Abstract
The presented paper discusses the matrix summability of the Walsh–Fourier series. In particular, we discuss the convergence of matrix transforms in space and in space in terms of modulus of continuity and matrix transform variation. Moreover, we show the sharpness of our result. We also discuss some properties of the maximal operator of the matrix transform of the Walsh–Fourier series. As a consequence, we obtain the sufficient condition so that the matrix transforms of the Walsh–Fourier series are convergent almost everywhere to the function f. The problems listed above are related to the corresponding Lebesgue constant of the matrix transformations. The paper sets out two-sides estimates for Lebesgue constants. The proven theorems can be used in the case of a variety of summability methods. Specifically, the proven theorems are used in the case of Cesàro means with varying parameters.
Keywords:
Walsh system; matrix transforms; Cesaro mean; logarithmic means; martingale transform; weak type inequality; convergence in norm; almost everywhere convergence and divergence MSC:
42C10
1. Introduction
The issues of summability of Fourier series have been studied by many authors. In particular, different methods of summabilities are known in the literature. The summability methods are concerned with matrix transformations of partial sums of Walsh–Fourier series. It is well known that the partial sums of Walsh–Fourier series are not convergent in the norm both in the classes of continuous functions and in classes of integrable functions [1] (Chapter 4). It is also known that there is an integral function whose Walsh–Fourier series is divergent at all points [1,2].
An example of matrix transformation is the Fejér or arithmetic mean. In this case, there is a matrix transformation where the elements of each row of the corresponding triangular matrix are constants. As a result of such a transformation, we obtain a new sequence that can be convergent in the space and , and is also convergent almost everywhere for all integrable functions [1,2].
Another example of matrix summability is summability by the Riesz’s logarithmic method (). The new sequence has “good” properties (convergence in the space and as well as convergence almost everywhere for all integrable functions).
From the above, we can assume that if the matrix transformations whose first n element of the nth row represents a non-increasing sequence, then the new sequence obtained as a result of such a transformation is characterized by “good” properties (see estimation (29), Theorem 5 and Corollary 4).
Examples of matrix transformations whose first n element of the nth row represents an increasing sequence are:
- summability (), where
- Nörlund logarithmic summability ();
- Cesàro means with varying parameters ( as ).
In the case for summability (), it is known that the new sequence obtained by matrix transformation () has “good” properties [1,2,3]. On the other hand, if () or ( as ), then the new sequences are not characterized by “good” properties [4,5].
Therefore, the sequences obtained by matrix transformations can have “good” or “bad” properties. The article sets out the necessary and sufficient conditions for the sequence obtained as a result of the matrix transformation to be convergence in the space and (see Theorem 3, Corollarys 2 and 3, Theorem 4).
Sufficient conditions have been established for the sequence obtained as a result of the matrix transformation to be almost everywhere convergent (see Theorem 6).
Note that the behavior of the sequences obtained as a result of the matrix transformation depends on two-sided estimations of the integral norm (Lebesgue’s constant) of the corresponding kernel of the matrix transformation (see Theorem 1).
The theorems can be used for various methods of summability. At the end of the article, the theorems are used in the case of Cesàro means with varying parameters; this new result improves the theorem of Gát and Abu Joudeh [6].
2. Definitions
Let denote the set of positive integers, . By a dyadic interval in , we mean one of the form for some , . Given and let denote the dyadic interval of length which contains the point x. We use also the notation . Let
be the dyadic expansion of , where or 1, and if x is a dyadic rational number, we choose the expansion which terminates in s. We also use the following notation
For any given , it is possible to write n uniquely as
where or 1 for . This expression will be called the binary expansion of n and the numbers will be called the binary coefficients of n. Let us denote for , , that is
Let us set the definition of the nth Walsh–Paley function at point as:
Let us denote by ∔ the logical addition on . That is, for any and
Let us define the binary operator by
It is well known (see [1], p. 5) that
The Walsh–Dirichlet kernel is defined by
Recall that [1,2]
where is the characteristic function of the set E,
The partial sums of Walsh–Fourier series of a function are defined as follows: and
where .
3. Triangular Matrix Transforms
Let be an infinite triangular matrix satisfying the following conditions:
- (a)
- (b)
- (c)
We define the nth triangular matrix transform of the Walsh–Fourier series by
The triangular matrix transform kernels are defined by
We have
Let us define the following matrices
Then, equality (6) can be written as follows
The Fejér means and kernels are denoted by
where
It is easily seen that
It is well known that norms of Fejér kernels are uniformly bounded, that is
Yano [7] estimated the value of c, and he gave . Recently, in paper [8], it was shown that the exact value of c is .
4. Auxiliary Results
This section will mention the definitions and notations from the book [1] (Chapter 3).
