Khintchine inequality

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In mathematics, the Khintchine inequality, named after Aleksandr Khinchin and spelled in multiple ways in the Roman alphabet, is a theorem from probability, and is also frequently used in analysis. Heuristically, it says that if we pick $N$ complex numbers $x_1,...,x_N \in\mathbb{C}$ , and add them together each multiplied by a random sign $\pm 1$ , then the expected value of its size, or the size it will be closest to on average, will be not too far off from $\sqrt{|x_1|^{2}+\cdots + |x_N|^{2}}$ .

[edit] Statement of theorem

Let $\{w_{n}\}_{n=1}^{N}$ be independent random variables taking values of $\pm 1$ , $0<p<\infty$ , and $x_1,...,x_N\in \mathbb{C}$ . Then

$A_p ( \sum_{n=1}^{N}|x_{n}|^{2})^{\frac{1}{2}} \leq (\mathbb{E}|\sum_{n=1}^{N}w_{n}x_{n}|^{p})^{1/p} \leq B_p (\sum_{n=1}^{N}|x_{n}|^{2})^{\frac{1}{2}}$

for some constants $A p, B p > 0$ depending only on $p$ (see Expected value for notation).

[edit] Uses in analysis

The uses of this inequality are not limited to applications in probability theory. One example of its use in analysis is the following: if we let $T$ be a linear operator between two L^p spaces $L p (X,μ)$ and $L p (Y,ν)$ , $1\leq p<\infty$ , with bounded norm $||T||<\infty$ , then one can use Khinchine's inequality to show that