(New page: 8a) Given such an <math>r<s</math> for any <math>p \in (r,s)</math> we have <math>\int_X |f|^p = \int_{\{x:|f|\leq 1 \}} |f|^p+\int_{\{x:|f|> 1 \}} |f|^p \leq \int_{\{x:|f|\leq 1 \}} |f|...)
 
 
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8a)
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First, subdivide <math> [0,1] </math> into tenths and remove the interior of the seventh. And let <math> E1 = [0,1]\(\frac{7}{10},\frac{8}{10}) </math>.
  
Given such an <math>r<s</math> for any <math>p \in (r,s)</math> we have
 
  
 
<math>\int_X |f|^p = \int_{\{x:|f|\leq 1 \}} |f|^p+\int_{\{x:|f|> 1 \}} |f|^p \leq \int_{\{x:|f|\leq 1 \}} |f|^r+\int_{\{x:|f|> 1 \}} |f|^s <\infty</math>
 
<math>\int_X |f|^p = \int_{\{x:|f|\leq 1 \}} |f|^p+\int_{\{x:|f|> 1 \}} |f|^p \leq \int_{\{x:|f|\leq 1 \}} |f|^r+\int_{\{x:|f|> 1 \}} |f|^s <\infty</math>
  
b)
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b)  Recall that a function <math>\sigma (x):(r,s)\rightarrow \mathbb{R}</math> is convex iff <math>\sigma(tp_1+(1-t)p_2) \leq t\sigma (p_1)+(1-t)\sigma( p_2)</math>
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for any <math>t\in [0,1]</math> and any <math>r<p_1<p_2<s</math>.
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Now, by Holder,
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<math>\int|f|^{tp_1}|f|^{(1-t)p_2} \leq \|f^{tp_1}\|_{\frac{1}{t}}\|f^{tp_2}\|_{\frac{1}{1-t}}</math> since f is in <math>L^{p_1}</math> and <math>L^{p_2}</math> and since <math>t+(1-t)=1</math>.
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Since log is increasing we may take log of both sides while preserving the inequality:
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<math>log[\int|f|^{tp_1}|f|^{(1-t)p_2}] \leq log[\|f^{tp_1}\|_{\frac{1}{t}}\|f^{tp_2}\|_{\frac{1}{1-t}}]
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=log[(\int|f|^{p_1})^t(\int|f|^{p_2})^{1-t}]= log[(\int|f|^{p_1})^t]+log[(\int|f|^{p_2})^{1-t}]
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</math> <math> = tlog[\int|f|^{p_1}]+(1-t)log[\int|f|^{p_2}]= tlog(\phi (p_1))+(1-t)log(\phi (p_2))</math>
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So <math>tlog(\phi (p_1))+(1-t)log(\phi (p_2)) \geq log(\int|f|^{tp_1}|f|^{(1-t)p_2}) = log(\int|f|^{tp_1+(1-t)p_2})=log(\phi (tp_1+(1-t)p_2)).</math>
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Thus <math>log(\phi)</math> is convex on <math>(r,s).</math>
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--[[User:Wardbc|Wardbc]] 19:57, 14 July 2008 (EDT)

Latest revision as of 08:32, 16 July 2008

2a)

First, subdivide $ [0,1] $ into tenths and remove the interior of the seventh. And let $ E1 = [0,1]\(\frac{7}{10},\frac{8}{10}) $.


$ \int_X |f|^p = \int_{\{x:|f|\leq 1 \}} |f|^p+\int_{\{x:|f|> 1 \}} |f|^p \leq \int_{\{x:|f|\leq 1 \}} |f|^r+\int_{\{x:|f|> 1 \}} |f|^s <\infty $


b) Recall that a function $ \sigma (x):(r,s)\rightarrow \mathbb{R} $ is convex iff $ \sigma(tp_1+(1-t)p_2) \leq t\sigma (p_1)+(1-t)\sigma( p_2) $ for any $ t\in [0,1] $ and any $ r<p_1<p_2<s $.

Now, by Holder,

$ \int|f|^{tp_1}|f|^{(1-t)p_2} \leq \|f^{tp_1}\|_{\frac{1}{t}}\|f^{tp_2}\|_{\frac{1}{1-t}} $ since f is in $ L^{p_1} $ and $ L^{p_2} $ and since $ t+(1-t)=1 $.

Since log is increasing we may take log of both sides while preserving the inequality:

$ log[\int|f|^{tp_1}|f|^{(1-t)p_2}] \leq log[\|f^{tp_1}\|_{\frac{1}{t}}\|f^{tp_2}\|_{\frac{1}{1-t}}] =log[(\int|f|^{p_1})^t(\int|f|^{p_2})^{1-t}]= log[(\int|f|^{p_1})^t]+log[(\int|f|^{p_2})^{1-t}] $ $ = tlog[\int|f|^{p_1}]+(1-t)log[\int|f|^{p_2}]= tlog(\phi (p_1))+(1-t)log(\phi (p_2)) $


So $ tlog(\phi (p_1))+(1-t)log(\phi (p_2)) \geq log(\int|f|^{tp_1}|f|^{(1-t)p_2}) = log(\int|f|^{tp_1+(1-t)p_2})=log(\phi (tp_1+(1-t)p_2)). $

Thus $ log(\phi) $ is convex on $ (r,s). $


--Wardbc 19:57, 14 July 2008 (EDT)

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