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# 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 vector space.

The elements of topological vector spaces are typically functions, and the topology is often defined so as to capture a particular notion of convergence of sequences of functions.

Hilbert spaces and Banach spaces are well known examples.

 Contents

## Definition

A topological vector space X is a real or complex vector space which is endowed with a Hausdorff topology such that vector addition X × XX and scalar multiplication K × XX are continuous (where the product topologies are used and the base field K carries its standard topology).

## Examples

All normed vector spaces (and therefore all Banach spaces and Hilbert spaces) are examples of topological vector spaces.

#### Product vector spaces

A cartesian product of a family of topological vector spaces, when endowed with the product topology is a topological vector space. For instance, the set X of all functions f : RR. X can be identified with the product space RR and carries a natural product topology. With this topology, X becomes a topological vector space, called the space of pointwise convergence. The reason for this name is the following: if (fn) is a sequence of elements in X, then fn has limit f in X if and only if fn(x) has limit f(x) for every real number x. This space is complete, but not normable.

## Topological structure

A vector space is an abelian group with respect to the operation of addition, and in a topological vector space the inverse operation is always continuous (since it is the same as multiplication by -1). Hence, every topological vector space is a topological group. In particular, topological vector spaces are uniform spaces and one can thus talk about completeness, uniform convergence and uniform continuity. The vector space operations of addition and scalar multiplication are actually uniformly continuous. Because of this, every topological vector space can be completed and is thus a dense linear subspace of a complete topological vector space.

Vector addition and scalar multiplication are not only continuous but even homeomorphic which means we can construct a base for the topology and thus reconstruct the whole topology of the space from any local base around 0.

Every topological vector space has a local base of absorbing and balanced sets.

If a topological vector space is semi-metrisable, that is the topology can be given by a semi-metric, then the semi-metric must be translation invariant.

A linear function between two topological vector spaces which is continuous at one point is continuous on the whole domain.

A linear functional f on a topological vector space X is continuous if and only if kernel(f) is closed in X

A topological vector space is finite-dimensional if and only if it is locally compact, in which case it is isomorphic to a Euclidean space Rn or Cn (in the sense that there exists a linear homeomorphism between the two spaces).

## Types of topological vector spaces

We start the list with the most general classes and proceed to the "nicer" ones.

• Locally convex topological vector spaces: here each point has a local base consisting of convex sets. By a technique known as Minkowski functionals it can be shown that a space is locally convex if and only if its topology can be defined by a family of semi-norms. Local convexity is the minimum requirement for "geometrical" arguments.
• Barrelled spaces: locally convex spaces where the Banach-Steinhaus theorem holds.
• F-spaces are complete topological vector spaces with a translation-invariant metric. These include Lp spaces for all p > 0.
• Fréchet spaces: these are complete locally convex spaces where the topology comes from a translation-invariant metric, or equivalently: from a countable family of semi-norms. Many interesting spaces of functions fall into this class. A locally convex F-space is a Fréchet space.
• Normed spaces and semi-normed spaces: locally convex spaces where the topology can be described by a single norm or semi-norm. In normed spaces a linear operator is continuous if and only if it is bounded.
• Banach spaces: Complete normed vector spaces. Most of functional analysis is formulated for Banach spaces.
• Reflexive Banach spaces: Banach spaces naturally isomorphic to their double dual (see below), which ensures that some geometrical arguments can be carried out. An important example which is not reflexive is L1, whose dual is L but is strictly contained in the dual of L.
• Hilbert spaces: these have an inner product; even though these spaces may be infinite dimensional, most geometrical reasoning familiar from finite dimensions can be carried out in them.

## Dual space

Every topological vector space has a dual - the set V* of all continuous linear functionals, i.e. continuous linear maps from the space into the base field K. A topology on the dual can be defined to be the coarsest topology such that the dual pairing V* × V -> K is continuous. This turns the dual into a locally convex topological vector space. This topology is called the weak-* topology. This may not be the only natural topology on the dual space; for instance, the dual of a Banach space has a natural norm defined on it. However, it is very important in applications because of its compactness properties (see Banach-Alaoglu theorem).

## References

• A Grothendieck: Topological vector spaces, Gordon and Breach Science Publishers, New York, 1973.
• G Köthe: Topological vector spaces. Grundlehren der mathematischen Wissenschaften, Band 159, Springer-Verlag, New York, 1969.
• H H Schaefer: Topological vector spaces, Springer-Verlag, New York, 1971.
• F Trčves: Topological Vector Spaces, Distributions, and Kernels, Academic Press, 1967.
03-10-2013 05:06:04