1. Introduction
Matrix transformations of sequence spaces constitute a classical and well-developed topic in summability theory and functional analysis. Given an infinite matrix
, such transformations describe linear operators acting on sequences via series of the form
and their boundedness properties characterize how different modes of convergence and summability are preserved under linear mappings. Significant contributions in this direction include the works of Toeplitz, Lorentz, Maddox, and Nanda, among others [
1,
2,
3,
4,
5].
In many applied and computational contexts, one naturally encounters two-dimensional (or, more generally, multidimensional) discrete data, such as images and signals sampled on a grid, finite-difference/finite-element discretizations of partial differential equations, and matrices of coefficients arising in bilinear transforms. In such settings the behavior of discrete operators is governed by their action on double sequences rather than on single sequences. This motivates a systematic study of the summability and stability properties of operators acting on double sequences, particularly matrix transformations between double sequence spaces with nonuniform (position-dependent) growth conditions.
In recent decades, this theory has been extended to generalized sequence spaces with variable exponents and to almost convergent sequences. However, the corresponding results for double sequences and, in particular, for matrix transformations acting between generalized almost convergent double sequence spaces, remain relatively limited. The present paper contributes to this line of research by providing complete characterizations of four-dimensional matrix transformations between such spaces.
The concept of almost convergence, introduced by Lorentz ([
2]), has played an important role in summability theory and the study of sequence spaces. Unlike ordinary convergence, almost convergence captures limiting behavior through uniform Cesàro-type averages and allows meaningful limits for sequences that fail to converge in the classical sense. Over the years, this notion has been extended and refined in various directions, including generalized almost convergence and matrix transformations between sequence spaces.
Intuitively, the ‘almost limit’ of a double sequence is obtained by averaging over growing rectangular blocks in a way that is uniform with respect to shifts in the averaging window. Convergence of these two-dimensional Cesàro means expresses a robustness of the limit under local perturbations and translations of the indexing grid; thus, almost convergence detects a global averaged stability rather than pointwise behavior. In formulas, the means (defined below) serve exactly this averaging purpose: the requirement that converge uniformly in the shift indices formalizes the idea that the averaged value is insensitive to finite local translations.
Parallel to these developments, the study of double sequences has gained increasing attention due to their relevance in approximation theory, numerical analysis, summability methods, and multidimensional functional analysis. Double sequences naturally arise, for example, in discretizations of functions of several variables, in iterative schemes, and in the study of series and transforms indexed by multiple parameters. Consequently, extending convergence concepts from single sequences to double sequences is both natural and necessary.
Generalized almost convergence for double sequences provides a flexible framework that unifies several summability methods and allows for finer control over limiting processes. In particular, it enables the study of convergence behavior under multidimensional averaging procedures and matrix transformations that cannot be captured by classical convergence alone. Matrix transformations play a central role in this context, as they provide a powerful tool for describing how summability properties are preserved or altered under linear operators.
The aim of this paper is to study matrix transformations between spaces of generalized almost convergent double sequences and double sequence spaces with variable exponents. Extending earlier results for single sequences, we establish the necessary and sufficient conditions under which infinite matrices define bounded linear operators between these spaces. The obtained results generalize previous theorems by the authors to the two-dimensional setting.
To characterize matrix transformations involving almost convergent (to 0) sequences with powers in [
6], the authors used matrix transformations involving convergent (to 0) sequences with powers mainly considered in [
7]. To characterize matrix transformations involving almost convergent (to 0) double sequences with powers, we will use the mentioned results for single almost convergent (to 0) sequences with powers; matrix transformations involving convergent (to 0) double sequences with powers (see [
8,
9]), and the reduction method of matrix maps from a sequence space to a double sequence space to matrix maps between sequence spaces as described in [
10].
First, we introduce the necessary notions and notation for the paper. We begin with notions and notation pertaining to sequence spaces.
Let
Y be two nonempty subsets of the space
of all complex (or real) sequences, and let
be an infinite matrix of complex (or real) numbers. For every
and every
, we write
We say that
if and only if
whenever
. One can see that for a given
, for
to exist for every
, the sequence
must belong to
Let
be a sequence of strictly positive numbers. The following variable exponent spaces were defined by Maddox ([
3]) and Nakano ([
11]):
When all terms of are constant and equal to , we have , , , and , where , , c, are the spaces of p-summable, bounded, convergent, and null sequences, respectively.
