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
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 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
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 Proof of Lemma 3. This and equality (
3) yield that
Since
is non-decreasing, we can write
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
For
, we have that
The construction of the sequences
and
yields
and
Now, we set
.
and
The sets
and
are pairwise disjoint intervals (
), and we have
(see inequalities (
15) and (
16) as well). Taking into account that
we conclude that
Summarizing our results in inequalities (
13) and (
17), we complete the proof. □
Theorem 1. (a) If the sequence is monotone non-increasing (in sign ) for any fixed n, then there exists a positive constant c such thatholds 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
Applying equality (
23) and Abel’s transformation, we obtain
Analogously, we transform the expression
. Inequality (
7) yields
and
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 thatholds 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).
Inequalities (
26)–(
28) complete the proof of Theorem 2. □
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
For sequence
we apply the equality (
5), and we obtain
Applying Abel’s transform and inequalities (
7) and (
33), we learn that
Analogously, we can prove 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
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
Combining (
31), (
34)–(
36), (
39) and (
40), we complete the proof. □
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 conditionbe 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 thatand 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
Inequalities (
43) and (
44) yield that
Consequently, taking the supremum for all
, we have that
From inequality (
19), we have that
Equality (
3) and condition (
43) yield that
By Theorem 3 and (
44), we obtain the following inequality
Since the sequence
is non-decreasing, we write
and
By inequality (
42), we obtain
and
Combining (
45)–(
49), we have that
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
Theorem 1, conditions (
42) and (
43) yield that
We apply Theorem 3, inequality (
48), conditions (
42) and (
43); we have that
Combining (
50)–(
54), we complete the proof of Theorem 4. □
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 thatholds for all . Moreover, the maximal operator is of weak type . That is, there exists a positive constant c such thatholds for all , . Proof of Theorem 5. Since (see (
20))
and
we complete the 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])
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 thatholds for all , . Proof of Lemma 4. Since
and
from (
58), we have
and consequently, by (
56) and (
55), we complete the proof of Lemma 4. □
Theorem 6. Let be a strictly monotone increasing sequence. Let be a monotone non-decreasing sequence of non-negative numbers for every fixed . Ifholds, then there exists a positive constant c such thatholds for all , . Proof of Theorem 6. We obtain
where
. Since
we conclude that
Consequently, we can write
We combine (
60), (
61) and (
62) in order to 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]).
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,