Seminorm

In mathematics, particularly in functional analysis, a seminorm is a vector space norm that need not be positive definite. Seminorms are intimately connected with convex sets: every seminorm is the Minkowski functional of some absorbing disk and, conversely, the Minkowski functional of any such set is a seminorm. A topological vector space is locally convex if and only if its topology is induced by a family of seminorms.

## Contents

* 1 Definition
* 2 Examples
* 3 Minkowski functionals and seminorms
* 4 Algebraic properties
* 4.1 Relationship to other norm-like concepts
* 4.2 Inequalities involving seminorms
* 4.3 Hahn–Banach theorem for seminorms
* 5 Topologies of seminormed spaces
* 5.1 Pseudometrics and the induced topology
* 5.1.1 Stronger, weaker, and equivalent seminorms
* 5.2 Normability and seminormability
* 5.3 Topological properties
* 5.4 Continuity of seminorms
* 5.5 Continuity of linear maps
* 6 Generalizations
* 7 See also
* 8 Notes
* 9 References
* 10 External links

## Definition

Let [math]\displaystyle{ X }[/math] be a vector space over either the real numbers [math]\displaystyle{ \R }[/math] or the complex numbers [math]\displaystyle{ \Complex. }[/math] A real-valued function [math]\displaystyle{ p : X \to \R }[/math] is called a seminorm if it satisfies the following two conditions:

1. Subadditivity[1]/Triangle inequality: [math]\displaystyle{ p(x + y) \leq p(x) + p(y) }[/math] for all [math]\displaystyle{ x, y \in X. }[/math]
2. Absolute homogeneity:[1] [math]\displaystyle{ p(s x) =|s|p(x) }[/math] for all [math]\displaystyle{ x \in X }[/math] and all scalars [math]\displaystyle{ s. }[/math]

These two conditions imply that [math]\displaystyle{ p(0) = 0 }[/math][proof 1] and that every seminorm [math]\displaystyle{ p }[/math] also has the following property:[proof 2]

3. Nonnegativity:[1] [math]\displaystyle{ p(x) \geq 0 }[/math] for all [math]\displaystyle{ x \in X. }[/math]

Some authors include non-negativity as part of the definition of "seminorm" (and also sometimes of "norm"), although this is not necessary since it follows from the other two properties.

By definition, a norm on [math]\displaystyle{ X }[/math] is a seminorm that also separates points, meaning that it has the following additional property:

4. Positive definite/Positive[1]/Point-separating: whenever [math]\displaystyle{ x \in X }[/math] satisfies [math]\displaystyle{ p(x) = 0, }[/math] then [math]\displaystyle{ x = 0. }[/math]

A seminormed space is a pair [math]\displaystyle{ (X, p) }[/math] consisting of a vector space [math]\displaystyle{ X }[/math] and a seminorm [math]\displaystyle{ p }[/math] on [math]\displaystyle{ X. }[/math] If the seminorm [math]\displaystyle{ p }[/math] is also a norm then the seminormed space [math]\displaystyle{ (X, p) }[/math] is called a normed space.

Since absolute homogeneity implies positive homogeneity, every seminorm is a type of function called a sublinear function. A map [math]\displaystyle{ p : X \to \R }[/math] is called a sublinear function if it is subadditive and positive homogeneous. Unlike a seminorm, a sublinear function is not necessarily nonnegative. Sublinear functions are often encountered in the context of the Hahn–Banach theorem. A real-valued function [math]\displaystyle{ p : X \to \R }[/math] is a seminorm if and only if it is a sublinear and balanced function.

