Thu-22-05-2025 18:04
profe: Natalia Accomazzo Scotti
status:
tags: Espacios Normados
Def. : {\color{Cyan} \text{Def. :} } Def. :
Sea E un R − espacio vectorial. Una norma sobre E es una funci o ˊ n ∣ ∣ ⋅ ∣ ∣ : E → R ≥ 0 tal que: 1. ∣ ∣ x ∣ ∣ = 0 ⟺ x = 0 2. ∣ ∣ α x ∣ ∣ = ∣ α ∣ ⋅ ∣ ∣ x ∣ ∣ , α ∈ R , x ∈ E 3. ∣ ∣ x + y ∣ ∣ ≤ ∣ ∣ x ∣ ∣ + ∣ ∣ y ∣ ∣ , x , y ∈ E \begin{array}{l}
\text{Sea $E$ un $\mathbb{R}-$espacio vectorial. Una norma sobre $E$ es una función $\lvert \lvert \cdot \rvert \rvert:E\to \mathbb{R}_{\geq 0}$ tal que:}
\\1. \lvert \lvert x \rvert \rvert=0\iff x=0\\
\text{2. $\lvert \lvert \alpha x \rvert\rvert=|\alpha|\cdot \lvert \lvert x \rvert\rvert$ , $\alpha \in \mathbb{R},x \in E$}\\
\text{3. $\lvert \lvert x+y \rvert\rvert\leq \lvert \lvert x \rvert\rvert+\lvert \lvert y \rvert\rvert,\quad x,y \in E$ }
\end{array} Sea E un R − espacio vectorial. Una norma sobre E es una funci o ˊ n ∣∣ ⋅ ∣∣ : E → R ≥ 0 tal que: 1. ∣∣ x ∣∣ = 0 ⟺ x = 0 2. ∣∣ α x ∣∣ = ∣ α ∣ ⋅ ∣∣ x ∣∣ , α ∈ R , x ∈ E 3. ∣∣ x + y ∣∣ ≤ ∣∣ x ∣∣ + ∣∣ y ∣∣ , x , y ∈ E
O b s : Obs: O b s :
Los espacios normados son espacios métricos con d ( x , y ) = ∣ ∣ x − y ∣ ∣ d(x,y)=\lvert \lvert x-y \rvert\rvert d ( x , y ) = ∣∣ x − y ∣∣ . No todas las distancias provienen de una norma.
En ( R , δ ) (\mathbb{R},\delta) ( R , δ )
∄ ∣ ∣ ⋅ ∣ ∣ en R ∣ δ ( x , y ) = ∣ ∣ x − y ∣ ∣ \not\exists\:\lvert \lvert \cdot \rvert\rvert\text{ en }\mathbb{R}\bigm| \delta(x,y)=\lvert \lvert x-y \rvert\rvert ∃ ∣∣ ⋅ ∣∣ en R δ ( x , y ) = ∣∣ x − y ∣∣
Para probar esto, supongamos que existe:
1 = δ ( 2 , 1 ) = ∣ ∣ 2 − 1 ∣ ∣ = ∣ ∣ 1 ∣ ∣ 1 = δ ( 3 , 1 ) = ∣ ∣ 3 − 1 ∣ ∣ = 2 ⋅ ∣ ∣ 1 ∣ ∣ = 2 \begin{array}{c}
1=\delta(2,1)=\lvert \lvert 2-1 \rvert\rvert =\lvert \lvert 1 \rvert\rvert \\
1=\delta(3,1)=\lvert \lvert 3-1 \rvert\rvert =2\cdot \lvert \lvert 1 \rvert\rvert =2
\end{array} 1 = δ ( 2 , 1 ) = ∣∣ 2 − 1 ∣∣ = ∣∣ 1 ∣∣ 1 = δ ( 3 , 1 ) = ∣∣ 3 − 1 ∣∣ = 2 ⋅ ∣∣ 1 ∣∣ = 2
Absurdo, entonces no existe.