Note that (Theorem 6 in [
12], Theorem 2 in [
13])
and (Theorem 2 in [
14])
Now we introduce the notions and notation for double sequence spaces.
Let e be the double sequence with all elements equal to 1, and let be the double sequence with the -th element equal to 1 and all others equal 0 . By an index sequence , we mean an increasing sequence of integers.
A double sequence
of real or complex numbers is said to converge to the limit
a in Pringsheim’s sense (shortly,
p-converge to
a) if
Let denote the linear space of all double sequences. Linear subspaces of are called double sequence spaces.
For any notion of convergence
, the space of all
-convergent double sequences will be denoted by
and the limit of a
-convergent double sequence
x by
−
. The sum of a double series
is defined by
whenever
. In the case of regular convergence, we skip the prefix
-. We consider the following double sequence spaces:
where
Given a double sequence space
E, we define its
- and
-dual by
where
. For other notions and notations in the area of double sequences, we refer the reader to [
15].
In [
16], the authors showed that
where
It can be easily shown that
if and only if
, and
if and only if
. Since
, it follows that
Let
be a double sequence of strictly positive numbers. We consider the following double sequence spaces with powers:
Most of these spaces were defined and studied by Gökhan and Çolak in [
17,
18,
19]. In the proofs, we will use the representation ([
8]):
In [
8], the authors verified that
where
and in [
18] that
Let
be any four-dimensional scalar matrix and let
be some convergence notion for double sequences. We define
The map
is called a matrix map of type
. We use the notation
if and only if
A is a matrix map of type
and
whenever
. If
(for example,
X is solid), we use the notation
.
In addition, if
X is a double sequence space,
Y is a single sequence space and
is a three-dimensional matrix, we use the notation
if and only if
and
whenever
. As before if
(for example if
X is solid), we use the notation
.
If
X is a sequence space,
Y is a double sequence space and
is a three-dimensional matrix, we use the notation
if and only if
and
whenever
.
We set
and note that
where
We consider generalizations of the spaces of all almost convergent double sequences defined by Móricz and Rhoades [
20]:
and of all almost convergent to 0 double sequences
with indexes:
and
The following inclusions hold for these spaces.
Lemma 1. Let be bounded positive double sequences such that . Then, the following apply:
- (a)
.
- (b)
.
Proof. We prove part (b); part (a) follows by the same argument with
. Let
and let
l be a number such that
Then
for sufficiently large
, so
for sufficiently large
, and since
, we have
□
As in the case of single sequences, almost every convergent double sequence is bounded ([
20]). The inclusion
does not hold in general. Set
,
, and
.
. Then
. The following lemma demonstrates that
holds when
p is bounded.
Lemma 2. Let p be a bounded positive double sequence. Then .
Proof. Let
and
. Then we also have
, where
. Hence, by Lemma 1,
. Therefore, by Theorem 1 (ii) in [
18],
. □
In [
6], we characterized the classes of two-dimensional matrices
where
and
.
In this paper, we will generalize these results to the corresponding double sequence spaces and four-dimensional matrices. More precisely, we will give the conditions for the classes of matrices
where
and
Although the present work is mainly theoretical, the obtained results are closely related to several topics in matrix analysis and numerical linear algebra (see, for example, [
21,
22]). In particular, condition numbers quantify the sensitivity of solutions of linear systems and inverse problems with respect to data perturbations, which is directly connected to the boundedness and stability properties of the associated operators (see [
23,
24]). In this sense, infinite matrix transformations acting between generalized almost convergent double sequence spaces can be viewed as abstract models of discrete operators arising in numerical approximations, where stability plays a central role.
Moreover, generalized inverses, including the Moore–Penrose inverse and its weighted variants, as well as regularization techniques such as Tikhonov regularization, are fundamental tools in the analysis of ill-posed problems. The matrix transformation results established in this paper may, therefore, support further investigations of stability and robustness in regularized inverse problems.
It is worth noting that several results related to the summability and convergence of sequences can also be formulated within the general framework of ideal convergence (see [
25] for the definition). While this approach provides a broad and unifying perspective, the present work focuses on generalized almost convergence, which is particularly well suited for obtaining explicit characterizations of infinite matrix transformations. This setting allows us to derive concrete operator conditions and matrix criteria that are less transparent in a purely ideal-theoretic formulation.