## Examples

* The trivial seminorm on [math]\displaystyle{ X, }[/math] which refers to the constant [math]\displaystyle{ 0 }[/math] map on [math]\displaystyle{ X, }[/math] induces the indiscrete topology on [math]\displaystyle{ X. }[/math]
* Let [math]\displaystyle{ \mu }[/math] be a measure on a space [math]\displaystyle{ \Omega }[/math]. For an arbitrary constant [math]\displaystyle{ c \geq 1 }[/math], let [math]\displaystyle{ X }[/math] be the set of all functions [math]\displaystyle{ f: \Omega \rightarrow \mathbb{R} }[/math] for which [math]\displaystyle{ \lVert f \rVert_c := \left( \int_{\Omega}| f |^c \, d\mu \right)^{1/c} }[/math] exists and is finite. It can be shown that [math]\displaystyle{ X }[/math] is a vector space, and the functional [math]\displaystyle{ \lVert \cdot \rVert_c }[/math] is a seminorm on [math]\displaystyle{ X }[/math]. However, it is not always a norm (e.g. if [math]\displaystyle{ \Omega = \mathbb{R} }[/math] and [math]\displaystyle{ \mu }[/math] is the Lebesgue measure) because [math]\displaystyle{ \lVert h \rVert_c = 0 }[/math] does not always imply [math]\displaystyle{ h = 0 }[/math]. To make [math]\displaystyle{ \lVert \cdot \rVert_c }[/math] a norm, quotient [math]\displaystyle{ X }[/math] by the closed subspace of functions [math]\displaystyle{ h }[/math] with [math]\displaystyle{ \lVert h \rVert_c = 0 }[/math]. The resulting space, [math]\displaystyle{ L^c(\mu) }[/math], has a norm induced by [math]\displaystyle{ \lVert \cdot \rVert_c }[/math].
* If [math]\displaystyle{ f }[/math] is any linear form on a vector space then its absolute value [math]\displaystyle{ |f|, }[/math] defined by [math]\displaystyle{ x \mapsto |f(x)|, }[/math] is a seminorm.
* A sublinear function [math]\displaystyle{ f : X \to \R }[/math] on a real vector space [math]\displaystyle{ X }[/math] is a seminorm if and only if it is a symmetric function, meaning that [math]\displaystyle{ f(-x) = f(x) }[/math] for all [math]\displaystyle{ x \in X. }[/math]
* Every real-valued sublinear function [math]\displaystyle{ f : X \to \R }[/math] on a real vector space [math]\displaystyle{ X }[/math] induces a seminorm [math]\displaystyle{ p : X \to \R }[/math] defined by [math]\displaystyle{ p(x) := \max \\{f(x), f(-x)\\}. }[/math][2]
* Any finite sum of seminorms is a seminorm. The restriction of a seminorm (respectively, norm) to a vector subspace is once again a seminorm (respectively, norm).
* If [math]\displaystyle{ p : X \to \R }[/math] and [math]\displaystyle{ q : Y \to \R }[/math] are seminorms (respectively, norms) on [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] then the map [math]\displaystyle{ r : X \times Y \to \R }[/math] defined by [math]\displaystyle{ r(x, y) = p(x) + q(y) }[/math] is a seminorm (respectively, a norm) on [math]\displaystyle{ X \times Y. }[/math] In particular, the maps on [math]\displaystyle{ X \times Y }[/math] defined by [math]\displaystyle{ (x, y) \mapsto p(x) }[/math] and [math]\displaystyle{ (x, y) \mapsto q(y) }[/math] are both seminorms on [math]\displaystyle{ X \times Y. }[/math]
* If [math]\displaystyle{ p }[/math] and [math]\displaystyle{ q }[/math] are seminorms on [math]\displaystyle{ X }[/math] then so are[3] [math]\displaystyle{ (p \vee q)(x) = \max \\{p(x), q(x)\\} }[/math] and [math]\displaystyle{ (p \wedge q)(x) := \inf \\{p(y) + q(z) : x = y + z \text{ with } y, z \in X\\} }[/math] where [math]\displaystyle{ p \wedge q \leq p }[/math] and [math]\displaystyle{ p \wedge q \leq q. }[/math][4]
* The space of seminorms on [math]\displaystyle{ X }[/math] is generally not a distributive lattice with respect to the above operations. For example, over [math]\displaystyle{ \R^2 }[/math], [math]\displaystyle{ p(x, y) := \max(|x|, |y|), q(x, y) := 2|x|, r(x, y) := 2|y| }[/math] are such that [math]\displaystyle{ ((p \vee q) \wedge (p \vee r)) (x, y) = \inf \\{\max(2|x_1|, |y_1|) + \max(|x_2|, 2|y_2|) : x = x_1 + x_2 \text{ and } y = y_1 + y_2\\} }[/math] while [math]\displaystyle{ (p \vee q \wedge r) (x, y) := \max(|x|, |y|) }[/math]
* If [math]\displaystyle{ L : X \to Y }[/math] is a linear map and [math]\displaystyle{ q : Y \to \R }[/math] is a seminorm on [math]\displaystyle{ Y, }[/math] then [math]\displaystyle{ q \circ L : X \to \R }[/math] is a seminorm on [math]\displaystyle{ X. }[/math] The seminorm [math]\displaystyle{ q \circ L }[/math] will be a norm on [math]\displaystyle{ X }[/math] if and only if [math]\displaystyle{ L }[/math] is injective and the restriction [math]\displaystyle{ q\big\vert_{L(X)} }[/math] is a norm on [math]\displaystyle{ L(X). }[/math]

## Minkowski functionals and seminorms

Main page: Minkowski functional

Seminorms on a vector space [math]\displaystyle{ X }[/math] are intimately tied, via Minkowski functionals, to subsets of [math]\displaystyle{ X }[/math] that are convex, balanced, and absorbing. Given such a subset [math]\displaystyle{ D }[/math] of [math]\displaystyle{ X, }[/math] the Minkowski functional of [math]\displaystyle{ D }[/math] is a seminorm. Conversely, given a seminorm [math]\displaystyle{ p }[/math] on [math]\displaystyle{ X, }[/math] the sets[math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} }[/math] and [math]\displaystyle{ \\{x \in X : p(x) \leq 1\\} }[/math] are convex, balanced, and absorbing and furthermore, the Minkowski functional of these two sets (as well as of any set lying "in between them") is [math]\displaystyle{ p. }[/math][5]

## Algebraic properties

Every seminorm is a sublinear function, and thus satisfies all properties of a sublinear function, including convexity, [math]\displaystyle{ p(0) = 0, }[/math] and for all vectors [math]\displaystyle{ x, y \in X }[/math]: the reverse triangle inequality: [6][7] [math]\displaystyle{ |p(x) - p(y)| \leq p(x - y) }[/math] and also [math]\displaystyle{ 0 \leq \max \\{p(x), p(-x)\\} }[/math] and [math]\displaystyle{ p(x) - p(y) \leq p(x - y). }[/math][6][7]

For any vector [math]\displaystyle{ x \in X }[/math] and positive real [math]\displaystyle{ r \gt 0: }[/math][8] [math]\displaystyle{ x + \\{y \in X : p(y) \lt r\\} = \\{y \in X : p(x - y) \lt r\\} }[/math] and furthermore, [math]\displaystyle{ \\{x \in X : p(x) \lt r\\} }[/math] is an absorbing disk in [math]\displaystyle{ X. }[/math][3]