For each , let represent the -algebra generated by the collection of dyadic intervals . Thus, every element of is a finite union of intervals of the form or an empty set.
Let represent the collection of -measurable functions on . By the Paley Lemma [1] (Chapter 1, p. 12), coincides with the collection of Walsh polynomials of order less than .
A sequence of functions is called a dyadic martingale if each belongs to and
Let denote the collection of sequences which satisfy for and
For a given and , the martingale transform of f is defined by
where for . The maximal martingale transform is defined by
The next Lemma plays an important role in our paper and methods [1] [page 97].
Lemma 1
(Schipp, Simon, Wade and Pál [1]). Let and . Then, the operator is of weak type (1,1). That is, there exists an absolute constant C such that
5. Kernel Representation and -Norm of the Matrix Transform Kernels
First, we start with a useful decomposition of the kernel function . We use the next notation in the proof.
and
We note that .
Lemma 2.
Let . Then, the next decomposition of the matrix transform kernel holds:
Proof of Lemma 2.
For any positive integer n, we write that
Then, from (2), we have that
For , we have
Hence,
This completes the proof of Lemma 2. □
We introduce the notation
Before we discuss the -norm of the kernels , we prove the following lemma.
Lemma 3.
Let be a non-decreasing (in sign ) bounded sequence of positive real numbers . Let the kernel of martingale transform be defined by
Then
Proof of Lemma 3.
We write that
This and equality (3) yield that
Since is non-decreasing, we can write
This yields
Now, we show the lower estimate for . We use the construction in the book ([1], p. 35). Let us choose the strictly monotone increasing sequences and () such that
It is easy to see that
holds. We define the nature number by
Let us set the sets
For , we have that
The construction of the sequences and yields
and
That is, we obtain that
Now, we set .
and
Theorem 1.
(a) If the sequence is monotone non-increasing (in sign ) for any fixed n, then there exists a positive constant c such that
holds for all .
(b) If the sequence is monotone non-decreasing (in sign ) for any fixed n, then
Proof of Theorem 1.
First, let the sequence be monotone non-increasing (in sign ). For the kernel , we apply Abel’s transformation
Inequality (7) implies that
Second, let the sequence be monotone non-decreasing (in sign ). Theorem 2 yields that
Applying Lemma 3 with setting , we obtain
At last, we discuss the norm . In case , we write that
For , we have that
It is known that
Applying equality (23) and Abel’s transformation, we obtain
Thus,
Theorem 1 is proved. □
6. Convergence in Measure of Matrix Transform of Walsh–Fourier Series
Theorem 2.
Let be a monotone non-decreasing (or monotone non-increasing) sequence for any fixed n. Then, there exists a positive constant c such that
holds for all and .
Proof of Theorem 2.
First, let the sequence be monotone non-increasing (in sign ). Since, by Theorem 1, we write that
(for more details, see [1,2]). We immediately learn that the operator is of weak type (1,1).
Second, let the sequence be monotone non-decreasing (in sign ). Lemma 2 yields that
Since is a martingale transform with coefficients , we apply Lemma 1. This lemma gives immediately that the operator is of weak type . That is, there exists a positive constant c such that
holds for all .
For the operator , we apply inequality (25) and write that
(for more details, see [1,2]). That is, the operator is of weak type (1,1).
Theorem 2 implies that the following is valid.
Corollary 1.
Let be a monotone non-decreasing (or monotone non-increasing) sequence for any fixed n. Then, for all in measure as .
Remark 1.
In the case that the sequence is not increasing for any fixed n, below, more is proved. In particular, the weak type inequality for the maximal operator is proved (see Theorem 5).
7. Convergence in -Norm and -Norm
Let represent the collection of functions f which are continuous at every dyadic irrational, continuous from the right on , and have a finite limit from the left on , all this in the usual topology.
Set . Let us denote by the usual Lebesgue spaces on with the corresponding norm (). Let be either or with the corresponding norm denoted by . The modulus of continuity, when , and the integrated modulus of continuity, while are defined by
In this section, we discuss the convergence of matrix transforms in space and in in terms of modulus of continuity and matrix transform variation. Moreover, in Theorem 4, we show the sharpness of our result.
For non-negative integer n, the variation of n is defined by
(see [1], p. 34). Motivated by this definition for the monotone non-decreasing sequence (in sign ), we introduce the matrix transform variation of n by
For the convenience of the reader, the main theorems of this section will be formulated first, and the proofs will be given below.
Theorem 3.
Let and be a sequence of non-negative numbers.
(a) If the sequence is monotone non-increasing (in sign ), then
(b) If the sequence is monotone non-decreasing (in sign ), then
Proof of Theorem 3.