E j e m p l o s : Ejemplos: E j e m pl os :
E = R n E=\mathbb{R}^{n} E = R n
∣ ∣ x ∣ ∣ 1 = ∑ i = 1 n ∣ x i ∣ ∣ ∣ x ∣ ∣ 2 = ( ∑ i = 1 n ∣ x i ∣ 2 ) 1 2 ∣ ∣ x ∣ ∣ ∞ = s u p 1 ≤ i ≤ n ∣ x i ∣ \begin{array}{l}
\displaystyle \lvert \lvert x \rvert\rvert_{1}=\sum_{i=1}^{n} |x_{i}| \\
\displaystyle \lvert \lvert x \rvert\rvert _{2}=\left( \sum_{i=1}^{n} |x_{i}|^{2} \right)^{\frac{1}{2}} \\
\lvert \lvert x \rvert\rvert _{\infty}=\underset{ 1\leq i\leq n }{ sup }\:\left| x_{i} \right|
\end{array} ∣∣ x ∣ ∣ 1 = i = 1 ∑ n ∣ x i ∣ ∣∣ x ∣ ∣ 2 = ( i = 1 ∑ n ∣ x i ∣ 2 ) 2 1 ∣∣ x ∣ ∣ ∞ = 1 ≤ i ≤ n s u p ∣ x i ∣
E = l ∞ ( R ) = { ( ( a n ) ⊆ R , s u p n ∣ a n ∣ < ∞ ) } E=\mathscr{l}^{\infty(\mathbb{R})}=\{ ((a_{n})\subseteq \mathbb{R},\underset{ n }{ sup }\:\left| a_{n} \right|<\infty)\} E = l ∞ ( R ) = {(( a n ) ⊆ R , n s u p ∣ a n ∣ < ∞ )}
l ∞ \mathscr{l}^{\infty} l ∞ es espacio vectorial.
( a n ) + ( b n ) = ( a n + b n ) n ∈ N (a_{n})+(b_{n})=(a_{n}+b_{n})_{n \in \mathbb{N}} ( a n ) + ( b n ) = ( a n + b n ) n ∈ N
α ( a n ) = ( α a n ) n ∈ N \alpha(a_{n})=(\alpha a_{n})_{n \in \mathbb{N}} α ( a n ) = ( α a n ) n ∈ N
∣ ∣ ( a n ) ∣ ∣ ∞ = s u p n ∣ a n ∣ \lvert \lvert (a_{n}) \rvert\rvert_{\infty}=\underset{ n }{ sup }\:\left| a_{n} \right| ∣∣( a n )∣ ∣ ∞ = n s u p ∣ a n ∣ es una norma.
∣ ∣ ( a n ) ∣ ∣ ∞ = 0 \lvert \lvert (a_{n}) \rvert\rvert_{\infty}=0 ∣∣( a n )∣ ∣ ∞ = 0
⟹ s u p n ∣ a n ∣ = 0 ⟹ ∣ a n ∣ = 0 ∀ n \implies \underset{ n }{ sup }\:\left| a_{n} \right| =0\implies |a_{n}|=0\quad \forall n ⟹ n s u p ∣ a n ∣ = 0 ⟹ ∣ a n ∣ = 0 ∀ n
⟹ a n = 0 ∀ n \implies a_{n}=0\quad \forall n ⟹ a n = 0 ∀ n
∣ ∣ α ⋅ ( a n ) ∣ ∣ ∞ = ∣ ∣ ( α ⋅ a n ) ∣ ∣ ∞ \lvert \lvert \alpha\cdot(a_{n}) \rvert\rvert_{\infty}=\lvert \lvert (\alpha\cdot a_{n}) \rvert\rvert_{\infty} ∣∣ α ⋅ ( a n )∣ ∣ ∞ = ∣∣( α ⋅ a n )∣ ∣ ∞
= s u p n ∣ α ⋅ a n ∣ = ∣ α ∣ ⋅ ∣ ∣ ( a n ) ∣ ∣ ∞ =\underset{ n }{ sup }\:\left| \alpha\cdot a_{n} \right| =|\alpha|\cdot \lvert \lvert (a_{n}) \rvert\rvert _{\infty} = n s u p ∣ α ⋅ a n ∣ = ∣ α ∣ ⋅ ∣∣( a n )∣ ∣ ∞
∣ ∣ ( a n ) + ( b n ) ∣ ∣ ∞ = s u p n ∣ a n + b n ∣ \lvert \lvert (a_{n})+(b_{n}) \rvert\rvert_{\infty}=\underset{ n }{ sup }\:\left| a_{n}+b_{n} \right| ∣∣( a n ) + ( b n )∣ ∣ ∞ = n s u p ∣ a n + b n ∣