If [math]\displaystyle{ p }[/math] is a sublinear function on a real vector space [math]\displaystyle{ X }[/math] then there exists a linear functional [math]\displaystyle{ f }[/math] on [math]\displaystyle{ X }[/math] such that [math]\displaystyle{ f \leq p }[/math][7] and furthermore, for any linear functional [math]\displaystyle{ g }[/math] on [math]\displaystyle{ X, }[/math] [math]\displaystyle{ g \leq p }[/math] on [math]\displaystyle{ X }[/math] if and only if [math]\displaystyle{ g^{-1}(1) \cap \\{x \in X : p(x) \lt 1 = \varnothing\\}. }[/math][7]

Other properties of seminorms

Every seminorm is a balanced function. A seminorm [math]\displaystyle{ p }[/math] is a norm on [math]\displaystyle{ X }[/math] if and only if [math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} }[/math] does not contain a non-trivial vector subspace.

If [math]\displaystyle{ p : X \to [0, \infty) }[/math] is a seminorm on [math]\displaystyle{ X }[/math] then [math]\displaystyle{ \ker p := p^{-1}(0) }[/math] is a vector subspace of [math]\displaystyle{ X }[/math] and for every [math]\displaystyle{ x \in X, }[/math] [math]\displaystyle{ p }[/math] is constant on the set [math]\displaystyle{ x + \ker p = \\{x + k : p(k) = 0\\} }[/math] and equal to [math]\displaystyle{ p(x). }[/math][proof 3]

Furthermore, for any real [math]\displaystyle{ r \gt 0, }[/math][3] [math]\displaystyle{ r \\{x \in X : p(x) \lt 1\\} = \\{x \in X : p(x) \lt r\\} = \left\\{x \in X : \tfrac{1}{r} p(x) \lt 1 \right\\}. }[/math]

If [math]\displaystyle{ D }[/math] is a set satisfying [math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} \subseteq D \subseteq \\{x \in X : p(x) \leq 1\\} }[/math] then [math]\displaystyle{ D }[/math] is absorbing in [math]\displaystyle{ X }[/math] and [math]\displaystyle{ p = p_D }[/math] where [math]\displaystyle{ p_D }[/math] denotes the Minkowski functional associated with [math]\displaystyle{ D }[/math] (that is, the gauge of [math]\displaystyle{ D }[/math]).[5] In particular, if [math]\displaystyle{ D }[/math] is as above and [math]\displaystyle{ q }[/math] is any seminorm on [math]\displaystyle{ X, }[/math] then [math]\displaystyle{ q = p }[/math] if and only if [math]\displaystyle{ \\{x \in X : q(x) \lt 1\\} \subseteq D \subseteq \\{x \in X : q(x) \leq\\}. }[/math][5]

If [math]\displaystyle{ (X, \|\,\cdot\,\|) }[/math] is a normed space and [math]\displaystyle{ x, y \in X }[/math] then [math]\displaystyle{ \|x - y\| = \|x - z\| + \|z - y\| }[/math] for all [math]\displaystyle{ z }[/math] in the interval [math]\displaystyle{ [x, y]. }[/math][9]

Every norm is a convex function and consequently, finding a global maximum of a norm-based objective function is sometimes tractable.

### Relationship to other norm-like concepts

Let [math]\displaystyle{ p : X \to \R }[/math] be a non-negative function. The following are equivalent:

1. [math]\displaystyle{ p }[/math] is a seminorm.
2. [math]\displaystyle{ p }[/math] is a convex [math]\displaystyle{ F }[/math]-seminorm.
3. [math]\displaystyle{ p }[/math] is a convex balanced G-seminorm.[10]

If any of the above conditions hold, then the following are equivalent:

1. [math]\displaystyle{ p }[/math] is a norm;
2. [math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} }[/math] does not contain a non-trivial vector subspace.[11]
3. There exists a norm on [math]\displaystyle{ X, }[/math] with respect to which, [math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} }[/math] is bounded.

If [math]\displaystyle{ p }[/math] is a sublinear function on a real vector space [math]\displaystyle{ X }[/math] then the following are equivalent:[7]

1. [math]\displaystyle{ p }[/math] is a linear functional;
2. [math]\displaystyle{ p(x) + p(-x) \leq 0 \text{ for every } x \in X }[/math];
3. [math]\displaystyle{ p(x) + p(-x) = 0 \text{ for every } x \in X }[/math];

### Inequalities involving seminorms

If [math]\displaystyle{ p, q : X \to [0, \infty) }[/math] are seminorms on [math]\displaystyle{ X }[/math] then:

* [math]\displaystyle{ p \leq q }[/math] if and only if [math]\displaystyle{ q(x) \leq 1 }[/math] implies [math]\displaystyle{ p(x) \leq 1. }[/math][12]
* If [math]\displaystyle{ a \gt 0 }[/math] and [math]\displaystyle{ b \gt 0 }[/math] are such that [math]\displaystyle{ p(x) \lt a }[/math] implies [math]\displaystyle{ q(x) \leq b, }[/math] then [math]\displaystyle{ a q(x) \leq b p(x) }[/math] for all [math]\displaystyle{ x \in X. }[/math] [13]
* Suppose [math]\displaystyle{ a }[/math] and [math]\displaystyle{ b }[/math] are positive real numbers and [math]\displaystyle{ q, p_1, \ldots, p_n }[/math] are seminorms on [math]\displaystyle{ X }[/math] such that for every [math]\displaystyle{ x \in X, }[/math] if [math]\displaystyle{ \max \\{p_1(x), \ldots, p_n(x)\\} \lt a }[/math] then [math]\displaystyle{ q(x) \lt b. }[/math] Then [math]\displaystyle{ a q \leq b \left(p_1 + \cdots + p_n\right). }[/math][11]
* If [math]\displaystyle{ X }[/math] is a vector space over the reals and [math]\displaystyle{ f }[/math] is a non-zero linear functional on [math]\displaystyle{ X, }[/math] then [math]\displaystyle{ f \leq p }[/math] if and only if [math]\displaystyle{ \varnothing = f^{-1}(1) \cap \\{x \in X : p(x) \lt 1\\}. }[/math][12]

