1. Introduction
The concept of summability is essential for understanding the convergence behavior of sequences and series, especially those that diverge in the classical sense. A foundational contribution to this theory was made by Cesàro in 1890 through his paper Sur la multiplication des séries [
1], where he introduced a summability method that later came to bear his name. This pioneering idea paved the way for more generalized summability techniques. Among these, strong summability emerged as a significant extension, allowing for the analysis of sequences that fail to converge in the usual sense, as elaborated by Soomer and Tali [
2]. The further generalization known as strong
-summability incorporates a parameter
, providing finer control over convergence behavior. These methods have proven useful across several mathematical disciplines, including functional analysis and number theory. In 1951, Fast introduced the concept of statistical convergence, offering an alternative viewpoint within the broader framework of summability theory [
3]. The concept of convergence forms the foundation of analysis and functional analysis. A generalization of this concept, statistical convergence, which is based on the idea of the natural density of positive integers, plays a crucial role in summability theory and functional analysis. The idea of statistical convergence was initially proposed by Zygmund [
4] in 1935, where it was referred to as almost convergence. Steinhaus [
5] developed this idea independently of Fast [
3] in 1951. In addition to summability theory, it has played a significant role—under various names—in Fourier analysis, ergodic theory, number theory, measure theory, trigonometric series, turnpike theory, and Banach spaces. In 1959, Schoenberg [
6] redefined statistical convergence and presented it as a method of summability. Salát [
7] examined several topological properties of statistical convergence for sequences of real numbers. Fridy [
8] introduced the notion of statistical Cauchyness and proved that it is equivalent to statistical convergence. In 1988, Connor [
9] demonstrated that there exists a strong relationship between the concept of statistical convergence and strong p-Cesàro summability. Çolak [
10] introduced a generalized form of statistical convergence involving a real parameter
, where
. Based on Çolak’s approach [
10], the concept of statistical convergence was applied to the theory of probability by Akbaş and Işık [
11,
12]. In 2014, Aizpuru et al. [
13] defined the concept of
-statistical convergence, using an unbounded modulus function. Building on the framework proposed by Aizpiru et al. [
13], Bhardwaj and Dhawan [
14] employed the concept of f-density to formulate the
-statistical convergence of sequences. Over time, substantial effort has been devoted to extending the concept of statistical convergence and devising new related summability methods. Some important studies on statistical convergence can be found in [
15,
16,
17,
18,
19,
20,
21,
22].
The concept of convergence of number sequences has been extended to the convergence of set-valued sequences by many authors. One such extension is the Wijsman convergence, which is considered in this study. Nuray and Rhoades [
23] extended the concept of Wijsman convergence of set valued sequences to statistical convergence and provided some fundamental theorems. Ulusu et al. [
24,
25] defined the concept of Wijsman lacunary statistical convergence based on lacunary sequences and explored its relationship with Wijsman statistical convergence. Bhardwaj et al. [
26] have worked on the applications of this concept to specific sequences and its generalization. As a result, Wijsman statistical convergence has played a key role in the development of functional analysis and sequence theory.
This paper introduces the new notions of Wijsman statistical convergence and Wijsman strong summability of order α via unbounded modulus functions, establishes their fundamental properties and strict inclusion relations, and demonstrates that these concepts extend and generalize several classical results. The framework proposed is significant because it unifies and generalizes several existing notions of statistical convergence in metric spaces, many of which arise as special cases of our definitions. This comprehensive approach not only clarifies the relationships among different convergence concepts but also provides a solid foundation for further developments and applications in functional analysis, approximation theory, and related areas. In this way, our work fills notable gaps in the literature and opens up new directions for future research.
This study is divided into the following sections. The subsequent section provides the necessary preliminaries, including classical results related to density, statistical convergence, and strong
-Cesàro summability. In
Section 3, we define and explore the notions of Wijsman
-statistical convergence and Wijsman
-summability of order
, where
is an unbounded modulus function and
is a non-decreasing sequence of positive real numbers diverging to infinity. Additionally, we examine the connections between these two concepts.
2. Definitions and Preliminaries
We begin this section by exploring the concepts of natural density and statistical convergence. We denote the set of natural numbers by .
Statistical convergence, which generalizes classical convergence, is based on the natural density of subsets of the natural numbers and is precisely defined as follows:
The natural density of a subset
is given by
where
denotes the count of elements in
that are less than or equal to
. Clearly, the density
equals zero when
is finite.
A sequence
is said to be statistically convergent to
if, for every
,
If a sequence
is statistically convergent to
, we denote this by
.
The function
is called a modulus function if it satisfies the following conditions:
if and only if
,
for
,
is right-continuous at 0, and
is an increasing function. Moreover,
is continuous on the interval
when it is defined as a modulus function [
27].
Let
be an unbounded modulus function. The
-density of a set
is defined by
if the limit exists [
13]. Here and in what follows, we assume that
f is an unbounded modulus function unless stated otherwise.
