On an abelian group

For (G,+)(G,+) an abelian group, then a norm on the group is a function

||:G {\vert-\vert} \;\colon\; G \longrightarrow \mathbb{R}

to the real numbers, such that

  1. (positivity) (g0)(|g|>0)(g \neq 0) \Rightarrow (\vert g\vert \gt 0)

  2. (triangle inequality) |g+h||g|+|h|{\vert g + h\vert}\leq {\vert g\vert} + {\vert h\vert}

  3. (linearity) |kg|=|k||g|{\vert k g\vert} = {\vert k\vert} {\vert g\vert} for all kk \in \mathbb{Z}.

Here |k|{\vert k\vert} \in \mathbb{N} denotes the absolute value.

A group with a norm is a normed group, see there for more.

On a vector space

For kk a field equipped with a valuation (most usually, a local field such as \mathbb{R}, \mathbb{C}, or a p-adic completion of a number field), a norm on a kk-vector space VV is a function

||:V {\vert-\vert} \colon V \to \mathbb{R}

such that for all λk\lambda \in k, v,wVv,w \in V we have

  1. |λv|=|λ||v|{\vert \lambda v \vert} = {\vert \lambda\vert} {\vert v \vert} (where |λ|\vert \lambda \vert denotes the valuation)

  2. |v+w||v|+|w|{\vert v + w\vert } \leq {\vert v \vert } + {\vert w \vert} (“triangle inequality”)

  3. if |v|=0{\vert v\vert} = 0 then v=0v = 0.

If the third property is not required, one speaks of a seminorm.

If the triangle identity is strengthened to

  • |v+w|max(|v|,|w|){\vert v + w\vert } \leq max ({\vert v\vert}, {\vert w\vert})

one speaks of a non-archimedean seminorm, otherwise of an archimedean one.

A vector space equipped with a norm is a normed vector space. A vector of norm 1 is a unit vector.

Each seminorm determines a topology, which is Hausdorff precisely if it is a norm.

A topological vector space is called (semi-)normed if its topology can be induced by a (semi-)norm.

Two seminorms || 1{\vert - \vert}_1 and || 2{\vert - \vert}_2 are called equivalent if there are 0<C,C0 \lt C, C' \in \mathbb{R} such that for all vv we have

C|v| 1|v| 2C|v| 1. C {\vert v \vert}_1 \leq {\vert v \vert}_2 \leq C' {\vert v \vert}_1 \,.

Equivalent seminorms determine the same topology.

The collection of (bounded) multiplicative seminorms on a (Banach) ring is called its analytic spectrum (see there for details).



  • The standard absolute value is a norm on the real numbers.

  • More generally, on any Cartesian space n\mathbb{R}^n the Euclidean norm is given by

    |x|i=1n(x i) 2. \vert \vec x\vert \;\coloneqq\; \sqrt{ \underoverset{i = 1}{n}{\sum} (x_i)^2 } \,.
  1. more generally, for nn \in \mathbb{N}, and pp \in \mathbb{N}, p1p \geq 1, then the Cartesian space n\mathbb{R}^n carries the p-norm

    |x| p i|x i| pp {\vert \vec x \vert}_p \coloneqq \root p {\sum_i {|x_i|^p}}
  2. The p-norm generalizes to sequence spaces and Lebesgue spaces.

Minkowski Functionals

Let VV be a vector space and BVB \subseteq V an absorbing absolutely convex subset. The Minkowski functional of BB is the function μ B:V\mu_B \colon V \to \mathbb{R} defined by:

μ B(v)=inf{t>0:vtB} \mu_B(v) = \inf\{t \gt 0 : v \in t B\}

This is a semi-norm on VV.


The (open or closed) unit ball of a seminormed vector space is a convex set, a balanced set and an absorbing set. The first two of these properties make the unit ball (or even any ball of positive radius) an absolutely convex set.


In dream mathematics, a given real vector space (with no topological structure) can have at most one complete norm, up to topological equivalence (homeomorphism of the identity function). It can have multiple inequivalent complete seminorms and incomplete norms, but their Hausdorff quotients and completions must be different. For example, the various Lebesgue norms on a Cartesian space n\mathbb{R}^n for finite nn are complete and equivalent; on \mathbb{R}^\infty, they are inequivalent but incomplete.

As dream mathematics includes excluded middle and dependent choice, the existence of inequivalent complete norms on a given vector space cannot be proved without a stronger form of the axiom of choice, enough to disprove the Baire property (which is the only classically false axiom needed in the proof of uniqueness). In HAF, it is argued that this explains why, in applied mathematics, there tends to be only one norm considered on any particular vector space (after Hausdorff completion).

This theorem applies more generally to F-norms but not to G-norms (even on a real vector space).

algebraic structuregroupringfieldvector spacealgebra
(submultiplicative) normnormed groupnormed ringnormed fieldnormed vector spacenormed algebra
multiplicative norm (absolute value/valuation)valued field
completenesscomplete normed groupBanach ringcomplete fieldBanach vector spaceBanach algebra


Last revised on October 13, 2021 at 12:55:22. See the history of this page for a list of all contributions to it.