Difference between revisions of "Norm"
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| <math>\|f\|_{C^k}=\sum^k_{i=1}\sup_{x\in[0,1]}(|f^{(i)}|)</math> | | <math>\|f\|_{C^k}=\sum^k_{i=1}\sup_{x\in[0,1]}(|f^{(i)}|)</math> | ||
| here <math>f^{(k)}</math> denotes the <math>k^\text{th}</math> derivative. | | here <math>f^{(k)}</math> denotes the <math>k^\text{th}</math> derivative. | ||
+ | |- | ||
+ | !colspan="3"|Induced norms | ||
+ | |- | ||
+ | | [[Pullback norm]] | ||
+ | |<math>\|\cdot\|_U</math> | ||
+ | |For a [[Linear map|linear isomorphism]] <math>L:U\rightarrow V</math> where V is a normed vector space | ||
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Revision as of 03:51, 8 March 2015
An understanding of a norm is needed to proceed to linear isometries
Contents
[hide]Normed vector spaces
A normed vector space is a vector space equipped with a norm ∥⋅∥V, it may be denoted (V,∥⋅∥V,F)
Definition
A norm on a vector space (V,F) is a function ∥⋅∥:V→R such that:
- ∀x∈V ∥x∥≥0
- ∥x∥=0⟺x=0
- ∀λ∈F,x∈V ∥λx∥=|λ|∥x∥ where |⋅| denotes absolute value
- ∀x,y∈V ∥x+y∥≤∥x∥+∥y∥ - a form of the triangle inequality
Often parts 1 and 2 are combined into the statement
- ∥x∥≥0 and ∥x∥=0⟺x=0 so only 3 requirements will be stated.
I don't like this
Common norms
Name | Norm | Notes |
---|---|---|
Norms on Rn | ||
1-norm | ∥x∥1=n∑i=1|xi| | it's just a special case of the p-norm. |
2-norm | ∥x∥2=√n∑i=1x2i | Also known as the Euclidean norm (see below) - it's just a special case of the p-norm. |
p-norm | ∥x∥p=(n∑i=1|xi|p)1p | (I use this notation because it can be easy to forget the p in p√) |
∞−norm | ∥x∥∞=sup | Also called \infty-norm |
Norms on \mathcal{C}([0,1],\mathbb{R}) | ||
\|\cdot\|_{L^p} | \|f\|_{L^p}=\left(\int^1_0|f(x)|^pdx\right) | NOTE be careful extending to interval [a,b] as proof it is a norm relies on having a unit measure |
\infty-norm | \|f\|_\infty=\sup_{x\in[0,1]}(|f(x)|) | Following the same spirit as the \infty-norm on \mathbb{R}^n |
\|\cdot\|_{C^k} | \|f\|_{C^k}=\sum^k_{i=1}\sup_{x\in[0,1]}(|f^{(i)}|) | here f^{(k)} denotes the k^\text{th} derivative. |
Induced norms | ||
Pullback norm | \|\cdot\|_U | For a linear isomorphism L:U\rightarrow V where V is a normed vector space |
Equivalence of norms
Given two norms \|\cdot\|_1 and \|\cdot\|_2 on a vector space V we say they are equivalent if:
\exists c,C\in\mathbb{R}\ \forall x\in V:\ c\|x\|_1\le\|x\|_2\le C\|x\|_1
We may write this as \|\cdot\|_1\sim\|\cdot\|_2 - this is an Equivalence relation
TODO: proof
Examples
- Any two norms on \mathbb{R}^n are equivalent
- The norms \|\cdot\|_{L^1} and \|\cdot\|_\infty on \mathcal{C}([0,1],\mathbb{R}) are not equivalent.