If [math]\displaystyle{ p }[/math] is a seminorm on [math]\displaystyle{ X }[/math] and [math]\displaystyle{ f }[/math] is a linear functional on [math]\displaystyle{ X }[/math] then:

* [math]\displaystyle{ |f| \leq p }[/math] on [math]\displaystyle{ X }[/math] if and only if [math]\displaystyle{ \operatorname{Re} f \leq p }[/math] on [math]\displaystyle{ X }[/math] (see footnote for proof).[14][15]
* [math]\displaystyle{ f \leq p }[/math] on [math]\displaystyle{ X }[/math] if and only if [math]\displaystyle{ f^{-1}(1) \cap \\{x \in X : p(x) \lt 1 = \varnothing\\}. }[/math][7][12]
* If [math]\displaystyle{ a \gt 0 }[/math] and [math]\displaystyle{ b \gt 0 }[/math] are such that [math]\displaystyle{ p(x) \lt a }[/math] implies [math]\displaystyle{ f(x) \neq b, }[/math] then [math]\displaystyle{ a |f(x)| \leq b p(x) }[/math] for all [math]\displaystyle{ x \in X. }[/math][13]

### Hahn–Banach theorem for seminorms

Seminorms offer a particularly clean formulation of the Hahn–Banach theorem:

If [math]\displaystyle{ M }[/math] is a vector subspace of a seminormed space [math]\displaystyle{ (X, p) }[/math] and if [math]\displaystyle{ f }[/math] is a continuous linear functional on [math]\displaystyle{ M, }[/math] then [math]\displaystyle{ f }[/math] may be extended to a continuous linear functional [math]\displaystyle{ F }[/math] on [math]\displaystyle{ X }[/math] that has the same norm as [math]\displaystyle{ f. }[/math][16]

A similar extension property also holds for seminorms:

Theorem[17][13] (Extending seminorms) -- If [math]\displaystyle{ M }[/math] is a vector subspace of [math]\displaystyle{ X, }[/math] [math]\displaystyle{ p }[/math] is a seminorm on [math]\displaystyle{ M, }[/math] and [math]\displaystyle{ q }[/math] is a seminorm on [math]\displaystyle{ X }[/math] such that [math]\displaystyle{ p \leq q\big\vert_M, }[/math] then there exists a seminorm [math]\displaystyle{ P }[/math] on [math]\displaystyle{ X }[/math] such that [math]\displaystyle{ P\big\vert_M = p }[/math] and [math]\displaystyle{ P \leq q. }[/math]

Proof: Let [math]\displaystyle{ S }[/math] be the convex hull of [math]\displaystyle{ \\{m \in M : p(m) \leq 1\\} \cup \\{x \in X : q(x) \leq 1\\}. }[/math] Then [math]\displaystyle{ S }[/math] is an absorbing disk in [math]\displaystyle{ X }[/math] and so the Minkowski functional [math]\displaystyle{ P }[/math] of [math]\displaystyle{ S }[/math] is a seminorm on [math]\displaystyle{ X. }[/math] This seminorm satisfies [math]\displaystyle{ p = P }[/math] on [math]\displaystyle{ M }[/math] and [math]\displaystyle{ P \leq q }[/math] on [math]\displaystyle{ X. }[/math] [math]\displaystyle{ \blacksquare }[/math]

## Topologies of seminormed spaces

### Pseudometrics and the induced topology

A seminorm [math]\displaystyle{ p }[/math] on [math]\displaystyle{ X }[/math] induces a topology, called the seminorm-induced topology, via the canonical translation-invariant pseudometric [math]\displaystyle{ d_p : X \times X \to \R }[/math]; [math]\displaystyle{ d_p(x, y) := p(x - y) = p(y - x). }[/math] This topology is Hausdorff if and only if [math]\displaystyle{ d_p }[/math] is a metric, which occurs if and only if [math]\displaystyle{ p }[/math] is a norm.[4] This topology makes [math]\displaystyle{ X }[/math] into a locally convex pseudometrizable topological vector space that has a bounded neighborhood of the origin and a neighborhood basis at the origin consisting of the following open balls (or the closed balls) centered at the origin: [math]\displaystyle{ \\{x \in X : p(x) \lt r\\} \quad \text{ or } \quad \\{x \in X : p(x) \leq r\\} }[/math] as [math]\displaystyle{ r \gt 0 }[/math] ranges over the positive reals. Every seminormed space [math]\displaystyle{ (X, p) }[/math] should be assumed to be endowed with this topology unless indicated otherwise. A topological vector space whose topology is induced by some seminorm is called seminormable.

