Hardy–Littlewood Maximal Function

In mathematics, the Hardy–Littlewood maximal operator ''M'' is a significant non-linear operator used in real analysis and harmonic analysis.

Definition

The operator takes a locally integrable function $f: \R^d \to \mathbb C$ and returns another function Mf : \R^d \to , \infty/math>, where $Mf (x)$ is the supremum of the average of $f$ among all possible balls centered on $x$. Formally,

:$Mf(x)=\sup_ \frac\int_ , f(y), \, dy$,

where , ''E'', denotes the ''d''-dimensional Lebesgue measure of a subset ''E'' ⊂ R^''d'', and $B(x,\,r)$ is the ball of radius, $r>0$, centered at the point $x\in\mathbb^d$.

Since $f$ is locally integrable, the averages are jointly continuous in ''x'' and ''r'', so the maximal function ''Mf'', being the supremum over ''r'' > 0, is measurable.

A nontrivial corollary of the Hardy–Littlewood maximal inequality states that $Mf$ is finite almost everywhere for functions in $L^1$.

Hardy–Littlewood maximal inequality

This theorem of G. H. Hardy and J. E. Littlewood states that ''M'' is bounded as a sublinear operator from ''L^p''(R^''d'') to itself for ''p'' > 1. That is, if ''f'' ∈ ''L^p''(R^''d'') then the maximal function ''Mf'' is weak ''L''¹-bounded and ''Mf'' ∈ ''L^p''(R^''d''). Before stating the theorem more precisely, for simplicity, let denote the set . Now we have:

Theorem (Weak Type Estimate). For ''d'' ≥ 1, there is a constant ''C_d'' > 0 such that for all λ > 0 and ''f'' ∈ ''L''¹(R^''d''), we have:

:$\left , \ \right , < \frac \Vert f\Vert_.$

With the Hardy–Littlewood maximal inequality in hand, the following ''strong-type'' estimate is an immediate consequence of the Marcinkiewicz interpolation theorem:

Theorem (Strong Type Estimate). For ''d'' ≥ 1, 1 < ''p'' ≤ ∞, and ''f'' ∈ ''L^p''(R^''d''),
there is a constant ''C_p,d'' > 0 such that

:$\Vert Mf\Vert_\leq C_\Vert f\Vert_.$

In the strong type estimate the best bounds for ''C_p,d'' are unknown. However subsequently Elias M. Stein used the Calderón-Zygmund method of rotations to prove the following:

Theorem (Dimension Independence). For 1 < ''p'' ≤ ∞ one can pick ''C_p,d'' = ''C_p'' independent of ''d''.

Proof

While there are several proofs of this theorem, a common one is given below, that uses the following version of the Vitali covering lemma to prove the weak-type estimate. (See the article for the proof of the lemma.)

Note that the constant $C=5^d$ in the proof can be improved to $3^d$ by using the inner regularity of the Lebesgue measure, and the finite version of the Vitali covering lemma. See the Discussion section below for more about optimizing the constant.

Applications

Some applications of the Hardy–Littlewood Maximal Inequality include proving the following results:
* Lebesgue differentiation theorem
* Rademacher differentiation theorem
* Fatou's theorem on nontangential convergence.
* Fractional integration theorem

Here we use a standard trick involving the maximal function to give a quick proof of Lebesgue differentiation theorem. (But remember that in the proof of the maximal theorem, we used the Vitali covering lemma.) Let ''f'' ∈ ''L''¹(R^''n'') and

:$\Omega f (x) = \limsup_ f_r(x) - \liminf_ f_r(x)$

where

:$f_r(x) = \frac \int_ f(y) dy.$

We write ''f'' = ''h'' + ''g'' where ''h'' is continuous and has compact support and ''g'' ∈ ''L''¹(R^''n'') with norm that can be made arbitrary small. Then

:$\Omega f \le \Omega g + \Omega h = \Omega g$

by continuity. Now, Ω''g'' ≤ 2''Mg'' and so, by the theorem, we have:

:$\left , \ \right , \le \frac \, g\, _1$

Now, we can let $\, g\, _1 \to 0$ and conclude Ω''f'' = 0 almost everywhere; that is, $\lim_ f_r(x)$ exists for almost all ''x''. It remains to show the limit actually equals ''f''(''x''). But this is easy: it is known that $\, f_r - f\, _1 \to 0$ ( approximation of the identity) and thus there is a subsequence $f_ \to f$ almost everywhere. By the uniqueness of limit, ''f_r'' → ''f'' almost everywhere then.

Discussion

It is still unknown what the smallest constants ''C_p,d'' and ''C_d'' are in the above inequalities. However, a result of Elias Stein about spherical maximal functions can be used to show that, for 1 < ''p'' < ∞, we can remove the dependence of ''C_p,d'' on the dimension, that is, ''C_p,d'' = ''C_p'' for some constant ''C_p'' > 0 only depending on ''p''. It is unknown whether there is a weak bound that is independent of dimension.

There are several common variants of the Hardy-Littlewood maximal operator which replace the averages over centered balls with averages over different families of sets. For instance, one can define the ''uncentered'' HL maximal operator (using the notation of Stein-Shakarchi)

:$f^*(x) = \sup_ \frac \int_ , f(y), dy$

where the balls ''B_x'' are required to merely contain x, rather than be centered at x. There is also the ''dyadic'' HL maximal operator

:$M_\Delta f(x) = \sup_ \frac \int_ , f(y), dy$

where ''Q_x'' ranges over all dyadic cubes containing the point ''x''. Both of these operators satisfy the HL maximal inequality.

See also

* Rising sun lemma

References

* John B. Garnett, ''Bounded Analytic Functions''. Springer-Verlag, 2006
*G. H. Hardy and J. E. Littlewood. ''A maximal theorem with function-theoretic applications''. Acta Math. 54, 81–116 (1930)

*Antonios D. Melas, ''The best constant for the centered Hardy–Littlewood maximal inequality'', Annals of Mathematics, 157 (2003), 647–688
*Rami Shakarchi & Elias M. Stein, ''Princeton Lectures in Analysis III: Real Analysis''. Princeton University Press, 2005
*Elias M. Stein, ''Maximal functions: spherical means'', Proc. Natl. Acad. Sci. U.S.A. 73 (1976), 2174–2175
*Elias M. Stein, ''Singular Integrals and Differentiability Properties of Functions''. Princeton University Press, 1971
*Gerald Teschl
Topics in Real and Functional Analysis
(lecture notes)

{{DEFAULTSORT:Hardy-Littlewood Maximal Function
Real analysis
Harmonic analysis
Types of functions