≤ s u p n ( ∣ a n ∣ + ∣ b n ∣ ) ≤ s u p n ∣ a n ∣ + s u p n ∣ b n ∣ = ∣ ∣ ( a n ) ∣ ∣ ∞ + ∣ ∣ b n ∣ ∣ ∞ \leq \underset{ n }{ sup }\:(\left| a_{n} \right|+|b_{n}|)\leq \underset{ n }{ sup }\:\left| a_{n} \right| +\underset{ n }{ sup }\:\left| b_{n} \right| =\lvert \lvert (a_{n}) \rvert\rvert _{\infty}+\lvert \lvert b_{n} \rvert\rvert _{\infty} ≤ n s u p ( ∣ a n ∣ + ∣ b n ∣ ) ≤ n s u p ∣ a n ∣ + n s u p ∣ b n ∣ = ∣∣( a n )∣ ∣ ∞ + ∣∣ b n ∣ ∣ ∞
l ∞ \mathscr{l}^{\infty} l ∞ tiene dimensión infinito
e 1 = ( 1 , 0 , 0 , 0 , , , , ) e 2 = ( 0 , 1 , 0 , 0 , 0 , … ) e 3 = ( 0 , 0 , 1 , 0 , … ) e_{1}=(1,0,0,0,,,,)
\quad e_{2}=(0,1,0,0,0,\dots)
\quad e_{3}=(0,0,1,0,\dots) e 1 = ( 1 , 0 , 0 , 0 ,,,, ) e 2 = ( 0 , 1 , 0 , 0 , 0 , … ) e 3 = ( 0 , 0 , 1 , 0 , … )
e m = 0 , 0 , … , 0 , 1 , ⏟ m-esimo elemento 0 , … , 0 e_{m}=\underbrace{ 0,0,\dots,0,1, }_{ \text{m-esimo elemento} }0,\dots, 0 e m = m-esimo elemento 0 , 0 , … , 0 , 1 , 0 , … , 0
Son l i li l i ⟸ d i m ( E ) = ∞ \impliedby dim(E)=\infty ⟸ d im ( E ) = ∞
E = C [ 0 , 1 ] = { f : [ 0 , 1 ] → R c o n t i n u a } E=C[0,1]=\{ f:[0,1]\to \mathbb{R} \\\text{ }continu a\} E = C [ 0 , 1 ] = { f : [ 0 , 1 ] → R co n t in u a }
( f + g ) ( t ) (f+g)(t) ( f + g ) ( t ) =f ( t ) + g ( t ) f(t)+g(t) f ( t ) + g ( t )
( α f ) ( t ) = α ⋅ f ( t ) (\alpha f)(t)=\alpha\cdot f(t) ( α f ) ( t ) = α ⋅ f ( t )
∣ ∣ f ∣ ∣ = s u p t ∈ [ 0 , 1 ] ∣ f ( t ) ∣ \lvert \lvert f \rvert\rvert=\underset{ t\in[0,1] }{ sup }\:\left|f(t) \right| ∣∣ f ∣∣ = t ∈ [ 0 , 1 ] s u p ∣ f ( t ) ∣
∣ ∣ ∫ 0 1 ∣ f ( t ) ∣ d x ∣ ∣ \left\lvert \left\lvert_{} \int _{0}^{1} |f(t)| \, dx \right\rvert \right\rvert ∫ 0 1 ∣ f ( t ) ∣ d x
Son normas. Además tiene dimensión infinito: f n ( t ) = t n , \frac{f_{n}}(t)=t^{n}, ( f n t ) = t n , n ≥ 0 n\geq_{0} n ≥ 0
Son l i li l i y son ∞ \infty ∞
Def. : {\color{Cyan} \text{Def. :} } Def. :
( E , ∣ ∣ ⋅ ∣ ∣ ) decimos que es un espacio de Banach si es completo Con la distancia heredada de la norma \begin{array}{l}
\text{$(E,\lvert \lvert \cdot \rvert\rvert)$ decimos que es un espacio de Banach si es completo}\\
\text{Con la distancia heredada de la norma}
\end{array} ( E , ∣∣ ⋅ ∣∣) decimos que es un espacio de Banach si es completo Con la distancia heredada de la norma
O b s : Obs: O b s :
Sea ( E , ∣ ∣ ⋅ ∣ ∣ ) (E,\lvert \lvert \cdot \rvert\rvert_{}) ( E , ∣∣ ⋅ ∣ ∣ )
B ( x , r ) = { y ∈ E : ∣ ∣ x − y ∣ ∣ < r } B(x,r)=\{ y \in E:\lvert \lvert x-y \rvert\rvert <r \} B ( x , r ) = { y ∈ E : ∣∣ x − y ∣∣ < r }
Si y ∈ B ( x , r ) ⟹ y \in B(x,r)\implies y ∈ B ( x , r ) ⟹ el segmento que une a y y y con x x x también está en la bola.
