/Math. ISBN 0070542368.
See also
Categories: Topological vector spaces
Wikimedia Foundation. 2010.
Look at other dictionaries:
Topological vector space — In mathematics, a topological vector space is one of the basic structures investigated in functional analysis. As the name suggests the space blends a topological structure (a uniform structure to be precise) with the algebraic concept of a… … Wikipedia
Bounded set (topological vector space) — In functional analysis and related areas of mathematics, a set in a topological vector space is called bounded or von Neumann bounded, if every neighborhood of the zero vector can be inflated to include the set. Conversely a set which is not… … Wikipedia
Vector space — This article is about linear (vector) spaces. For the structure in incidence geometry, see Linear space (geometry). Vector addition and scalar multiplication: a vector v (blue) is added to another vector w (red, upper illustration). Below, w is… … Wikipedia
Normed vector space — In mathematics, with 2 or 3 dimensional vectors with real valued entries, the idea of the length of a vector is intuitive and can easily be extended to any real vector space Rn. The following properties of vector length are crucial. 1. The zero… … Wikipedia
Ordered vector space — A point x in R2 and the set of all y such that x≤y (in red). The order here is x≤y if and only if x1 ≤ y1 and x2 ≤ y2. In mathematics an ordered vector space or partially ordered vector space is a vector space equi … Wikipedia
Topological tensor product — In mathematics, there are usually many different ways to construct a topological tensor product of two topological vector spaces. For Hilbert spaces or nuclear spaces there is a simple well behaved theory of tensor products (see Tensor product of … Wikipedia
Space (mathematics) — This article is about mathematical structures called spaces. For space as a geometric concept, see Euclidean space. For all other uses, see space (disambiguation). A hierarchy of mathematical spaces: The inner product induces a norm. The norm… … Wikipedia
Nuclear space — In mathematics, a nuclear space is a topological vector space with many of the good properties of finite dimensional vector spaces. The topology on them can be defined by a family of seminorms whose unit balls decrease rapidly in size. Vector… … Wikipedia
Lp space — In mathematics, the Lp spaces are function spaces defined using a natural generalization of the p norm for finite dimensional vector spaces. They are sometimes called Lebesgue spaces, named after Henri Lebesgue (Dunford Schwartz 1958, III.3),… … Wikipedia
Barrelled space — In functional analysis and related areas of mathematics barrelled spaces are topological vector spaces where every barrelled set in the space is a neighbourhood for the zero vector. A barrelled set or a barrel in a topological vector space is a… … Wikipedia