Let
. The
-density of
is given by
provided that this limit exists. Here and hereafter, we assume that
α is a real number such that
, unless otherwise stated. It is important to note that when
, the
-density coincides with the natural density. However, as observed with
-density, the identity
does not generally hold when natural density is replaced by
-density for any
. Additionally, similar to the case of
-density, the
-density of any finite set remains zero [
28].
The sequence
is said to be
-statistically convergent to
, or
-convergent to
, if for every
,
that is,
and we denote this as
[
13].
A number sequence
is said to be statistically convergent of order
to
, or
-convergent to
, if
i.e.,
for every
.
We denote this as
. The set of all statistically convergent sequences of order
α is denoted by
. When
α = 1, statistical convergence of order
α reduces to the usual statistical convergence [
10].
The -density of and -statistical convergence of a sequence of order are defined as follows:
The
-density of
is defined by
provided the limit exists [
28].
A sequence
is said to be
-statistically convergent of order
to
, or
-convergent to
, if for every
,
that is,
In this case, we write
[
28].
Let
be a metric space, and consider non-empty closed subsets
and
of the metric space
. The distance
d(
z,
B) is defined as [
23]
If
(for each
z ∈
Z), then we say that the sequence
is bounded.
A sequence
is said to be Wijsman
-statistically convergent of order
(denoted as
-convergent) to
if, for every
and
, the following condition holds:
Here and in what follows,
is a non-decreasing sequence of positive real numbers tending to infinity, satisfying the following conditions:
In this case, we write
. The set of all sequences
that are Wijsman
-statistically convergent of order
to
is denoted by
[
29].
3. Main Results
Our work begins with the introduction of two new definitions
Definition 1. Let be a metric space, and let be as above. Let and be non-empty closed subsets of . The sequence is said to be Wijsman -statistically convergent of order to ifWe denote by the set of all Wijsman -statistically convergent of order . For special choices of the modulus function , the parameter and the sequence we obtain one of the following special cases. For example:
If
, then we write
instead of
which was defined and studied by Aral et al. [
29].
If
and
, then we write
instead of
which was defined and studied by Aral et al. [
30].
If
,
, and
, then we write
instead of
which was defined and studied by Nuray and Rhoades [
23].
If , then we write instead of
If , then we write instead of
In the special case and we write instead of
The following example is important in showing that there exists a sequence of sets that is Wijsman -statistically convergent of order ; that is, the set is non-empty.
Define the sequence
by
Let
be the metric space with usual metric,
,
, and
. Then for every
, we have
Hence
which implies that
.
Every Wijsman convergent sequence is also Wijsman -statistically convergent of order α for any unbounded modulus and . However, as the following example shows, a Wijsman -statistically convergent sequence of order α need not be Wijsman convergent.
Consider the sequence
defined by
Then
(
), but it not Wijsman convergent when
with 0 <
p ≤ 1.
Theorem 1. Let be a metric space, an unbounded modulus function, and let be non-empty closed subsets. For any , the inclusion holds strictly. Here and hereafter, and will represent two real numbers such that .
Proof. . If
then
. Since
f is increasing, we have
. Moreover, since
is a non-decreasing sequence of positive real numbers, it follows that
for every
. This gives
.
To show that the inclusion is strict consider the sequence
defined by
Then
(
), but
when
. □
Definition 2. Let be a metric space, and let be as above. Let and be non-empty closed subsets of . The sequence is said to be Wijsman strongly -summable of order to if, for each sequence of strictly positive real numbers and for each ,We denote by the set of all Wijsman strongly -summable sequences of order . In this context, we write , or equivalently, . When for all we write instead of
When for all we write instead of .
When
for all
and
we write
instead of
(as introduced by Aral et al. [
29]).
When
for all
and
we write
instead of
(as introduced by Aral et al. [
30]).
In particular, when
for all
and
, and
we write
instead of
(as introduced by Nuray and Rhoades [
23]).
Theorem 2. Let be a metric space, be an unbounded modulus function and let be non-empty closed subsets. For any , the inclusion holds strictly.
Proof. The proof is similar to that of Theorem 1. To show that the inclusion is strict, consider the sequence
defined by
Let
,
Since
we have
for
.
On the other hand,
for
.
Thus, for , but . □
The results stated in Theorems 1 and 2 are quite general. For special choices of the modulus function f, the parameter α, and the sequence ρ = (), we obtain the following special cases:
Corollary 1. - (i)
The inclusions and hold strictly, for
- (ii)
The inclusions and hold strictly, for
- (iii)
The inclusions and hold strictly, for
- (iv)
The inclusions and hold strictly, for
- (v)
The inclusion hold strictly, for
- (vi)
The inclusion hold strictly, for .
Theorem 3. Let be a metric space, an unbounded modulus function, and let be non-empty closed subsets. , we have
- (i)
If and , then .
- (ii)
If and , then .
- (iii)
If and , then .
- (iv)
If and , then .