Furthermore, although the present study is connected to earlier works on almost convergence and matrix transformations, it is not merely a marginal modification of known results. The extension to generalized almost convergent double sequence spaces and to four-dimensional matrix transformations introduces substantial technical and conceptual differences that do not follow directly from the one-dimensional setting studied in [
6]. In particular, the interaction between two-dimensional index structures and higher-dimensional matrices leads to new boundedness conditions and transformation criteria that do not follow directly from the one-dimensional setting.
2. Main Results
In what follows, we assume that
and
are bounded double sequences of strictly positive numbers. The boundedness of the exponent sequences
,
is essential, since otherwise the set of almost convergent (to 0) double sequences with powers may fail to be linear. We also follow the order of the results in [
6].
Example 1. Let and consider the constant double sequence defined by for all . Then for every , we haveSince as , it follows thatuniformly in . Hence, . On the other hand, for the sequence , we obtain , and thereforeThus, . Consequently, when the exponent sequence is unbounded, the space is not closed under scalar multiplication and therefore need not be linear. To characterize matrices in
, we use the characterizations of matrices in
and
(Theorems 2.6 and 2.4 in [
9], respectively).
Proposition 1 (Theorem 2.6 in [
9])
. if and only if the following conditions hold:- (i)
- (ii)
- (iii)
- (iv)
such that for
- (v)
such that for
- (vi)
- (vii)
Proposition 2 (Theorem 1 in [
9])
. if and only if the following conditions hold:- (i)
- (ii)
- (iii)
- (iv)
such that for
- (v)
such that for
- (vi)
- (vii)
.
Now, using these propositions, we find conditions for a matrix to map to . For clarity, the conditions appearing in the characterizations are grouped into four types:
- (A)
almost convergence-type conditions,
- (C)
convergence-type conditions,
- (B)
boundedness-type conditions (including restrictions on the support of the matrix entries),
- (M)
modular control conditions.
Theorem 1. is in if and only if the following conditions hold:
- (A1)
;
- (B1)
- (B2)
- (B3)
such that for
- (B4)
such that for
- (M1)
- (M2)
Proof. Necessity. (A1) follows since . (B1), (B2) and (M1) follow since .
Since by Lemma 2 implies , (B3) and (B4) follow from Proposition 2.
To prove (M2), suppose on the contrary that (M2) does not hold; that is,
Then we can choose index sequences
and sequences of integers
,
such that
We consider the matrix
with
and
for
. Since
we get
Hence, by Proposition 1, the matrix C is not in , so there exists such that .
Since
then
which implies
, and the contradiction follows.
Sufficiency. Suppose that the conditions (A1), (B1)–(B4) and (M1)–(M2) are satisfied; we shall prove that .
From (M2), it follows that
So the matrix satisfies the condition (vii) in Proposition 2 for .
From (A1), (B1)–(B4) and (M1), it follows that the matrix also satisfies the conditions (i)–(vi) in Proposition 2 for .
Hence, the matrix
for every
. Therefore, by Proposition 2 we have
Suppose on the contrary that
for some
. So
Hence, we can find index sequences
, sequences of integers
, and
such that
We consider the matrix with and for .
Then the matrix C does not map to .
Therefore, C does not satisfy at least one of the conditions (i)–(vii) in Proposition 1.
The condition (i) is fulfilled since
From (B1)–(B4) and (M1), it follows that the conditions (ii)–(vi) are satisfied in Proposition 1 for the matrix C.
Let us verify that the condition (vii) of Proposition 1 also holds.
By (M2), for a fixed
, we can find
such that
Let
D be such that
for
; then
Therefore, the condition (vii) in Proposition 1 also holds for the matrix C, and we reach a contradiction. □
Corollary 1. if and only if the conditions (A1), (B1)–(B4), and (M1)–(M2) in Theorem 1 are fulfilled as well as
- (A2)
Proof. Necessity. The necessity of (A1), (B1)–(B4), and (M1)–(M2) follows from Theorem 1. We obtain the necessity of (A2) since .
Sufficiency. Let be given. Then for some . By Theorem 1, and by (A2). So by the linearity of . □
To find the conditions for a matrix A to be in , we use the following result:
Proposition 3 (Theorem 2.5 in [
9])
. if and only if the following conditions hold:- (i)
:
- (ii)
- (iii)
- (iv)
] such that for
- (v)
such that for
- (vi)
- (vii)
:
- (viii)
: .
Applying this proposition, we obtain the conditions for a three-dimensional matrix to map to .
