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The maximal Hilbert transform along nonconvex curves
Journal of Inequalities and Applications volume 2012, Article number: 191 (2012)
Abstract
The Hilbert transform along curves is defined by the principal value integral. The pointwise existence of the principal value Hilbert transform can be educed from the appropriate estimates for the corresponding maximal Hilbert transform. By using the estimates of Fourier transforms and Littlewood-Paley theory, we obtain -boundedness for the maximal Hilbert transform associated to curves , where , P is a real polynomial and γ is convex on . Then, we can conclude that the Hilbert transform along curves exists in pointwise sense.
MSC:42B20, 42B25.
1 Introduction
For , let be a curve in with . To Γ we associate the Hilbert transform which is defined as a principal value integral
where and . Similarly, one can define the corresponding maximal function and the maximal Hilbert transform as
and
The -boundedness for the Hilbert transform and the maximal function above have been well studied by many scholars. See [10] for a survey of results through 1977. More recent results can be found in [1, 2, 4–7].
Appropriate estimates for the maximal Hilbert transform give the pointwise existence of the principal value Hilbert transform. So, we focus on the -bounds for the maximal Hilbert transform in this paper. Let us state some previous theorems to establish the background for our current work. The first result about is the work of Stein and Wainger (see [10]).
Theorem 1.1
-
(A)
If Γ is a two-sided homogeneous curve in , then
-
(B)
Assume that for small t, lies in the subspace spanned by . Then the maximal Hilbert transform
is bounded from to itself, .
For and , Córdoba and Rubio de Francia considered the case and , with the following properties:
-
(i)
γ is biconvex, i.e., is decreasing in and increasing in ;
-
(ii)
has doubling time, i.e., there exists a constant such that ;
-
(iii)
γ is balanced, by which we mean the following: there exists such that for every .
They proved the following theorem in [7].
Theorem 1.2 Under the assumptions (i), (ii) and (iii) on the γ, the maximal Hilbert transform is a bounded operator in for .
In this note, we consider the curve Γ with the form , where is a real-valued polynomial of t in , γ satisfies
Definition 1.3 A function belongs to , if there exists a constant such that for . It is also said that f has doubling time.
For this case, Bez obtained the -boundedness of and in [1].
Theorem 1.4 Suppose that P is a polynomial, γ satisfies (1.1) and . If , , and either (1) is zero, or (2) is nonzero and , then
Moreover, the constant C depends only on p, γ and the degree of P.
Remark 1.5 If , is “more convex” than in some sense, then Γ is “convex” enough for the -boundedness of and . In the case , the linear term of cannot improve the convexity of γ. To obtain the -boundedness for associated operators, one needs to pose additional condition(s) on γ, that is, . For more details, see [5] and [9].
Motivated by Bez’s result above, we obtain the -boundedness for . More precisely, we prove the following theorem.
Theorem 1.6 Suppose that P is a polynomial, γ satisfies (1.1) and . If , and either (1) is zero, or (2) is nonzero and , then
Moreover, the constant C depends only on p, γ and the degree of P.
Remark 1.7 Let . Comparing those conditions for γ in Theorem 1.2, we find that conditions in Theorem 1.6 are stricter. But we should note that may be a nonconvex function.
The convexity of the polynomial P is important for our main result. P has different convexity in different intervals, which suggests that will be decomposed according to the properties of P. The decay of associated multipliers is essential for the proof of Theorem 1.6. This set of techniques originated from the work [1] and [3]. Notice that is a nonlinear operator, Minkowski’s inequality cannot be used as in Section 1 of [1], the linearization method is invoked to treat it. Similarly, the essential Proposition 1.2 in [1] is useless for the maximal Hilbert transform. Littlewood-Paley theory and interpolation theorem are effective tools to treat those problems. Those ideas are due to the contribution of Córdoba, Nagel, Vance, Wainger, Rubio de Francia.
The organization of our paper is as follows. In Section 2, we list some key properties concerning the polynomial and give some lemmas for the proof of the main result. The -estimates for will be proved in Section 3.
2 Preliminaries
Without loss of generality, we suppose that , where . Let be d-complex roots of P ordered as
Let A be a positive constant which will be chosen in Lemma 2.1. Define if it is nonempty for and . Let , then can be decomposed as , where is the interval between two adjacent . It is obvious that is disjoint. Then, we can decompose as
where is the inverse image of a subset I restricted in .
The properties of P on and are important for our proof. The following related lemma can be found in [1] and [3].
Lemma 2.1 There exists a number such that for any and any ,
-
(i)
for ;
-
(ii)
for , for ;
-
(iii)
for ;
-
(iv)
and for , .
The following fact can be induced from the proof of Lemma 2.1 (see [1]), that is, we can choose such that, for ,
Let λ be the doubling constant for , define . Let I be a subset of , and , and are given by
and
For and , we define cones in by
and the corresponding projection operators by . Then, we have the following lemma which is Lemma 1 in [7].
Lemma 2.2 For and , we have
-
(i)
;
-
(ii)
;
-
(iii)
.
The bootstrap argument (see [8]) plays an important role in the proof of the main result, so we present the following well-known result which can be found in [2] and [7].
Lemma 2.3 Suppose that is a sequence of positive operators uniformly bounded in , and is bounded in for , then
for .