Proof. (i) Let
and suppose that
. If
the result is immediate. Now assume
. For any
, the result follows directly from the following equality
(ii) Next, assume
and
. For any
ε > 0, we can write
By the subadditivity of the modulus function
f,
Since both terms on the right-hand side tend to zero by assumption, it follows that
The proofs of (iii) and (iv) are analogous to those of (i) and (ii), respectively. □
Theorem 4. If and , then , where denotes the family of sequences such that Proof. If
, then there exists a constant
such that
for
. Hence,
On the other hand, since the inequality
is satisfied for any modulus function
, any real number
; and, for each
, we obtain
Therefore,
. □
Theorem 5. Let for all (a constant), and suppose that . Then and is a proper subset of , where denotes the family of sequences such that Proof. For
, we can write
Accordingly, it follows that
.
To show that the inclusion is proper, we need to find a sequence belonging to
but not to
. Define
by
and let
,
and
. For
and
we have
so
.
On the other hand, for
and
,
hence,
. □
Corollary 2. Let be an unbounded modulus function such that for all (where is a constant), and suppose that . Then and
Theorem 6. If , then .
Proof. Let
be given. Choose
such that
for every
with
. Set
Since
, we can write
Hence, we have
□
Theorem 7. Let for all (where is a constant) and . Let and suppose that the sequence is bounded, then .
Proof. Suppose that
and the sequence
is bounded. Then we can find a constant
such that
. Now, for a given
and
, we have
Hence, we conclude that
□
Theorem 8. If , then .
Proof. If
, then we have
For
we have
. Since
f is increasing, we can write
. Since
is a non-decreasing sequence of positive real numbers, we have
Taking the limit as
, we obtain
. □
Corollary 3. (i) If , then for
(ii) If , then for
(iii) If , then
Proof. We will briefly prove only (i); the others follow similarly. If we take in Theorem 8, then the condition “” reduces to the condition “” and “” becomes “”. □
Theorem 9. Let be an unbounded modulus function, and let with . Assume and . Then the inclusion is proper.
Proof. First, consider
. Then there exists
such that
for large
, which implies
Assuming
, for any
there exists a sufficiently large
such that
To show that the inclusion is proper, consider the following example.
Let , , and .
Let
J =
and consider a sequence
defined as follows:
Then, for
we have
Since
for
we obtain
Then, for
we have
and
Hence,
□
Remark 1. In the following Theorem, we obtain the same result as in Theorem 8 by changing the conditions on the function and the sequence .
Theorem 10. Let be an unbounded modulus function, and let with . If and , then
Proof. If
, then we have
Since
and
, the proof follows from the following inequality using the argument from Theorem 9.
□
Theorem 11. If , then = B converges uniquely.
Proof. Consider
. Suppose
and
=
. Then,
and
By the definition of
, we obtain
where
and
. Therefore
Since
, it follows that
. Hence, the limit is unique. □
Theorem 12. Let and be two sequences such that for all , and let be two real numbers such that . Ifthen - (i)
,
- (ii)
.
Proof. The proof of (i) is obtained from the following inequality:
(ii) Let
. For
and
for all
we have
Thus, we have
. □
Corollary 4. (i) If , then and for
(ii) If , then and ,
(iii) If , then and for .
Proof. Briefly, we will prove only a part of (i); the others follow in a similar way. If we take in (1), then the condition “” reduces to the condition “” and the relation “” becomes “”. □
5. Conclusions
In this paper, we introduced and studied new notions of Wijsman statistical convergence and Wijsman strong summability of order for sequences of closed sets in metric spaces. These concepts generalize classical Wijsman convergence by incorporating unbounded modulus functions and generalized summability methods.
We established several inclusion relations between these newly defined concepts, proving that the inclusions are strict under appropriate conditions on the parameters, the modulus function, and the sequence. Additionally, uniqueness of limits was demonstrated for the strongly summable sequences, and various properties such as linearity and stability under scalar multiplication and addition were confirmed.
Our results provide a broad framework for further exploration of statistical convergence concepts in metric spaces, opening paths for applications in analysis and approximation theory where such generalized convergence methods are essential. Future work may focus on several directions, including applications of these generalized convergence concepts to other areas of mathematics, such as functional analysis, optimization, and fixed point theory. Additionally, investigating the behavior of these convergence notions in more complex structures, such as Banach or Hilbert spaces, and extending them to probabilistic or fuzzy set settings could provide deeper insights. Another promising direction is to explore computational aspects and algorithmic implementations, potentially leading to practical methods for approximating set-valued mappings in applied problems. Overall, these avenues suggest that the concepts introduced here have significant potential for both theoretical development and practical applications.
The results obtained in this study are quite general. If the function f, the parameter α and the sequence are chosen in specific forms, our results reduce to several well-known results in the literature. For example:
When
, our results reduce to the concepts of Wijsman
-statistical convergence of order
studied in [
29].
When
and
, our results reduce to the concepts of Wijsman
-statistical convergence studied in [
30].
When
,
for all
and
, our results reduce to the concepts of Wijsman statistical convergence studied in [
23].
Moreover, if we consider sequences of real numbers instead of sequences of sets, our results generalize the works of Salat [
7], Fridy [
8], Connor [
9], Çolak [
10], Aizpuru et al. [
13], and Bhardwaj and Dhawan [
14,
28].