Cauchy-Schwarz Inequality – Application in Real Analysis

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I had a question about the inequality in the following picture. I'm working on problem 6.1.4 from Classical Fourier Analysis by Grafakos. I've been told that Cauchy-Schwarz will allow me to conclude that
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However, I can't seem to find the the right application of Cauchy-Schwarz that will allow me to conclude this. So far, I've tried making $A = \{x \in \mathbb{R}^n: |x| > 2|y|\}$ and writing the outside integral using an indicator function, but that didn't work. Is there something clever I should multiply by to conclude this?

Best Answer

Let $f(x)=\sqrt{|x|^{n+\epsilon} \int_0^\infty |\Psi_t(x-y)-\Psi_t(x)|^2 \frac{dt}{t}}$ and $g(x)=\frac{1}{\sqrt{|x|^{n+\epsilon}}}$.

Then, by C-S you have $$\int_{|x|>2|y|} \sqrt{\int_0^\infty |\Psi_t(x-y)-\Psi_t(x)|^2 \frac{dt}{t}} dx \leq \sqrt{ \int_{|x|>2|y|}\frac{1}{|x|^{n+\epsilon}}dx } \sqrt{ \int_{|x|>2|y|} |x|^{n+\epsilon} \int_0^\infty |\Psi_t(x-y)-\Psi_t(x)|^2 \frac{dt}{t}dx}$$

Therefore, your problem boils down to showing that $$ \int_{|x|>2|y|}\frac{1}{|x|^{n+\epsilon}}dx \leq c_n^2 |y|^{-\epsilon} $$ which should follow immediatelly from the convergence of the integral on the LHS, with the proper choice of $c_n$.

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