Corollary 2. if and only if the following conditions hold:
- (i)
:
- (ii)
- (iii)
- (iv)
such that for
- (v)
such that for
- (vi)
- (vii)
:
- (viii)
: .
Now, using these proposition and corollary, we find conditions for a matrix to map to .
We use the following notation: for
and
such that
, we set
. Analogously, for
and
such that
uniformly in
, we set
Theorem 2. if and only if
- (A1)
;
- (B1)
- (B2)
- (B3)
such that for
- (B4)
such that for
- (M1)
- (M2)
:
- (M3)
Proof. Necessity. The proof of (A1), (B1)–(B4), and (M1) follows in the same way as in Theorem 1.
To prove (M2), we suppose on the contrary that (M2) does not hold. Then
Hence, we can choose index sequences
and sequences of integers
such that
We consider the matrix
with
. Since
we get
Hence, by Corollary 2, the matrix C is not in , so there exists such that .
Therefore,
for any
.
Since
for any
, then
which implies
, a contradiction.
To prove the necessity of (M3), we first note that (A1) and (M2) imply that
for some
.
From (B3) and (B4) it follows that such that for and such that for . Hence, .
From the fact that , it follows that for every . Thus these matrices satisfy the conditions (i)–(viii) in Proposition 3.
Altogether, by Proposition 1, we obtain for every .
Hence, for every
we have
On the other hand, since
, for every
we can find
such that
So
and
Hence, the matrix is in , thus the condition (M3) holds by Theorem 1.
Sufficiency. Suppose that the conditions (A1), (B1)–(B4), and (M1)–(M3) are satisfied; we shall prove that .
By Proposition 3, we have for every ; so, in particular, converges for every .
In the necessity proof, we verified that .
By Theorem 1, the matrix is in .
Let
be given, then
Hence,
and Formula (
3) follows. □
Remark 1. The coefficients represent the asymptotic values of the Cesàro-averaged matrix entriesas the averaging window grows. In this sense, they describe the limiting behavior of the matrix transformation in the almost convergence framework and provide the coefficients of the corresponding limiting operator. Theorem 3. if and only if the conditions (A1), (B1)–(B4), and (M1)–(M3) in Theorem 2 hold and
- (A2)
.
Moreover, in this case,for each . Proof. Necessity and Sufficiency of (A1), (A2), (B1)–(B4), and (M1)–(M3) follow since
To prove Formula (
5), let
with
be given. Then by Theorem 2
Let
. In view of (A2) and
(
5) follows. □
To find the conditions for a matrix A to be in , we use the following results:
Proposition 4 (Theorem 3.6 in [
8])
. if and only if the following conditions hold:- (i)
- (ii)
Proposition 5 (Theorem 3.4 in [
8])
. if and only if the following condition holds: Now, using these propositions we find conditions for a matrix to map to .
Theorem 4. if and only if the following conditions hold:
- (B1)
- (A1)
Proof. Necessity. (B1) follows from Lemma 2 and Proposition 5.
To prove the necessity of (A1), we first note that by Lemma 2 and Proposition 5 for
, we have
Now, suppose on the contrary that
for some
.
Hence, we can find index sequences
, sequences of integers
, and
such that
We consider the matrix with and for .
Then, by Proposition 4, the matrix C does not map to .
Hence there exists such that .
Then, in the same way as in the proof of Theorem 1, we obtain that , which yields a contradiction.
Sufficiency. From (B1) it follows that exists for each .
Hence by Proposition 5, the matrix maps for each .
Now suppose on the contrary that for some .
In the same way as in Theorem 1, we obtain index sequences
, sequences
, and
such that
We consider the matrix as above.
Then C does not map to .
Hence C violates at least one of the conditions (i), (ii) in Proposition 4.
Condition (i) holds by (
7), therefore
This yields a contradiction. □
To find the conditions for a matrix A to be in , we use the following proposition:
Proposition 6 (Theorem 3.5 in [
8])
. if and only if the following conditions hold:- (i)
- (ii)
- (iii)
Applying this theorem, we obtain the conditions for a three-dimensional matrix to map to .
Corollary 3. if and only if the following conditions hold:
- (i)
- (ii)
- (iii)
Now, using these proposition and corollary, we find conditions for a matrix to map to .
Theorem 5. if and only if the following conditions hold:
- (M1)
- (B1)
- (A1)
Proof. Necessity. (M1) follows, since .