We carry out the proof of Theorem 3 for space . The proof for is similar and even simpler. Keeping in mind that , we write that
First, we discuss the expression . We write that
It is easily seen that . Applying generalized Minkowski’s inequality, we have
For sequence , we learn immediately that
Analogously, we can prove that
That is, we have that
For sequence we apply the equality (5), and we obtain
Analogously, we can prove that
That is, we have that
The estimation of the is analogous to the estimation of the , and we have
Now, we discuss the integral . We apply equality (23), Abel’s transformation and inequality (7). We have that
For sequence , we learn that
For sequence , we write
That is, we have that
in both cases (a) and (b).
At last, we discuss the expression .
It can be proved that . By generalized Minkowski’s inequality, we have that
Equality (5) and Abel’s transformation yield that
Inequality (7) gives
For sequence , we write
For sequence , we have
That is, for a monotone non-increasing sequence (in sign ), we have
and for a monotone non-decreasing sequence (in sign ),
For a monotone non-increasing sequence (in sign ), we proved that
For a monotone non-decreasing sequence (in sign ), we reached that
Corollary 2.
Let and be a strictly monotone increasing sequence. Let be a monotone non-decreasing sequence of non-negative numbers (in sign ). Let the condition
be satisfied. Then, the subsequence converges in the norm of the space .
Corollary 3.
Let and be a monotone non-decreasing sequence of non-negative numbers (in sign ). Let the sequence be such that the next condition holds
Then, the subsequence converges in the norm of the space .
The next theorem proofs the sharpness of condition (41).
Theorem 4.
Let the sequences be monotone non-decreasing (in sign ) for all . Let be a sequence of natural numbers such that
Then, there exists a sequence and a function such that
and
Proof of Theorem 4.
Let the sequence be monotone non-decreasing (in sign ) for all . Then, condition
yields that there exists a sequence such that the following two conditions hold
and
First, let us discuss . Now, we set
It is easy to check that . Let us calculate . We set , and we learn that
Consequently, taking the supremum for all , we have that
We can write
For
From inequality (19), we have that
By Theorem 3 and (44), we obtain the following inequality
Since the sequence is non-decreasing, we write
and
Second, we discuss the case . Let the condition (42) and (43) hold as well. We define the function h by
where
It is easily seen that . Now, we calculate the modulus of continuity in . Let then for , we obtain
Applying condition (43), we obtain
That is,
It is easily seen that
8. Almost Everywhere Convergence of Matrix Transforms of Walsh–Fourier Series
Let us set . The maximal function is defined by
It is known that ([1], p. 81) there exists a positive constant c such that
holds for all and .
We define the maximal operator of the linear transforms generated by the sequences
In this section, we discuss some properties of the maximal operator . As a consequence, we learn that the matrix transforms of the Walsh–Fourier series converge almost everywhere to the function f for all integrable functions. This result is reached with different monotonity conditions.
First, we state the boundedness of the maximal operator of the linear transforms defined by monotone non-increasing sequences.
Theorem 5.
Let be monotone non-increasing sequences of non-negative numbers (in sign ) for all . Then, the maximal operator is bounded from the Lebesque space to the Lebesque space for all . That is, there exists a positive constant which depends only on p such that
holds for all . Moreover, the maximal operator is of weak type . That is, there exists a positive constant c such that
holds for all , .
Proof of Theorem 5.
By the well-known density argument due to Marcinkiewicz and Zygmund [9], the next corollary holds.
Corollary 4.
Let be a monotone non-increasing sequence of non-negative numbers (in sign ) for all and . Then
Now, we consider the following maximal operator
We prove that the maximal operator is of weak (1,1) type. That is, there exists a positive constant c such that
holds for all , . For this, it is enough to prove that the operator is quasi-local and bounded from the space to the space (see [1]). The boundedness immediately follows from (7). Now, we prove the quasi-locality. In particular, let such that for some dyadic interval . Then, we show that there exists a positive constant c such that the next inequality
holds. It can be supposed that . If , then
Consequently, can be supposed.
It is known that (see Gát [10])
Then, we have
Hence, (56) is proved.
From (24), we can write
Let us set
It is easy to see that
In order to prove Theorem 6, we need the following lemmas.
Lemma 4.
Let be a monotone non-decreasing sequence of non-negative numbers for every fixed . The operator is of weak type . That is, there exists a positive constant c such that
holds for all , .
Proof of Lemma 4.
We can write
Theorem 6.
Let be a strictly monotone increasing sequence. Let be a monotone non-decreasing sequence of non-negative numbers for every fixed . If
holds, then there exists a positive constant c such that
holds for all , .
Proof of Theorem 6.