3 Proof of Theorem 1.6
Let and be given as in Section 2, then
Note that and are finite sets, it suffices to show that and are -bounded, respectively.
3.1 The -boundedness for
Let be some measurable function from to such that
By Minkowski’s inequality, we can control the -norm of by
Let for some and , then
and
Notice that γ is convex and , so for . Thus,
where is the inverse function of .
(3.1), (3.2) and (3.3) yield
3.2 The -boundedness for
For and , set and define measures by
for . For any , there exists such that . Then
Therefore,
By the -boundedness of (see [1]), it suffices to consider the latter term. Let such that for and for . Write and denote by ⋆ convolution in the first variable. For , the truncated Hilbert transform can be decomposed as
where δ is the Dirac measure in , and is the identity operator. Then, we just need to estimate , and , respectively.
The decay of is important for the boundedness of three maximal operators above. Essentially, estimates for in the following subsection have been proved in [1]. We repeat them just for completeness.
3.2.1 Fourier transform estimates of
Before the proof of Proposition 3.2, we need the following lemma which is Lemma 2.2 in [1].
Lemma 3.1 For all , the function
is singled-signed on .
Proposition 3.2 For and , if , then
Proof For fixed , let , then
Case 1. . If and , for , (2.1) implies
Note that is monotone on , this fact follows from Lemma 3.1. By Van der Corput’s lemma and (3.4), we get .
If γ is even, then . If γ is odd, Lemma 3.1 still holds for , can be considered in the same way. Then, we have
If and . In the same way, for , we have
In the same way as above, we can get
Case 2. . If and , (3.4) still holds for . By integrating by parts,
Essentially, we just need to consider the second term, which can be dominated by
In order to estimate the term , we define , then . By (3.4), for , it is obvious that
On the other hand, for ,
Further, by (2.1), for ,
Thus, combining (3.7), (3.9) and (3.8), we have
For , by (3.4),
Note that can be split into a finite number of disjoint intervals such that is singled-signed on each interval. Suppose that is such an interval and by (2.1), then . So .
If and , (3.6) holds for . The same arguments used above imply
where and are as previous ones. For above, we have
Thus, (3.9) and (3.12) give
For , by (3.12) and (2.1),
can be treated in the same way as in Case 1. Thus, (3.10), (3.11), (3.13) and (3.14) imply
□
3.2.2 -estimates for
The case of even γ By a linear transformation, we have
Note that , then for any ,
where is the Hardy-Littlewood maximal function acting on in the first variable and is given by
Thus, we obtain
If we can show that is -bounded, according to the -boundedness of (see [1]), we will get
So, it suffices to prove the following result.
Lemma 3.3 For , is a bounded operator in , .
Proof We denote by for short, then . Note that there is no root of in , that is, is singled-signed. For , , by (2) of Lemma 2.1, is also singled-signed on . By and the convexity of γ, for . Then, is monotonous on . On the other hand, by Lemma 3.1, for , is monotonous on .
Suppose that is increasing on , then
For , if is increasing on , then, for , is nonnegative and decreasing on . Furthermore, note that
Therefore, for , we have
where M is the Hardy-Littlewood maximal function.
If is decreasing on , write
where denotes the reflection of g. Notice that is nonnegative and decreasing on and . Similarly,
For , and are increasing on and , respectively. Then, is increasing on , that is, . According to (2.1), ; furthermore, for . Therefore, combining the convexity of γ, we have
where .
By the -boundedness of M, we complete the proof of Lemma 3.3. □
The case of odd γ We decompose as , where . Therefore,
For , note that
For , , we have
Similar to the case of even γ, we obtain
For the second term in the right-hand side of (3.16),
Then, we get the estimate
By Lemma 3.3 and the -boundedness for , we just need to prove that
To obtain (3.18), we denote the set by and define projection operators by . Then, we use the following majorization:
where .
We will prove that for and ,
and
(3.19) and interpolation between (3.20) and (3.21) give (3.18).
To prove (3.20), we use the fact , Lemma 3.3 and Lemma 2.3,
For (3.21), by Plancherel theorem,
So, it suffices to show
By (2.1), we have
On the other hand, by (2.1) and the convexity of γ,
By the argument similar to that in the proof of Proposition 3.2, we obtain
Notice that . (3.23) and (3.24) imply
For , by (3.25) and the convexity of γ, we have
and
This ends the proof of (3.22).
3.2.3 -estimates for
For fixed , , where are positive integers less than 5. Then
Therefore,
For operators , by Lemma 2.2 and Lemma 2.3, we have
where we have used the fact that .
Finally, Lemma 2.2, (3.26) and (3.27) give
3.2.4 -estimates for
The last term can be decomposed as
where
Set , then, . By the -boundedness of and Lemma 2.2, we obtain
On the other hand, for , Plancherel theorem and Proposition 3.2 imply
Interpolation between (3.29)-(3.30) and (3.28) gives
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Acknowledgements
The author was supported by Doctor Foundation of Henan Polytechnic University (Grant: B2011-034).
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Liu, H. The maximal Hilbert transform along nonconvex curves. J Inequal Appl 2012, 191 (2012). https://doi.org/10.1186/1029-242X-2012-191
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DOI: https://doi.org/10.1186/1029-242X-2012-191