La bola es convexa o conexa?
z ( t ) = t x + ( 1 − t ) y , t ∈ [ 0 , 1 ] ∣ ∣ z ( t ) − x ∣ ∣ = ∣ ∣ t x + ( 1 − t ) y − x ∣ ∣ = ∣ ∣ ( t − 1 ) x + ( 1 − t ) y ∣ ∣ = = ∣ 1 − t ∣ ⏟ ≤ 1 ⋅ ∣ ∣ y − x ∣ ∣ < r \begin{array}{c}
z(t)=tx+(1-t)y,\quad t \in[0,1] \\
\lvert \lvert z(t)-x \rvert\rvert =\lvert \lvert tx+(1-t)y-x \rvert\rvert =\lvert \lvert (t-1)x+(1-t)y \rvert\rvert = \\
=\underbrace{ |1-t| }_{ \leq 1 }\cdot \lvert \lvert y-x \rvert\rvert <r
\end{array} z ( t ) = t x + ( 1 − t ) y , t ∈ [ 0 , 1 ] ∣∣ z ( t ) − x ∣∣ = ∣∣ t x + ( 1 − t ) y − x ∣∣ = ∣∣( t − 1 ) x + ( 1 − t ) y ∣∣ = = ≤ 1 ∣1 − t ∣ ⋅ ∣∣ y − x ∣∣ < r
⟹ z ( t ) ∈ B ( x , r ) \implies z(t)\in B(x,r) ⟹ z ( t ) ∈ B ( x , r ) ∀ t ∈ [ 0 , 1 ] \forall t\in[0,1] ∀ t ∈ [ 0 , 1 ]
B ( x , r ) = { y : ∣ ∣ x − y ∣ ∣ < r } = { y + x : ∣ ∣ y ∣ ∣ < r } = B ( 0 , r ) + x = { y : ∣ ∣ y ∣ ∣ < r } + x = B(x,r)=\{ y:\lvert \lvert x-y \rvert\rvert <r \}=\{ y+x:\lvert \lvert y \rvert\rvert <r \}=B(0,r)+x=\{ y:\lvert \lvert y \rvert\rvert <r \}+x= B ( x , r ) = { y : ∣∣ x − y ∣∣ < r } = { y + x : ∣∣ y ∣∣ < r } = B ( 0 , r ) + x = { y : ∣∣ y ∣∣ < r } + x =
{ r ⋅ y : ∣ ∣ y ∣ ∣ < 1 } + x = r ⋅ B ( 0 , 1 ) + x \{ r\cdot y:\lvert \lvert y \rvert\rvert <1 \}+x=r\cdot B(0,1)+x { r ⋅ y : ∣∣ y ∣∣ < 1 } + x = r ⋅ B ( 0 , 1 ) + x
Esto quiere decir que en los espacios normados trasladar bolas, achicarlas o agrandarlas es indistinto.
Def. : {\color{Cyan} \text{Def. :} } Def. :
Sea E un espacio normado con dos normas ∣ ∣ ⋅ ∣ ∣ 1 y ∣ ∣ ⋅ ∣ ∣ 2 Decimos que son equivalentes si las distancias inducidas son equivalentes. \begin{array}{l}
\text{Sea $E$ un espacio normado con dos normas $\lvert \lvert \cdot \rvert\rvert_{1}$ y $\lvert \lvert \cdot \rvert\rvert_{2}$ }\\
\text{Decimos que son equivalentes si las distancias inducidas son equivalentes.}
\end{array} Sea E un espacio normado con dos normas ∣∣ ⋅ ∣ ∣ 1 y ∣∣ ⋅ ∣ ∣ 2 Decimos que son equivalentes si las distancias inducidas son equivalentes.
Prop. : {\color{Orange} \text{Prop. :} } Prop. :
Dos normas son equivalentes ⟺ ∃ c 1 , c 2 > 0 tal que: \begin{array}{l}
\text{Dos normas son equivalentes$\iff \:\exists\:c_{1},c_{2}>0$ tal que:}
\end{array} Dos normas son equivalentes ⟺ ∃ c 1 , c 2 > 0 tal que:
c 1 ∣ ∣ x ∣ ∣ 1 ≤ ∣ ∣ x ∣ ∣ 2 ≤ c 2 ∣ ∣ x ∣ ∣ 1 ∀ x ∈ E c_{1}\lvert \lvert x \rvert\rvert _{1}\leq \lvert \lvert x \rvert\rvert _{2}\leq c_{2}\lvert \lvert x \rvert\rvert _{1}\quad \forall x \in E c 1 ∣∣ x ∣ ∣ 1 ≤ ∣∣ x ∣ ∣ 2 ≤ c 2 ∣∣ x ∣ ∣ 1 ∀ x ∈ E
Dem: {\color{Orange} \text{Dem:} } Dem:
⟸ ) \impliedby) ⟸ )
Ya la sabemos.
