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.
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|-
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!colspan="3"|Induced norms
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|-
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| [[Pullback norm]]
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|<math>\|\cdot\|_U</math>
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|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

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 :VR such that:

  1. xV x0
  2. x=0x=0
  3. λF,xV λx=|λ|x where || denotes absolute value
  4. x,yV x+yx+y - a form of the triangle inequality

Often parts 1 and 2 are combined into the statement

  • x0 and x=0x=0 so only 3 requirements will be stated.

I don't like this

Common norms

Name Norm Notes
Norms on Rn
1-norm x1=ni=1|xi| it's just a special case of the p-norm.
2-norm x2=ni=1x2i Also known as the Euclidean norm (see below) - it's just a special case of the p-norm.
p-norm xp=(ni=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.

Examples