Equivalently, every vector space [math]\displaystyle{ X }[/math] with seminorm [math]\displaystyle{ p }[/math] induces a vector space quotient [math]\displaystyle{ X / W, }[/math] where [math]\displaystyle{ W }[/math] is the subspace of [math]\displaystyle{ X }[/math] consisting of all vectors [math]\displaystyle{ x \in X }[/math] with [math]\displaystyle{ p(x) = 0. }[/math] Then [math]\displaystyle{ X / W }[/math] carries a norm defined by [math]\displaystyle{ p(x + W) = p(v). }[/math] The resulting topology, pulled back to [math]\displaystyle{ X, }[/math] is precisely the topology induced by [math]\displaystyle{ p. }[/math]

Any seminorm-induced topology makes [math]\displaystyle{ X }[/math] locally convex, as follows. If [math]\displaystyle{ p }[/math] is a seminorm on [math]\displaystyle{ X }[/math] and [math]\displaystyle{ r \in \R, }[/math] call the set [math]\displaystyle{ \\{x \in X : p(x) \lt r\\} }[/math] the open ball of radius [math]\displaystyle{ r }[/math] about the origin; likewise the closed ball of radius [math]\displaystyle{ r }[/math] is [math]\displaystyle{ \\{x \in X : p(x) \leq r\\}. }[/math] The set of all open (resp. closed) [math]\displaystyle{ p }[/math]-balls at the origin forms a neighborhood basis of convex balanced sets that are open (resp. closed) in the [math]\displaystyle{ p }[/math]-topology on [math]\displaystyle{ X. }[/math]

#### Stronger, weaker, and equivalent seminorms

The notions of stronger and weaker seminorms are akin to the notions of stronger and weaker norms. If [math]\displaystyle{ p }[/math] and [math]\displaystyle{ q }[/math] are seminorms on [math]\displaystyle{ X, }[/math] then we say that [math]\displaystyle{ q }[/math] is stronger than [math]\displaystyle{ p }[/math] and that [math]\displaystyle{ p }[/math] is weaker than [math]\displaystyle{ q }[/math] if any of the following equivalent conditions holds:

1. The topology on [math]\displaystyle{ X }[/math] induced by [math]\displaystyle{ q }[/math] is finer than the topology induced by [math]\displaystyle{ p. }[/math]
2. If [math]\displaystyle{ x_{\bull} = \left(x_i\right)_{i=1}^{\infty} }[/math] is a sequence in [math]\displaystyle{ X, }[/math] then [math]\displaystyle{ q\left(x_{\bull}\right) := \left(q\left(x_i\right)\right)_{i=1}^{\infty} \to 0 }[/math] in [math]\displaystyle{ \R }[/math] implies [math]\displaystyle{ p\left(x_{\bull}\right) \to 0 }[/math] in [math]\displaystyle{ \R. }[/math][4]
3. If [math]\displaystyle{ x_{\bull} = \left(x_i\right)_{i \in I} }[/math] is a net in [math]\displaystyle{ X, }[/math] then [math]\displaystyle{ q\left(x_{\bull}\right) := \left(q\left(x_i\right)\right)_{i \in I} \to 0 }[/math] in [math]\displaystyle{ \R }[/math] implies [math]\displaystyle{ p\left(x_{\bull}\right) \to 0 }[/math] in [math]\displaystyle{ \R. }[/math]
4. [math]\displaystyle{ p }[/math] is bounded on [math]\displaystyle{ \\{x \in X : q(x) \lt 1\\}. }[/math][4]
5. If [math]\displaystyle{ \inf{} \\{q(x) : p(x) = 1, x \in X\\} = 0 }[/math] then [math]\displaystyle{ p(x) = 0 }[/math] for all [math]\displaystyle{ x \in X. }[/math][4]
6. There exists a real [math]\displaystyle{ K \gt 0 }[/math] such that [math]\displaystyle{ p \leq K q }[/math] on [math]\displaystyle{ X. }[/math][4]

The seminorms [math]\displaystyle{ p }[/math] and [math]\displaystyle{ q }[/math] are called equivalent if they are both weaker (or both stronger) than each other. This happens if they satisfy any of the following conditions:

1. The topology on [math]\displaystyle{ X }[/math] induced by [math]\displaystyle{ q }[/math] is the same as the topology induced by [math]\displaystyle{ p. }[/math]
2. [math]\displaystyle{ q }[/math] is stronger than [math]\displaystyle{ p }[/math] and [math]\displaystyle{ p }[/math] is stronger than [math]\displaystyle{ q. }[/math][4]
3. If [math]\displaystyle{ x_{\bull} = \left(x_i\right)_{i=1}^{\infty} }[/math] is a sequence in [math]\displaystyle{ X }[/math] then [math]\displaystyle{ q\left(x_{\bull}\right) := \left(q\left(x_i\right)\right)_{i=1}^{\infty} \to 0 }[/math] if and only if [math]\displaystyle{ p\left(x_{\bull}\right) \to 0. }[/math]
4. There exist positive real numbers [math]\displaystyle{ r \gt 0 }[/math] and [math]\displaystyle{ R \gt 0 }[/math] such that [math]\displaystyle{ r q \leq p \leq R q. }[/math]

### Normability and seminormability

A topological vector space (TVS) is said to be a seminormable space (respectively, a normable space) if its topology is induced by a single seminorm (resp. a single norm). A TVS is normable if and only if it is seminormable and Hausdorff or equivalently, if and only if it is seminormable and T1 (because a TVS is Hausdorff if and only if it is a T1 space). A locally bounded topological vector space is a topological vector space that possesses a bounded neighborhood of the origin.