We have (see (9))
We obtain
where . Since
we conclude that
Consequently, we can write
By Lemma 4, we obtain
Theorem 6 is proved. □
Let us define for positive real numbers K the subset of natural numbers by
The next corollary follows from Theorem 6 by the well-known density argument due to Marcinkiewicz and Zygmund [9].
Corollary 5.
Let be a monotone non-decreasing sequence of non-negative numbers for every fixed and . Then, almost everywhere provided that and .
9. Application: Cesàro Means with Varying Parameters of Walsh–Fourier Series
The theorems can be used for various methods of summability. In this section, the application of the theorems proved above to Cesàro means with varying parameters will be presented.
The means of the Walsh–Fourier series of the function f is given by
where
for any . The kernel is defined by
We shall need the following Lemma (see [11]).
Lemma 5.
Let . Then
The idea of Cesàro means with variable parameters of numerical sequences is due to Kaplan [12], and the introduction of these means of Fourier series is due to Akhobadze [11].
The almost everywhere convergence of the subsequence of Cesàro means with variable parameters has been studied by the following authors: Abu Joudeh and Gát [6], Gát and Goginava [13,14], Weisz [15].
Let Then, from (63), we have
Hence, from Corollary 5, we obtain
Theorem 7
(see [14]). Suppose that . Let . Then, almost everywhere provided that and .
Now, we consider the rate of convergence of the Cesàro means with varying parameters of Walsh–Fourier series. Since
and (see Lemma 5)
from Theorem 3, we have
Theorem 8.
Let and . Then,
Author Contributions
Investigation, U.G. and K.N.; Methodology, U.G.; Writing—original draft, U.G.; Writing—review & editing, K.N. All authors have read and agreed to the published version of the manuscript.
Funding
The authors are very thankful to United Arab Emirates University (UAEU) for the Start-up Grant 12S100.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Schipp, F.; Wade, W.R.; Simon, P. Walsh Series: An Introduction to Dyadic Harmonic Analysis; Adam Hilger, Ltd.: Bristol, UK, 1990; p. x+560. [Google Scholar]
- Golubov, B.; Efimov, A.; Skvortsov, V. Walsh Series and Transforms: Theory and Applications; Springer Science and Business Media: Berlin, Germany, 1991; Volume 64. [Google Scholar] [CrossRef]
- Weisz, F. Mathematics and Its Applications; Summability of Multi-Dimensional Fourier Series and Hardy Spaces; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2002; Volume 541, p. xvi+332. [Google Scholar] [CrossRef]
- Gát, G.; Goginava, U. Uniform and L-convergence of logarithmic means of Walsh-Fourier series. Acta Math. Sin. 2006, 22, 497–506. [Google Scholar] [CrossRef]
- Gát, G.; Goginava, U. On the divergence of Nörlund logarithmic means of Walsh-Fourier series. Acta Math. Sin. 2009, 25, 903–916. [Google Scholar] [CrossRef]
- Abu Joudeh, A.A.; Gát, G. Convergence of Cesáro means with varying parameters of Walsh-Fourier series. Miskolc Math. Notes 2018, 19, 303–317. [Google Scholar] [CrossRef]
- Yano, S. On Walsh-Fourier series. Tohoku Math. J. 1951, 3, 223–242. [Google Scholar] [CrossRef]
- Toledo, R. On the boundedness of the L1-norm of Walsh-Fejér kernels. J. Math. Anal. Appl. 2018, 457, 153–178. [Google Scholar] [CrossRef]
- Marcinkiewicz, J.; Zygmund, A. On the summability of double Fourier series. Fundam. Math. 1939, 32, 122–132. [Google Scholar] [CrossRef] [Green Version]
- Gát, G. Pointwise convergence of the Cesàro means of double Walsh series. Ann. Univ. Sci. Budapest. Sect. Comput. 1996, 16, 173–184. [Google Scholar]
- Akhobadze, T. On the convergence of generalized Cesàro means of trigonometric Fourier series. II. Acta Math. Hungar. 2007, 115, 79–100. [Google Scholar] [CrossRef]
- Kaplan, I.B. Cesàro means of variable order. Izv. Vysš. Učebn. Zaved. Matematika 1960, 1960, 62–73. [Google Scholar]
- Gát, G.; Goginava, U. Maximal operators of Cesàro means with varying parameters of Walsh-Fourier series. Acta Math. Hungar. 2019, 159, 653–668. [Google Scholar] [CrossRef]
- Gát, G.; Goginava, U. Almost everywhere convergence and divergence of Cesàro means with varying parameters of Walsh–Fourier series. Arab. J. Math. 2021, 1–19. [Google Scholar] [CrossRef]
- Weisz, F. Cesàro and Riesz summability with varying parameters of multi-dimensional Walsh-Fourier series. Acta Math. Hungar. 2020, 161, 292–312. [Google Scholar] [CrossRef]
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