A norm on a VV over F=R\mathbb F=\mathbb R or C\mathbb C is a function ∥⋅∥:V→[0,∞)\|\cdot\|:V\to[0,\infty) such that for all u,v∈Vu,v\in V and a∈Fa\in\mathbb{F}:

∥v∥=0  ⟺  v=0,∥av∥=∣a∣ ∥v∥,∥u+v∥≤∥u∥+∥v∥.\|v\|=0\iff v=0,\qquad \|a v\|=|a|\,\|v\|,\qquad \|u+v\|\le \|u\|+\|v\|.

Here ∣a∣|a| denotes the for real scalars and for complex scalars.

Remarks

A norm induces a by d(u,v)=∥u−v∥d(u,v)=\|u-v\|, making VV into a and thus giving notions of convergence and continuity.

Examples

The norm records length, while the induced metric records separation: d(u,v)=∥u−v∥d(u,v)=\|u-v\|. Different norms can give different lengths on the same vector space, even when they induce the same topology in finite dimensions.

  • On Rn\mathbb{R}^n, the Euclidean norm ∥x∥2=∑i=1nxi2\|x\|_2=\sqrt{\sum_{i=1}^n x_i^2} is a norm.
  • On Rn\mathbb{R}^n, the ℓ1\ell^1 norm ∥x∥1=∑i=1n∣xi∣\|x\|_1=\sum_{i=1}^n |x_i| and the max norm ∥x∥∞=max⁡i∣xi∣\|x\|_\infty=\max_i |x_i| are norms.
  • On continuous functions on [0,1][0,1], the sup norm ∥f∥∞=sup⁡t∈[0,1]∣f(t)∣\|f\|_\infty=\sup_{t\in[0,1]} |f(t)| is a norm.