⟹ ) \implies) ⟹ )
Sea x ∈ E , ∣ ∣ x ∣ ∣ 1 = 1 x \in E,\lvert \lvert x \rvert\rvert_{1}=1 x ∈ E , ∣∣ x ∣ ∣ 1 = 1
x ∈ B 1 ( 0 , 2 ) x \in B_{1}(0,2) x ∈ B 1 ( 0 , 2 )
∃ r > 0 ∣ B 1 ( 0 , 2 ) ⊆ B 2 ( 0 , r ) \:\exists\:r>0\bigm| B_{1}(0,2)\subseteq B_{2}(0,r) ∃ r > 0 B 1 ( 0 , 2 ) ⊆ B 2 ( 0 , r )
Pensar esto último porque vale esto. Es por esto:
Recordamos que ∣ ∣ ⋅ ∣ ∣ 1 , ∣ ∣ ⋅ ∣ ∣ 2 \lvert \lvert \cdot \rvert\rvert_{1},\lvert \lvert \cdot \rvert\rvert_{2} ∣∣ ⋅ ∣ ∣ 1 , ∣∣ ⋅ ∣ ∣ 2 equivalentes ⟺ ∀ x ∈ E ∀ r > 0 ∃ r 1 , r 2 > 0 ∣ \iff \forall x \in E \quad\forall r>0\:\exists\:r_{1},r_{2}>0\bigm| ⟺ ∀ x ∈ E ∀ r > 0 ∃ r 1 , r 2 > 0
B 1 ( x , r 1 ) ⊆ B 2 ( x , r ) B 2 ( x , r 2 ) ⊆ B 1 ( x , r ) \begin{array}{c}
B_{1}(x,r_{1})\subseteq B_{2}(x,r) \\
B_{2}(x,r_{2})\subseteq B_{1}(x,r)
\end{array} B 1 ( x , r 1 ) ⊆ B 2 ( x , r ) B 2 ( x , r 2 ) ⊆ B 1 ( x , r )
Si x = 0 , r = 1 ⟹ ∃ r 1 > 0 ∣ B 1 ( 0 , r 1 ) ⊆ B 2 ( 0 , 1 ) x=0,r=1\implies \:\exists\:r_{1}>0\bigm|B_{1}(0,r_{1})\subseteq B_{2}(0,1) x = 0 , r = 1 ⟹ ∃ r 1 > 0 B 1 ( 0 , r 1 ) ⊆ B 2 ( 0 , 1 )
2 r 1 B 1 ( 0 , r 1 ) ⊆ 2 r 1 B 2 ( 0 , 1 ) B 1 ( 0 , r 1 ) ⊆ B 2 ( 0 , 2 r 1 ⏟ r ) \begin{array}{c}
\frac{2}{r_{1}}B_{1}(0,r_{1})\subseteq \frac{2}{r_{1}}B_{2}(0,1) \\
B_{1}(0,r_{1})\subseteq B_{2}\left( 0,\underbrace{ \frac{2}{r_{1}} }_{ r } \right)
\end{array} r 1 2 B 1 ( 0 , r 1 ) ⊆ r 1 2 B 2 ( 0 , 1 ) B 1 ( 0 , r 1 ) ⊆ B 2 0 , r r 1 2
⟹ ∣ ∣ x ∣ ∣ 2 ≤ r ⟹ ∣ ∣ x ∣ ∣ 2 ≤ r ⏟ c 2 ⋅ ∣ ∣ x ∣ ∣ 1 \implies \lvert \lvert x \rvert\rvert _{2}\leq r\implies \lvert \lvert x \rvert\rvert _{2}\leq \underbrace{ r }_{ c_{2} }\cdot \lvert \lvert x \rvert\rvert _{1} ⟹ ∣∣ x ∣ ∣ 2 ≤ r ⟹ ∣∣ x ∣ ∣ 2 ≤ c 2 r ⋅ ∣∣ x ∣ ∣ 1