Normability of topological vector spaces is characterized by Kolmogorov's normability criterion. A TVS is seminormable if and only if it has a convex bounded neighborhood of the origin.[18] Thus a locally convex TVS is seminormable if and only if it has a non-empty bounded open set.[19] A TVS is normable if and only if it is a T1 space and admits a bounded convex neighborhood of the origin.

If [math]\displaystyle{ X }[/math] is a Hausdorff locally convex TVS then the following are equivalent:

1. [math]\displaystyle{ X }[/math] is normable.
2. [math]\displaystyle{ X }[/math] is seminormable.
3. [math]\displaystyle{ X }[/math] has a bounded neighborhood of the origin.
4. The strong dual [math]\displaystyle{ X^{\prime}_b }[/math] of [math]\displaystyle{ X }[/math] is normable.[20]
5. The strong dual [math]\displaystyle{ X^{\prime}_b }[/math] of [math]\displaystyle{ X }[/math] is metrizable.[20]

Furthermore, [math]\displaystyle{ X }[/math] is finite dimensional if and only if [math]\displaystyle{ X^{\prime}_{\sigma} }[/math] is normable (here [math]\displaystyle{ X^{\prime}_{\sigma} }[/math] denotes [math]\displaystyle{ X^{\prime} }[/math] endowed with the weak-* topology).

The product of infinitely many seminormable space is again seminormable if and only if all but finitely many of these spaces trivial (that is, 0-dimensional).[21]

### Topological properties

* If [math]\displaystyle{ X }[/math] is a TVS and [math]\displaystyle{ p }[/math] is a continuous seminorm on [math]\displaystyle{ X, }[/math] then the closure of [math]\displaystyle{ \\{x \in X : p(x) \lt r\\} }[/math] in [math]\displaystyle{ X }[/math] is equal to [math]\displaystyle{ \\{x \in X : p(x) \leq r\\}. }[/math][3]
* The closure of [math]\displaystyle{ \\{0\\} }[/math] in a locally convex space [math]\displaystyle{ X }[/math] whose topology is defined by a family of continuous seminorms [math]\displaystyle{ \mathcal{P} }[/math] is equal to [math]\displaystyle{ \bigcap_{p \in \mathcal{P}} p^{-1}(0). }[/math][22]
* A subset [math]\displaystyle{ S }[/math] in a seminormed space [math]\displaystyle{ (X, p) }[/math] is bounded if and only if [math]\displaystyle{ p(S) }[/math] is bounded.[23]
* If [math]\displaystyle{ (X, p) }[/math] is a seminormed space then the locally convex topology that [math]\displaystyle{ p }[/math] induces on [math]\displaystyle{ X }[/math] makes [math]\displaystyle{ X }[/math] into a pseudometrizable TVS with a canonical pseudometric given by [math]\displaystyle{ d(x, y) := p(x - y) }[/math] for all [math]\displaystyle{ x, y \in X. }[/math][24]
* The product of infinitely many seminormable spaces is again seminormable if and only if all but finitely many of these spaces are trivial (that is, 0-dimensional).[21]

### Continuity of seminorms

If [math]\displaystyle{ p }[/math] is a seminorm on a topological vector space [math]\displaystyle{ X, }[/math] then the following are equivalent:[5]

1. [math]\displaystyle{ p }[/math] is continuous.
2. [math]\displaystyle{ p }[/math] is continuous at 0;[3]
3. [math]\displaystyle{ \\{x \in X : p(x) \lt 1\\} }[/math] is open in [math]\displaystyle{ X }[/math];[3]
4. [math]\displaystyle{ \\{x \in X : p(x) \leq 1\\} }[/math] is closed neighborhood of 0 in [math]\displaystyle{ X }[/math];[3]
5. [math]\displaystyle{ p }[/math] is uniformly continuous on [math]\displaystyle{ X }[/math];[3]
6. There exists a continuous seminorm [math]\displaystyle{ q }[/math] on [math]\displaystyle{ X }[/math] such that [math]\displaystyle{ p \leq q. }[/math][3]

In particular, if [math]\displaystyle{ (X, p) }[/math] is a seminormed space then a seminorm [math]\displaystyle{ q }[/math] on [math]\displaystyle{ X }[/math] is continuous if and only if [math]\displaystyle{ q }[/math] is dominated by a positive scalar multiple of [math]\displaystyle{ p. }[/math][3]

If [math]\displaystyle{ X }[/math] is a real TVS, [math]\displaystyle{ f }[/math] is a linear functional on [math]\displaystyle{ X, }[/math] and [math]\displaystyle{ p }[/math] is a continuous seminorm (or more generally, a sublinear function) on [math]\displaystyle{ X, }[/math] then [math]\displaystyle{ f \leq p }[/math] on [math]\displaystyle{ X }[/math] implies that [math]\displaystyle{ f }[/math] is continuous.[7]

### Continuity of linear maps

If [math]\displaystyle{ F : (X, p) \to (Y, q) }[/math] is a map between seminormed spaces then let[16] [math]\displaystyle{ \|F\|_{p,q} := \sup \\{q(F(x)) : p(x) \leq 1, x \in X\\}. }[/math]

If [math]\displaystyle{ F : (X, p) \to (Y, q) }[/math] is a linear map between seminormed spaces then the following are equivalent:

1. [math]\displaystyle{ F }[/math] is continuous;
2. [math]\displaystyle{ \|F\|_{p,q} \lt \infty }[/math];[16]
3. There exists a real [math]\displaystyle{ K \geq 0 }[/math] such that [math]\displaystyle{ p \leq K q }[/math];[16]
* In this case, [math]\displaystyle{ \|F\|_{p,q} \leq K. }[/math]

If [math]\displaystyle{ F }[/math] is continuous then [math]\displaystyle{ q(F(x)) \leq \|F\|_{p,q} p(x) }[/math] for all [math]\displaystyle{ x \in X. }[/math][16]

The space of all continuous linear maps [math]\displaystyle{ F : (X, p) \to (Y, q) }[/math] between seminormed spaces is itself a seminormed space under the seminorm [math]\displaystyle{ \|F\|_{p,q}. }[/math] This seminorm is a norm if [math]\displaystyle{ q }[/math] is a norm.[16]

## Generalizations

The concept of norm in composition algebras does not share the usual properties of a norm.