In the same way as in Theorem 4, we obtain
To prove the necessity of (B1), suppose on the contrary that there exists
such that
Hence, we can find index sequences
and integers
such that
Define the matrix
with
. Then
Hence, by Corollary 3, the matrix C is not in . Thus there exists such that .
Now, in the same way as in the proof of (M2) of Theorem 2, we conclude that , which yields a contradiction.
To prove the necessity of (A1), note first that implies that the matrix maps for each .
Since
, there exists a sequence
such that
From the inequality
we obtain
hence
.
Therefore, for
with
, we have
Hence, implies − .
Thus is in and the necessity of (A1) follows from Theorem 4.
Sufficiency. From (A1) and (
10) it follows that
is in
.
From (B1) and (A1) it follows that .
Hence the series converges for every .
Hence and − . □
To find the conditions for , we first find the conditions for a matrix to map into .
Theorem 6. if and only if
- (C1)
- (M1)
Proof. We identify the double sequence space
with the sequence space
, where
, via the isomorphism
T between the spaces
and
introduced in [
26], and the four-dimensional matrix
A with the three-dimensional matrix
(
).
By Corollary 3.3 (c) in [
8],
if and only if for every index sequences
, we have
.
By Theorem 2.18 in [
6], this is equivalent to the following:
(a)
(b)
By Proposition 3 (e) in [
10], the statement (a) is equivalent to
which is equivalent to (C1).
By Proposition 3 (a) in [
10], the statement (b) is equivalent to
which is equivalent to (M1). □
Identifying the double sequence spaces
,
with the sequence spaces
(
),
(
), and applying the conditions for
(Theorem 2.20 in [
6]), we can find the conditions for
:
Proposition 7 (Theorem 2.20 in [
6])
. if and only if- (i)
.
Now, using these theorems, we find conditions for a matrix to map into .
Theorem 7. if and only if
- (A1)
;
- (B1)
- (M1)
.
Proof. Necessity. The necessity of (A1) follows since .
The necessity of (B1) follows in the same way as the necessity of (B1) in Theorem 5 by applying Theorem 6.
The necessity of (M1) follows since
Sufficiency. From (B1) it follows that
Thus, by Proposition 7, the matrix for every .
If we suppose, on the contrary, that
for some
, then in the same way as in Theorem 1, we obtain
, index sequences
and integers
such that
We consider the matrix with and for .
Then the matrix C does not map to .
Therefore (cf. the proof of Theorem 1), the condition (B1) is not satisfied in Theorem 6 for the matrix C.
Thus there exist
such that
Since
it follows that
for every
, which yields a contradiction. □
To obtain the conditions for to be in , in the same way as for Theorem 7, we first derive the conditions for a matrix to map into :
Theorem 8. if and only if
- (C1)
- (B1)
- (M1)
Proof. As in the proof of Theorem 6, we identify the double sequence space with the sequence space , where , and the four-dimensional matrix A with the three-dimensional matrix .
By Corollary 3.3 (a) in [
8],
if and only if for every index sequences
the matrix
and all these matrices are pairwise consistent on
.
By Theorem 2.22 in [
6], this is equivalent to the following conditions:
(a)
∀ index sequences
(c)
∀ index sequences
By Proposition 3 (a) in [
10], statement (a) is equivalent to
which is equivalent to (B1).
By Proposition 3 (d) in [
10], statement (b) is equivalent to
Denoting , we obtain (C1).
By Proposition 3 (a) in [
10], statement (c) is equivalent to
which is equivalent to (M1).
Since for
, we have (cf. [
7]):
(
12) is equivalent to
By Proposition 3 (d) in [
10], this is equivalent to
So by (
1), Formula (
11) holds. □
Now, with the help of this theorem, we get the conditions for a matrix to be in .
Theorem 9. if and only if
- (A1)
such that ;
- (M1)
- (B1)
- (M2)
.
Proof. Necessity. The necessity of (A1) follows since .
The necessity of (B1) follows in the same way as the necessity of (B1) in Theorem 5 by applying Theorem 8.
The necessity of (M2) follows since .
Since
implies
for every
, by Theorem 8, for every
and
, we have
Hence , so (M1) holds by Theorem 7.
Sufficiency. Conditions (A1) and (B1) imply that for every .
Hence, .
By Theorem 7, it follows that .
So, for every
,
Hence, . □
Set , and .
To obtain the conditions for a matrix to map into , we first derive the conditions for .