Sea x ∈ E x \in E x ∈ E cualquiera, x ≠ 0 x\neq0 x = 0
Sea x ~ = x ∣ ∣ x ∣ ∣ 1 \displaystyle\tilde{x}=\frac{x}{\lvert \lvert x \rvert\rvert_{1}} x ~ = ∣∣ x ∣ ∣ 1 x , así ∣ ∣ x ~ ∣ ∣ 1 = 1 \lvert \lvert \tilde{x} \rvert\rvert_{1}=1 ∣∣ x ~ ∣ ∣ 1 = 1
∣ ∣ x ~ ∣ ∣ 2 ≤ c 2 ⋅ ∣ ∣ x ~ ∣ ∣ 1 \lvert \lvert \tilde{x} \rvert\rvert _{2}\leq c_{2}\cdot \lvert \lvert \tilde{x} \rvert\rvert _{1} ∣∣ x ~ ∣ ∣ 2 ≤ c 2 ⋅ ∣∣ x ~ ∣ ∣ 1
∣ ∣ x ∣ ∣ x ~ ∣ ∣ 1 ∣ ∣ 2 ≤ c 2 ⋅ ∣ ∣ x ∣ ∣ x ~ ∣ ∣ 1 ∣ ∣ 1 \left\lvert \left\lvert \frac{x}{\lvert \lvert \tilde{x} \rvert\rvert _{1}} \right\rvert \right\rvert _{2}\leq c_{2}\cdot\left\lvert \left\lvert \frac{x}{\lvert \lvert \tilde{x} \rvert\rvert _{1}} \right\rvert \right\rvert _{1} ∣∣ x ~ ∣ ∣ 1 x 2 ≤ c 2 ⋅ ∣∣ x ~ ∣ ∣ 1 x 1
1 ∣ ∣ x ~ ∣ ∣ 1 ⋅ ∣ ∣ x ∣ ∣ 2 ≤ c 2 ⋅ 1 ∣ ∣ x ~ ∣ ∣ 1 ⋅ ∣ ∣ x ∣ ∣ 1 \frac{1}{\lvert \lvert \tilde{x} \rvert\rvert _{1}}\cdot \lvert \lvert x \rvert\rvert _{2}\leq c_{2}\cdot\frac{1}{\lvert \lvert \tilde{x} \rvert\rvert _{1}}\cdot \lvert \lvert x \rvert\rvert _{1} ∣∣ x ~ ∣ ∣ 1 1 ⋅ ∣∣ x ∣ ∣ 2 ≤ c 2 ⋅ ∣∣ x ~ ∣ ∣ 1 1 ⋅ ∣∣ x ∣ ∣ 1
⟹ ∣ ∣ x ∣ ∣ 2 ≤ c 2 ⋅ ∣ ∣ x ∣ ∣ 1 ∀ x ∈ E \implies \lvert \lvert x \rvert\rvert _{2}\leq c_{2}\cdot \lvert \lvert x \rvert\rvert _{1}\quad \quad \forall x \in E ⟹ ∣∣ x ∣ ∣ 2 ≤ c 2 ⋅ ∣∣ x ∣ ∣ 1 ∀ x ∈ E
Teorema : {\color{violet} \text{Teorema :} } Teorema :
En R n , todas las normas son equivalentes. \begin{array}{l}
\text{En $\mathbb{R}^{n},$ todas las normas son equivalentes.}
\end{array} En R n , todas las normas son equivalentes.
