Hardy-Littlewood maximal function

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. It takes a function "f" (a complex-valued and locally integrable function)

: f:mathbb{R}^{d} ightarrow mathbb{C}

and returns a second function

: Mf ,

that tells you, at each point xin mathbb{R}^{d}, how large the average value of f can be on balls centered at that point. More precisely,

: Mf(x)=sup_{r>0}frac{1}{m_d(B_{r}(x))}int_{B_{r}(x)} |f(y)| dm_{d}(y)

where

: B_{r}(x)={yin mathbb{R}^{d}: ||y-x||

is the ball of radius r centered at x), and m_{d} denotes the d-dimensional Lebesgue measure.

The averages are jointly continuous in "x" and "r", therefore the maximal function "Mf", being the supremum over "r" > 0, is measurable. It is not obvious that "Mf" is finite almost everywhere. This is a corollary of the Hardy-Littlewood maximal inequality

Hardy-Littlewood maximal inequality

This theorem of G. H. Hardy and J. E. Littlewood states that M is bounded as a sublinear operator from the "L""p" space

: L^{p}(mathbb{R}^{d}), ; p > 1

to itself. That is, if

:fin L^{p}(mathbb{R}^{d}),

then the maximal function "Mf" is weak "L"1 bounded and

:Mfin L^{p}(mathbb{R}^{d}).

More precisely, for all dimensions "d" &ge; 1 and 1 < "p" &le; &infin;, and all "f" &isin; "L"1(R"d"), there is a constant "Cd" > 0 such that for all "&lambda;" > 0 , we have the "weak type"-(1,1) bound:

: m_{d}{xinmathbb{R}^{d}: Mf(x)>lambda}{lambda}||f||_{L^{1}(mathbb{R}^{d})} .

This is the Hardy-Littlewood maximal inequality.

With the Hardy-Littlewood maximal inequality in hand, the following "strong-type" estimate is an immediate consequence of the Marcinkiewicz interpolation theorem: there exists a constant "A""p,d" > 0 such that

: ||Mf||_{L^p(mathbb{R}^{d})}leq A_{p,d}||f||_{L^p(mathbb{R}^{d})}.

Proof

While there are several proofs of this theorem, a common one is outlined as follows: For p=infty, (see Lp space for definition of L^{infty}) the inequality is trivial (since the average of a function is no larger than its essential supremum). For 1 < "p" < &infin;, one proves the weak bound using the Vitali covering lemma.

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.

Discussion

It is still unknown what the smallest constants A_{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, we can remove the dependence of A_{p,d} on the dimension, that is, A_{p,d}=A_{p} for some constant A_{p}>0 only depending on the value p. It is unknown whether there is a weak bound that is independent of dimension.

References

*John B. Garnett, "Bounded Analytic Functions". Springer-Verlag, 2006
*Rami Shakarchi & Elias M. Stein, "Princeton Lectures in Analysis III: Real Analysis". Princeton University Press, 2005
*Elias M. Stein, "Maximal functions: spherical means", Proc. Nat. Acad. Sci. U.S.A. 73 (1976), 2174-2175
*Elias M. Stein & Guido Weiss, "Singular Integrals and Differentiability Properties of Functions". Princeton University Press, 1971


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