Real Analysis - Convergence to Essential Supremum

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SUMMARY

The discussion centers on proving the limit of the ratio of integrals involving a bounded function \( f \) in a probability space \( (X, M, \mu) \). Specifically, it establishes that \( \lim_{n \rightarrow \infty} \frac{\int_{X} |f|^{n+1} \,d\mu}{\int_{X} |f|^{n} \,d\mu} = \|f\|_\infty \) when \( \|f\|_\infty > 0 \). The proof utilizes properties of integrals, Hölder's inequality, and the concept of convergence in measure. Key steps include bounding the integrals and applying the definitions of limits to demonstrate convergence to the essential supremum.

PREREQUISITES
  • Understanding of probability spaces, specifically \( (X, M, \mu) \)
  • Familiarity with \( L^\infty \) spaces and essential supremum
  • Knowledge of Hölder's inequality and its applications
  • Concepts of convergence in measure and limits of sequences
NEXT STEPS
  • Study the properties of \( L^\infty \) spaces in functional analysis
  • Learn about Hölder's inequality and its implications in measure theory
  • Explore convergence concepts in measure theory, particularly in probability spaces
  • Investigate the relationship between integrals and limits in the context of real analysis
USEFUL FOR

Mathematicians, students of real analysis, and researchers focusing on measure theory and functional analysis will benefit from this discussion.

joypav
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Problem:
Let $\left(X, M, \mu\right)$ be a probability space. Suppose $f \in L^\infty\left(\mu\right)$ and $\left| \left| f \right| \right|_\infty > 0$. Prove that
$lim_{n \rightarrow \infty} \frac{\int_{X}^{}\left| f \right|^{n+1} \,d\mu}{\int_{X}^{}\left| f \right|^{n} \,d\mu} = \left| \left| f \right| \right|_\infty$

Proof:
So... I'm not really sure how to approach this problem.
$\left(X, M, \mu\right)$ is a probability space $\implies \mu\left(X\right) = 1$
Which then, $\left| \left| f \right| \right|_p \leq \left| \left| f \right| \right|_\infty$ for all $p$.

I thought maybe to just use our usual definition for convergence? Meaning,
$\forall \epsilon>0, \exists \delta>0, n>\delta \implies \left|\frac{\int_{X}^{}\left| f \right|^{n+1} \,d\mu}{\int_{X}^{}\left| f \right|^{n} \,d\mu} - \left| \left| f \right| \right|_\infty \right| \leq \epsilon$

But again, I'm not seeing what the next step would be... if someone could give some help I would really appreciate it!
 
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Hello joypav,

If $\alpha_n = \int_X \lvert f\rvert^n\, d\mu$, then using the fact $\lvert f(x)\rvert \le \|f\|_\infty$ a.e., one finds $\alpha_{n+1} \le \|f\|_\infty \alpha_n$, forcing $\limsup_{n\to \infty} \alpha_{n+1}/\alpha_n \le \|f\|_\infty$. On the other hand, if $\varepsilon \in (0, \|f\|_\infty)$, the set $A := \{x\in X : |f(x)| > \|f\|_\infty - \varepsilon\}$ has finite positive measure. Using Hölder's inequality, show that $$\left(\frac{1}{\mu(A)} \int_A \lvert f\vert^{n+1}\, d\mu\right)^{1/(n+1)} \ge \left(\frac{1}{\mu(A)}\int_A \lvert f\vert^n\, d\mu\right)^{1/n}$$ and consequently $$\alpha_{n+1} \ge \int_A \lvert f\rvert^{n+1}\, d\mu \ge \mu(A)^{-1/n}(\|f\|_\infty - \varepsilon)^{n+1}$$ Since $\alpha_n \le \|f\|_\infty^n$, then $\alpha_{n+1}/\alpha_n \ge \mu(A)^{-1/n}(\|f\|_\infty - \varepsilon)$. Taking inferior limits as $n \to \infty$ results in $$\liminf_{n\to \infty} \frac{\alpha_{n+1}}{\alpha_n} \ge \|f\|_\infty-\varepsilon$$As $\epsilon$ was arbitrary, $\liminf_{n\to \infty} \alpha_{n+1}/\alpha_n \ge \|f\|_\infty$. Hence $\alpha_{n+1}/\alpha_n$ converges to $\|f\|_\infty$.
 

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