Dem: {\color{violet} \text{Dem:} } Dem:
Sea ∣ ∣ ⋅ ∣ ∣ \lvert \lvert \cdot \rvert\rvert ∣∣ ⋅ ∣∣ una norma en R n \mathbb{R}^{n} R n . Vamos a ver que ∃ c 1 , c 2 > 0 \:\exists\:c_{1},c_{2}>0 ∃ c 1 , c 2 > 0 tal que
c 1 ⋅ ∣ ∣ x ∣ ∣ ∞ ≤ ∣ ∣ x ∣ ∣ ≤ c 2 ⋅ ∣ ∣ x ∣ ∣ ∞ ∀ x ∈ R n c_{1}\cdot \lvert \lvert x \rvert\rvert _{\infty}\leq \lvert \lvert x \rvert\rvert \leq c_{2}\cdot \lvert \lvert x \rvert\rvert _{\infty}\quad \quad \forall x \in \mathbb{R}^{n} c 1 ⋅ ∣∣ x ∣ ∣ ∞ ≤ ∣∣ x ∣∣ ≤ c 2 ⋅ ∣∣ x ∣ ∣ ∞ ∀ x ∈ R n
Sea { e i } i = 1 n \{ e_{i} \}_{i=1}^{n} { e i } i = 1 n la base canónica de R n \mathbb{R}^{n} R n
x = ∑ i = 1 n x i ⋅ e i x=\sum_{i=1}^{n} x_{i}\cdot e_{i} x = i = 1 ∑ n x i ⋅ e i
∣ ∣ x ∣ ∣ = ∣ ∣ ∑ i = 1 n x i ⋅ e i ∣ ∣ ≤ ∑ i = 1 n ∣ ∣ x i − e i ∣ ∣ = ∑ i = 1 n ∣ x i ∣ ⋅ ∣ ∣ e i ∣ ∣ ≤ ∑ i = 1 n s u p i ∣ x i ∣ ⋅ ∣ ∣ e i ∣ ∣ ≤ ∣ ∣ x ∣ ∣ ∞ ⋅ ( ∑ i = 1 n ∣ ∣ e i ∣ ∣ ) ⏟ c 2 \begin{array}{c}
\displaystyle \lvert \lvert x \rvert\rvert =\left\lvert \left\lvert \sum_{i=1}^{n} x_{i}\cdot e_{i} \right\rvert \right\rvert \\
\displaystyle \leq \sum_{i=1}^{n} \lvert \lvert x_{i}-e_{i} \rvert\rvert =\sum_{i=1}^{n} |x_{i}|\cdot \lvert \lvert e_{i} \rvert\rvert \leq \sum_{i=1}^{n} \underset{ i }{ sup }\:\left| x_{i} \right| \cdot \lvert \lvert e_{i} \rvert\rvert \\
\displaystyle \leq \lvert \lvert x \rvert\rvert_{\infty} \cdot\underbrace{ \left( \sum_{i=1}^{n} \lvert \lvert e_{i} \rvert\rvert \right) }_{ c_{2} }
\end{array} ∣∣ x ∣∣ = i = 1 ∑ n x i ⋅ e i ≤ i = 1 ∑ n ∣∣ x i − e i ∣∣ = i = 1 ∑ n ∣ x i ∣ ⋅ ∣∣ e i ∣∣ ≤ i = 1 ∑ n i s u p ∣ x i ∣ ⋅ ∣∣ e i ∣∣ ≤ ∣∣ x ∣ ∣ ∞ ⋅ c 2 ( i = 1 ∑ n ∣∣ e i ∣∣ )
⟹ ∣ ∣ x ∣ ∣ ≤ c 2 ⋅ ∣ ∣ x ∣ ∣ ∞ \implies \lvert \lvert x \rvert\rvert \leq c_{2}\cdot \lvert \lvert x \rvert\rvert_{\infty} ⟹ ∣∣ x ∣∣ ≤ c 2 ⋅ ∣∣ x ∣ ∣ ∞
Falta ver que ∃ c 1 ∣ c 1 ⋅ ∣ ∣ x ∣ ∣ ∞ ≤ ∣ ∣ x ∣ ∣ \:\exists\:c_{1}\bigm|c_{1}\cdot \lvert \lvert x \rvert\rvert_{\infty}\leq \lvert \lvert x \rvert\rvert ∃ c 1 c 1 ⋅ ∣∣ x ∣ ∣ ∞ ≤ ∣∣ x ∣∣
Sea K = S ( 0 , 1 ) = { x ∈ R n : ∣ ∣ x ∣ ∣ ∞ = 1 } K=S(0,1)=\{ x \in \mathbb{R}^{n}:\lvert \lvert x \rvert\rvert_{\infty}=1 \} K = S ( 0 , 1 ) = { x ∈ R n : ∣∣ x ∣ ∣ ∞ = 1 }
Entonces K K K es cerrado y acotado en R n . \mathbb{R}^{n}. R n . Entonces es compacto (con ∣ ∣ ⋅ ∣ ∣ ∞ \lvert \lvert \cdot \rvert\rvert_{\infty} ∣∣ ⋅ ∣ ∣ ∞ ).