A composition algebra [math]\displaystyle{ (A, *, N) }[/math] consists of an algebra over a field [math]\displaystyle{ A, }[/math] an involution [math]\displaystyle{ \,*, }[/math] and a quadratic form [math]\displaystyle{ N, }[/math] which is called the "norm". In several cases [math]\displaystyle{ N }[/math] is an isotropic quadratic form so that [math]\displaystyle{ A }[/math] has at least one null vector, contrary to the separation of points required for the usual norm discussed in this article.

An ultraseminorm or a non-Archimedean seminorm is a seminorm [math]\displaystyle{ p : X \to \R }[/math] that also satisfies [math]\displaystyle{ p(x + y) \leq \max \\{p(x), p(y)\\} \text{ for all } x, y \in X. }[/math]

Weakening subadditivity: Quasi-seminorms

A map [math]\displaystyle{ p : X \to \R }[/math] is called a quasi-seminorm if it is (absolutely) homogeneous and there exists some [math]\displaystyle{ b \leq 1 }[/math] such that [math]\displaystyle{ p(x + y) \leq b p(p(x) + p(y)) \text{ for all } x, y \in X. }[/math] The smallest value of [math]\displaystyle{ b }[/math] for which this holds is called the multiplier of [math]\displaystyle{ p. }[/math]

A quasi-seminorm that separates points is called a quasi-norm on [math]\displaystyle{ X. }[/math]

Weakening homogeneity - [math]\displaystyle{ k }[/math]-seminorms

A map [math]\displaystyle{ p : X \to \R }[/math] is called a [math]\displaystyle{ k }[/math]-seminorm if it is subadditive and there exists a [math]\displaystyle{ k }[/math] such that [math]\displaystyle{ 0 \lt k \leq 1 }[/math] and for all [math]\displaystyle{ x \in X }[/math] and scalars [math]\displaystyle{ s, }[/math][math]\displaystyle{ p(s x) = |s|^k p(x) }[/math] A [math]\displaystyle{ k }[/math]-seminorm that separates points is called a [math]\displaystyle{ k }[/math]-norm on [math]\displaystyle{ X. }[/math]

We have the following relationship between quasi-seminorms and [math]\displaystyle{ k }[/math]-seminorms:

Suppose that [math]\displaystyle{ q }[/math] is a quasi-seminorm on a vector space [math]\displaystyle{ X }[/math] with multiplier [math]\displaystyle{ b. }[/math] If [math]\displaystyle{ 0 \lt \sqrt{k} \lt \log_2 b }[/math] then there exists [math]\displaystyle{ k }[/math]-seminorm [math]\displaystyle{ p }[/math] on [math]\displaystyle{ X }[/math] equivalent to [math]\displaystyle{ q. }[/math]

## See also

* Asymmetric norm - Generalization of the concept of a norm
* Banach space - Normed vector space that is complete
* Contraction mapping - Function reducing distance between all points
* Gowers norm
* Locally convex topological vector space - A vector space with a topology defined by convex open sets
* Mahalanobis distance - Statistical distance measure
* Matrix norm - Norm on a vector space of matrices
* Minkowski functional - Function made from a set
* Norm (mathematics) - Length in a vector space
* Normed vector space - Vector space on which a distance is defined
* Sublinear function - Type of function in linear algebra

## Notes

Proofs

1. ↑ If [math]\displaystyle{ z \in X }[/math] denotes the zero vector in [math]\displaystyle{ X }[/math] while [math]\displaystyle{ 0 }[/math] denote the zero scalar, then absolute homogeneity implies that [math]\displaystyle{ p(0) = p(0 z) = |0|p(z) = 0 p(z) = 0. }[/math] [math]\displaystyle{ \blacksquare }[/math]
2. ↑ Suppose [math]\displaystyle{ p : X \to \R }[/math] is a seminorm and let [math]\displaystyle{ x \in X. }[/math] Then absolute homogeneity implies [math]\displaystyle{ p(-x) = p((-1) x) =|-1|p(x) = p(x). }[/math] The triangle inequality now implies [math]\displaystyle{ p(0) = p(x + (- x)) \leq p(x) + p(-x) = p(x) + p(x) = 2 p(x). }[/math] Because [math]\displaystyle{ x }[/math] was an arbitrary vector in [math]\displaystyle{ X, }[/math] it follows that [math]\displaystyle{ p(0) \leq 2 p(0), }[/math] which implies that [math]\displaystyle{ 0 \leq p(0) }[/math] (by subtracting [math]\displaystyle{ p(0) }[/math] from both sides). Thus [math]\displaystyle{ 0 \leq p(0) \leq 2 p(x) }[/math] which implies [math]\displaystyle{ 0 \leq p(x) }[/math] (by multiplying thru by [math]\displaystyle{ 1/2 }[/math]).
3. ↑ Let [math]\displaystyle{ x \in X }[/math] and [math]\displaystyle{ k \in p^{-1}(0). }[/math] It remains to show that [math]\displaystyle{ p(x + k) = p(x). }[/math] The triangle inequality implies [math]\displaystyle{ p(x + k) \leq p(x) + p(k) = p(x) + 0 = p(x). }[/math] Since [math]\displaystyle{ p(-k) = 0, }[/math] [math]\displaystyle{ p(x) = p(x) - p(-k) \leq p(x - (-k)) = p(x + k), }[/math] as desired. [math]\displaystyle{ \blacksquare }[/math]