Theorem 10. if and only if
- (B1)
- (B2)
- (C1)
such that
- (M1)
- (M2)
To prove this theorem, we use the same reasoning as in Theorem 8 by applying Corollary 3.3 (a) in [
8] and Theorem 5.1, part 8 in [
7]. Now, with the help of this theorem and using a standard argument, we obtain the conditions for a matrix to be in
.
Theorem 11. if and only if
- (B1)
- (B2)
- (A1)
such that
- (M1)
- (M2)
To obtain the conditions for a matrix to map into , we first derive the conditions for .
Theorem 12. if and only if
- (C1)
- (M1)
- (M2)
To prove this theorem, we use the same reasoning as in Theorem 8 by applying Corollary 3.3 (c) in [
8] and Theorem 5.1, part 4 in [
7]. Now, with the help of this theorem and using a standard argument, we obtain the conditions for a matrix to be in
.
Theorem 13. if and only if
- (A1)
- (M1)
- (M2)
To obtain the conditions for , we use Theorem 9.
Theorem 14. if and only if
- (B1)
- (A1)
such that
- (M1)
- (A2)
such that
Proof. Necessity of (A1) and (A2) follows since
. To prove the necessity of (B1) and (M1), we note that by Abel’s summation formula for
and
, we have
where
Since
implies
, then
(
), so
Analogously,
Moreover,
implies that the double series
converges (regularly). By Proposition 8.11 in [
27], we have
. So
By considering , we find that the matrix is in . So the necessity of (B1) and (M1) follows from Theorem 11.
Sufficiency. Let
. Since
for any
, the matrix
is in
by Theorem 11.
Hence, the first summand in the right part of (
16) is in
.
Moreover, from (A2) it follows that the second summand in the right part of (
16) is in
.
Hence,
and (
15) follows. □
From this theorem, we easily get conditions for a matrix to be in .
Corollary 4. A matrix is in if and only if the conditions of Theorem 14 hold with and .
Proof. The result follows directly from Theorem 14 by applying (
15) and using the identities
and
. □
We summarize our results in the
Table 1. The number indicates the result where the characterization of
can be found.
In the following examples, we illustrate how the previously discussed theorems can be applied to four-dimensional matrices.
Example 2. Let , . We consider the matrix defined by for and otherwise (). Then, , since conditions (B3), (B4) of Theorem 1 are not satisfied. Analogously, , and .
Now we verify that both conditions of Theorem 4 are satisfied. Let be fixed, thenHence, condition (B1) of Theorem 4 is satisfied. Moreover,Since the averaged element is a Cesàro mean of terms that tend to 0, the limit of the supremum over is also 0. So, condition (A1) of Theorem 4 also follows, and we have . Consequently, . Next we show that . Condition (A1) of Theorem 7 requires . For our matrix,and hence the Cesàro averages also tend to 0. Since is bounded for any fixed M and , condition (C1) of Theorem 7 follows. Condition (B1) of Theorem 7 requires boundedness of the weighted matrix elements. Since is bounded for any fixed M, and the average preserves this, the condition holds. Therefore, by Theorem 7, , and consequently . Now we verify that (Theorem 13). Condition (A1) of Theorem 13 has already been verified. We check condition (M1) of Theorem 13 for (since ):First we estimate :Thus, the term inside the parentheses is bounded by , and condition (M1) of Theorem 13 follows. Condition (M2) of Theorem 13 holds trivially since . Thus, by Theorem 13, , and consequently . Finally, we will show that . Condition (B1) of Theorem 14 involves the partial sums :So condition (B1) of Theorem 14 follows. We have seen already that condition (A1) of Theorem 14 is satisfied for (). For condition (M1) of Theorem 14, we have:andCondition (M2) of Theorem 14 requiresIn a similar way to condition (B1), we getSo (A2) follows with . Hence, by Theorem 14, and consequently . We now modify the previous example slightly so that it satisfies Theorems 1–3, as well as Corollary 1.
Example 3. Let , . We consider the matrix with for or and otherwise () (cf. Example 2.17 in [9]). Then, evidently, conditions (B1)–(B4) of Theorem 1 are satisfied. Condition (M1) holds sinceUsing the estimatefrom the previous example, we obtain (A1). For condition (M2), we estimateHence, condition (M2) of Theorem 1 follows. Therefore, , and consequently . Condition (A2) of Corollary 1 follows in a similar way to condition (A2) of Theorem 14 in the previous example. Thus, by Corollary 1, , and consequently .