Afirmo
f : ( K , ∣ ∣ ⋅ ∣ ∣ ∞ ) → R Es continua x ↦ ∣ ∣ x ∣ ∣ 1 \begin{array}{c}
f:(K,\lvert \lvert \cdot \rvert\rvert_{\infty} )\to \mathbb{R} \quad \text{ Es continua}\\
\quad x\mapsto \lvert \lvert x \rvert\rvert_{1}
\end{array} f : ( K , ∣∣ ⋅ ∣ ∣ ∞ ) → R Es continua x ↦ ∣∣ x ∣ ∣ 1
∣ ∣ ∣ x ∣ ∣ − ∣ ∣ y ∣ ∣ ∣ ≤ ∣ ∣ x − y ∣ ∣ ≤ c 2 ⋅ ∣ ∣ x − y ∣ ∣ ∞ \left| \lvert \lvert x \rvert\rvert -\lvert \lvert y \rvert\rvert \right| \leq \lvert \lvert x-y \rvert\rvert \leq c_{2}\cdot \lvert \lvert x-y \rvert\rvert_{\infty} ∣ ∣∣ x ∣∣ − ∣∣ y ∣∣ ∣ ≤ ∣∣ x − y ∣∣ ≤ c 2 ⋅ ∣∣ x − y ∣ ∣ ∞
Es Lipschitz, por lo tanto es continua.
Como f f f es continua y K K K compacto ⟹ f \implies f ⟹ f tiene máximo y mínimo.
Sea c 2 = m i n ( f ) = m i n { ∣ ∣ x ∣ ∣ : x ∈ K } c_{2}=min(f)=min\{ \lvert \lvert x \rvert\rvert:x \in K \} c 2 = min ( f ) = min {∣∣ x ∣∣ : x ∈ K }
Entonces c 2 > 0 c_{2}>0 c 2 > 0 porque 0 ∉ K 0 \not\in K 0 ∈ K
∣ ∣ x ∣ ∣ ≥ c 2 ∀ x ∈ K \lvert \lvert x \rvert\rvert \geq c_{2}\quad \forall x \in K ∣∣ x ∣∣ ≥ c 2 ∀ x ∈ K
⟹ ∣ ∣ x ∣ ∣ ≥ c 2 ⋅ ∣ ∣ x ∣ ∣ ∞ p ∀ ∣ ∣ x ∣ ∣ = 1 \implies \lvert \lvert x \rvert\rvert \geq c_{2}\cdot \lvert \lvert x \rvert\rvert_{\infty} \quad p\quad \forall \lvert \lvert x \rvert\rvert =1 ⟹ ∣∣ x ∣∣ ≥ c 2 ⋅ ∣∣ x ∣ ∣ ∞ p ∀ ∣∣ x ∣∣ = 1
Sea x ∈ R n x \in \mathbb{R}^{n} x ∈ R n , x ≠ . S e a x\neq.Sea x = . S e a x ~ = x ∣ ∣ x ∣ ∣ ∞ \displaystyle\tilde{x}=\frac{x}{\lvert \lvert x \rvert\rvert_{\infty}} x ~ = ∣∣ x ∣ ∣ ∞ x
⟹ x ~ ∈ K ⟹ ∣ ∣ x ~ ∣ ∣ ≥ c 2 ⋅ ∣ ∣ x ~ ∣ ∣ ∞ \implies \tilde{x} \in K\implies \lvert \lvert \tilde{x} \rvert\rvert \geq c_{2}\cdot \lvert \lvert \tilde{x} \rvert\rvert_{\infty} ⟹ x ~ ∈ K ⟹ ∣∣ x ~ ∣∣ ≥ c 2 ⋅ ∣∣ x ~ ∣ ∣ ∞
⟹ ∣ ∣ x ∣ ∣ x ∣ ∣ ∞ ∣ ∣ ≥ c 2 ⋅ ∣ ∣ x ∣ ∣ x ∣ ∣ ∞ ∣ ∣ ∞ \implies \left\lvert \left\lvert \frac{x}{\lvert \lvert x \rvert\rvert_{\infty} } \right\rvert \right\rvert \geq c_{2}\cdot \left\lvert \left\lvert \frac{x}{\lvert \lvert x \rvert\rvert_{\infty} } \right\rvert \right\rvert_{\infty} ⟹ ∣∣ x ∣ ∣ ∞ x ≥ c 2 ⋅ ∣∣ x ∣ ∣ ∞ x ∞
⟹ ∣ ∣ x ∣ ∣ ≥ c 2 ⋅ ∣ ∣ x ∣ ∣ ∞ \implies \lvert \lvert x \rvert\rvert \geq c_{2}\cdot \lvert \lvert x \rvert\rvert_{\infty} ⟹ ∣∣ x ∣∣ ≥ c 2 ⋅ ∣∣ x ∣ ∣ ∞
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