## References

1. ↑ 1.0 1.1 1.2 1.3 Kubrusly 2011, p. 200.
2. ↑ Narici & Beckenstein 2011, pp. 120–121.
3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 Narici & Beckenstein 2011, pp. 116–128.
4. ↑ 4.0 4.1 4.2 4.3 4.4 4.5 4.6 Wilansky 2013, pp. 15-21.
5. ↑ 5.0 5.1 5.2 5.3 Schaefer & Wolff 1999, p. 40.
6. ↑ 6.0 6.1 Narici & Beckenstein 2011, pp. 120-121.
7. ↑ 7.0 7.1 7.2 7.3 7.4 7.5 7.6 Narici & Beckenstein 2011, pp. 177-220.
8. ↑ Narici & Beckenstein 2011, pp. 116−128.
9. ↑ Narici & Beckenstein 2011, pp. 107-113.
10. ↑ Schechter 1996, p. 691.
11. ↑ 11.0 11.1 Narici & Beckenstein 2011, p. 149.
12. ↑ 12.0 12.1 12.2 Narici & Beckenstein 2011, pp. 149–153.
13. ↑ 13.0 13.1 13.2 Wilansky 2013, pp. 18-21.
14. ↑ Obvious if [math]\displaystyle{ X }[/math] is a real vector space. For the non-trivial direction, assume that [math]\displaystyle{ \operatorname{Re} f \leq p }[/math] on [math]\displaystyle{ X }[/math] and let [math]\displaystyle{ x \in X. }[/math] Let [math]\displaystyle{ r \geq 0 }[/math] and [math]\displaystyle{ t }[/math] be real numbers such that [math]\displaystyle{ f(x) = r e^{i t}. }[/math] Then [math]\displaystyle{ |f(x)|= r = f\left(e^{-it} x\right) = \operatorname{Re}\left(f\left(e^{-it} x\right)\right) \leq p\left(e^{-it} x\right) = p(x). }[/math]
15. ↑ Wilansky 2013, p. 20.
16. ↑ 16.0 16.1 16.2 16.3 16.4 16.5 Wilansky 2013, pp. 21-26.
17. ↑ Narici & Beckenstein 2011, pp. 150.
18. ↑ Wilansky 2013, pp. 50-51.
19. ↑ Narici & Beckenstein 2011, pp. 156-175.
20. ↑ 20.0 20.1 Trèves 2006, pp. 136–149, 195–201, 240–252, 335–390, 420–433.
21. ↑ 21.0 21.1 Narici & Beckenstein 2011, pp. 156–175.
22. ↑ Narici & Beckenstein 2011, pp. 149-153.
23. ↑ Wilansky 2013, pp. 49-50.
24. ↑ Narici & Beckenstein 2011, pp. 115-154.

* Adasch, Norbert; Ernst, Bruno; Keim, Dieter (1978). Topological Vector Spaces: The Theory Without Convexity Conditions. Lecture Notes in Mathematics. {3834. Berlin New York: Springer-Verlag. ISBN 978-3-540-08662-8. OCLC 297140003.
* Jarchow, Hans (1981). Locally convex spaces. Stuttgart: B.G. Teubner. ISBN 978-3-519-02224-4. OCLC 8210342.
* Bourbaki, Nicolas (1987). Topological Vector Spaces: Chapters 1–5. Éléments de mathématique. 2. Berlin New York: Springer-Verlag. ISBN 978-3-540-42338-6. OCLC 17499190. http://www.numdam.org/item?id=AIF_1950__2__5_0.
* Conway, John (1990). A course in functional analysis. Graduate Texts in Mathematics. 96 (2nd ed.). New York: Springer-Verlag. ISBN 978-0-387-97245-9. OCLC 21195908.
* Edwards, Robert E. (Jan 1, 1995). Functional Analysis: Theory and Applications. New York: Dover Publications. ISBN 978-0-486-68143-6. OCLC 30593138.
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* Jarchow, Hans (1981). Locally convex spaces. Stuttgart: B.G. Teubner. ISBN 978-3-519-02224-4. OCLC 8210342.
* Khaleelulla, S. M. (July 1, 1982). written at Berlin Heidelberg. Counterexamples in Topological Vector Spaces. Lecture Notes in Mathematics. 936. Berlin New York: Springer-Verlag. ISBN 978-3-540-11565-6. OCLC 8588370.
* Köthe, Gottfried (1969). Topological Vector Spaces I. Grundlehren der mathematischen Wissenschaften. 159. New York: Springer Science & Business Media. ISBN 978-3-642-64988-2. OCLC 840293704.
* Template:Kubrusly The Elements of Operator Theory 2nd Edition 2011
* Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834.
* Prugovečki, Eduard (1981). Quantum mechanics in Hilbert space (2nd ed.). Academic Press. p. 20. ISBN 0-12-566060-X.
* Schaefer, Helmut H.; Wolff, Manfred P. (1999). Topological Vector Spaces. GTM. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135.
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