Proof of the square-function characterisation of L(log L)^{1/2}: part I

This is a follow-up on the post on the Chang-Wilson-Wolff inequality and how it can be proven using a lemma of Tao-Wright. The latter consists of a square-function characterisation of the Orlicz space L(\log L)^{1/2} analogous in spirit to the better known one for the Hardy spaces.
In this post we will commence the proof of the Tao-Wright lemma, as promised. We will start by showing how the lemma, which is stated for smooth frequency projections, can be deduced from its discrete variant stated in terms of Haar coefficients (or equivalently, martingale differences with respect to the standard dyadic filtration). This is a minor part of the overall argument but it is slightly tricky so I thought I would spell it out.

Recall that the Tao-Wright lemma is as follows. We write \widetilde{\Delta}_j f for the smooth frequency projection defined by \widehat{\widetilde{\Delta}_j f}(\xi) = \widehat{\psi}(2^{-j}\xi) \widehat{f}(\xi) , where \widehat{\psi} is a smooth function compactly supported in 1/2 \leq |\xi| \leq 4 and identically equal to 1 on 1 \leq |\xi| \leq 2 .


Lemma 1 – Square-function characterisation of L(\log L)^{1/2} [Tao-Wright, 2001]:
Let for any j \in \mathbb{Z}

\displaystyle  \phi_j(x) := \frac{2^j}{(1 + 2^j |x|)^{3/2}}

(notice \phi_j is concentrated in [-2^{-j},2^{-j}] and \|\phi_j\|_{L^1} \sim 1).
If the function {f} is in L(\log L)^{1/2}([-R,R]) and such that \int f(x) \,dx = 0 , then there exists a collection (F_j)_{j \in \mathbb{Z}} of non-negative functions such that:

  1. pointwise for any j \in \mathbb{Z}

    \displaystyle \big|\widetilde{\Delta}_j f\big| \lesssim F_j \ast \phi_j ;

  2. they satisfy the square-function estimate

    \displaystyle  \Big\|\Big(\sum_{j \in \mathbb{Z}} |F_j|^2\Big)^{1/2}\Big\|_{L^1} \lesssim \|f\|_{L(\log L)^{1/2}}.

Continue reading

Representing points in a set in positional-notation fashion (a trick by Bourgain): part II

This is the second and final part of an entry dedicated to a very interesting and inventive trick due to Bourgain. In part I we saw a lemma on maximal Fourier projections due to Bourgain, together with the context it arises from (the study of pointwise ergodic theorems for polynomial sequences); we also saw a baby version of the idea to come, that we used to prove the Rademacher-Menshov theorem (recall that the idea was to represent the indices in the supremum in their binary positional notation form and to rearrange the supremum accordingly). Today we finally get to see Bourgain’s trick.

Before we start, recall the statement of Bourgain’s lemma:

Lemma 1 [Bourgain]: Let K be an integer and let \Lambda = \{\lambda_1, \ldots, \lambda_K \} a set of {K} distinct frequencies. Define the maximal frequency projections

\displaystyle \mathcal{B}_\Lambda f(x) := \sup_{j} \Big|\sum_{k=1}^{K} (\mathbf{1}_{[\lambda_k - 2^{-j}, \lambda_k + 2^{-j}]} \widehat{f})^{\vee}\Big|,

where the supremum is restricted to those {j \geq j_0} with j_0 = j_0(\Lambda) being the smallest integer such that 2^{-j_0} \leq \frac{1}{2}\min \{ |\lambda_k - \lambda_{k'}| : 1\leq k\neq k'\leq K \}.
Then

\displaystyle \|\mathcal{B}_\Lambda f\|_{L^2} \lesssim (\log \#\Lambda)^2 \|f\|_{L^2}.

Here we are using the notation (\mathbf{1}_{[\lambda_k - 2^{-j}, \lambda_k + 2^{-j}]} \widehat{f})^{\vee} in the statement in place of the expanded formula \int_{|\xi - \lambda_k| < 2^{-j}} \widehat{f}(\xi) e^{2\pi i \xi x} d\xi. Observe that by the definition of j_0 we have that the intervals [\lambda_k - 2^{-j_0}, \lambda_k + 2^{-j_0}] are disjoint (and j_0 is precisely maximal with respect to this condition).
We will need to do some reductions before we can get to the point where the trick makes its appearance. These reductions are the subject of the next section.

3. Initial reductions

A first important reduction is that we can safely replace the characteristic functions \mathbf{1}_{[\lambda_k - 2^{-j}, \lambda_k + 2^{-j}]} by smooth bump functions with comparable support. Indeed, this is the result of a very standard square-function argument which was already essentially presented in Exercise 22 of the 3rd post on basic Littlewood-Paley theory. Briefly then, let \varphi be a Schwartz function such that \widehat{\varphi} is a smooth bump function compactly supported in the interval [-1,1] and such that \widehat{\varphi} \equiv 1 on the interval [-1/2, 1/2]. Let \varphi_j (x) := \frac{1}{2^j} \varphi \Big(\frac{x}{2^j}\Big) (so that \widehat{\varphi_j}(\xi) = \widehat{\varphi}(2^j \xi)) and let for convenience \theta_j denote the difference \theta_j := \mathbf{1}_{[-2^{-j}, 2^{-j}]} - \widehat{\varphi_j}. We have that the difference

\displaystyle \sup_{j\geq j_0(\Lambda)} \Big|\sum_{k=1}^{K} ((\mathbf{1}_{[\lambda_k - 2^{-j}, \lambda_k + 2^{-j}]} - \widehat{\varphi_j}(\cdot - \lambda_k)) \widehat{f})^{\vee}\Big|

is an L^2 bounded operator with norm O(1) (that is, independent of K ). Indeed, observe that \mathbf{1}_{[\lambda_k - 2^{-j}, \lambda_k + 2^{-j}]}(\xi) - \widehat{\varphi_j}(\xi - \lambda_k) = \theta_j (\xi - \lambda_k), and bounding the supremum by the \ell^2 sum we have that the L^2 norm (squared) of the operator above is bounded by

\displaystyle \sum_{j \geq j_0(\Lambda)} \Big\|\sum_{k=1}^{K} (\theta_j(\cdot - \lambda_k)\widehat{f})^{\vee}\Big\|_{L^2}^2,

where the summation in {j} is restricted in the same way as the supremum is in the lemma (that is, the intervals [\lambda_k - 2^{-j}, \lambda_k + 2^{-j}] must be pairwise disjoint). By an application of Plancherel we see that the above is equal to

\displaystyle \sum_{k=1}^{K} \Big\| \widehat{f}(\xi) \Big[\sum_{j \geq j_0} \theta_j(\xi - \lambda_k) \Big]\Big\|_{L^2}^2;

but notice that the functions \theta_j have supports disjoint in {j} , and therefore the multiplier satisfies \sum_{j\geq j_0} \theta_j(\xi - \lambda_k) \lesssim 1 in a neighbourhood of \lambda_k , and vanishes outside such neighbourhood. A final application of Plancherel allows us to conclude that the above is bounded by \lesssim \|f\|_{L^2}^2 by orthogonality (these neighbourhoods being all disjoint as well).
By triangle inequality, we see therefore that in order to prove Lemma 1 it suffices to prove that the operator

\displaystyle \sup_{j} \Big|\sum_{k=1}^{K} (\widehat{\varphi_j}(\cdot - \lambda_k) \widehat{f})^{\vee}\Big|

is L^2 bounded with norm at most O((\log \#\Lambda)^2).

Continue reading