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,\dots,x_N \in\mathbb{C}, and add them together each multiplied by a random sign \pm 1 , then the expected value of its modulus, or the modulus it will be closest to on average, will be not too far off from  \sqrt{|x_1|^{2}+\cdots + |x_N|^{2}}.

Statement of theorem[edit]

Let  \{\epsilon_{n}\}_{n=1}^{N} be i.i.d. random variables with P(\epsilon_n=\pm1)=\frac12 for every n=1\ldots N, i.e., a sequence with Rademacher distribution. Let  0<p<\infty and let  x_1,...,x_N\in \mathbb{C}. Then

 A_p \left( \sum_{n=1}^{N}|x_{n}|^{2} \right)^{\frac{1}{2}} \leq \left(\mathbb{E}\Big|\sum_{n=1}^{N}\epsilon_{n}x_{n}\Big|^{p} \right)^{1/p}  \leq B_p \left(\sum_{n=1}^{N}|x_{n}|^{2}\right)^{\frac{1}{2}}

for some constants  A_p,B_p>0 depending only on p (see Expected value for notation). The sharp values of the constants A_p,B_p were found by Haagerup (Ref. 2; see Ref. 3 for a simpler proof).

Uses in analysis[edit]

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 Lp spaces  L^p(X,\mu) and  L^p(Y,\nu) , 1\leq p < \infty, with bounded norm  \|T\|<\infty , then one can use Khintchine's inequality to show that

 \left\|\left(\sum_{n=1}^{N}|Tf_n|^{2} \right)^{\frac{1}{2}}\right\|_{L^p(Y,\nu)}\leq C_p\left\|\left(\sum_{n=1}^{N}|f_{n}|^{2}\right)^{\frac{1}{2}}\right\|_{L^p(X,\mu)}

for some constant C_p>0 depending only on p and \|T\|.

See also[edit]

References[edit]

  1. Thomas H. Wolff, "Lectures on Harmonic Analysis". American Mathematical Society, University Lecture Series vol. 29, 2003. ISBN 0-8218-3449-5
  2. Uffe Haagerup, "The best constants in the Khintchine inequality", Studia Math. 70 (1981), no. 3, 231–283 (1982).
  3. Fedor Nazarov and Anatoliy Podkorytov, "Ball, Haagerup, and distribution functions", Complex analysis, operators, and related topics, 247–267, Oper. Theory Adv. Appl., 113, Birkhäuser, Basel, 2000.