1. Introduction
Menger’s work [
1] marked a significant advancement in the realm of metric axioms, as it introduced a novel approach by associating each pair of points in a set with a distribution function. Schweizer and Sklar [
2] subsequently expanded upon this idea, which originally emerged as the concept of a statistical metric space. The key innovation was to replace nonnegative real values with distribution functions. Within this framework, the family of probabilistic normed spaces emerged as a notable subset, wherein probability distribution functions replaced traditional norms for vectors instead of numerical values. The inception of probabilistic normed spaces was initially attributed to Šerstnev in 1963, as documented in [
3].
In the 1960s, Gähler [
4] played a pivotal role in propelling the concept of 2-normed spaces forward. Building on this groundbreaking work, several other scholars have adopted this concept. Gürdal and Pehlivan [
5] extensively examined statistical convergence, statistical Cauchy sequences, and other aspects of statistical convergence within 2-normed spaces. Within the realm of 2-normed spaces, Gürdal and Açk [
6] investigated
-Cauchy and
-Cauchy sequences. Moreover, Sarabadan and Talebi [
7] delved into statistical convergence and ideal convergence of function sequences within 2-normed spaces. Arslan and Dündar [
8] also explored
-convergence,
-convergence,
-Cauchy, and
-Cauchy sequences of functions within 2-normed spaces. It is important to emphasize that significant progress in this field has been well-documented in references [
9,
10].
Subsequently, Alsina et al. [
11] introduced a novel definition of PN (probabilistic normed) spaces, building upon Šerstnev’s foundational work. This advancement ultimately led to the identification of a crucial category of PN spaces known as Menger spaces.
More recently, Golet [
12] expanded the scope of probabilistic normed spaces to include both random and probabilistic 2-normed spaces. This expansion was influenced by Gahler’s concept of a 2-norm, as outlined in [
4].
Moving on to the realm of convergence, Fast independently extended the concept of statistical convergence from the context of real number sequences [
13,
14]. Subsequently, Sahiner et al. [
15,
16] applied this notion to triple sequences. In this context, a triple sequence
is considered convergent to
if, for any given
, there exists a natural number
such that
holds for all
, and
t exceeding
. Furthermore, the concept of density
for a subset
E of
was introduced and defined as the limit of the expression
where
represents the characteristic function of the set
E.
The concept of “ideal convergence”, which expands on statistical convergence, finds its theoretical roots in the framework of the ideal
as it pertains to subsets of natural numbers. Kostyrko et al. [
17] propelled the study of
-convergence even further, utilizing the framework of the ideal
when dealing with subsets of natural numbers. Sahiner and Tripathy [
16] subsequently applied the notion of
-convergence to triple sequences in metric spaces, attracting considerable attention from mathematicians across various disciplines. For instance, Altaweel et al. [
18] adapted this theory to the fuzzy metric space, while Kočinac and Rashid [
9] expanded it to encompass the probabilistic metric space. Furthermore, Rashid and Kočinac and Rashid [
10] delved into the investigation of the ideal of convergence within the framework of fuzzy 2-normed space. The introduction of
-convergence in [
17] spurred extensive research efforts aimed at uncovering its relationship with
-convergence.
Recently, Mohiuddine and Alotaibi [
19] delved into the domain of RTNS, with a specific focus on exploring stability results associated with the cubic functional equation. In the context of double sequences situated in random 2-normed spaces, Mohiuddine et al. [
19] introduced and thoroughly examined the concepts of
-convergence and
-convergence. Their research also unveiled a correlation between these two modes of convergence, establishing that
-convergence serves as a sufficient condition for
-convergence.
Moreover, they presented a compelling instance demonstrating that, in the general scenario,
-convergence does not necessarily imply
-convergence when applied to random 2-normed spaces. For further exploration of random 2-normed spaces, please consult the references [
20,
21,
22].
The work introduced by [
23] focuses on the investigation of unbounded fuzzy order convergence and its real-world applications. Moreover, the article delves into the correlation between unbounded fuzzy order convergence and theoretical concepts such as fuzzy weak order units and fuzzy ideals. Within the scope of our research, the authors in [
24] propose an enhanced algorithm for deionising images, which is based on the TV model. This approach effectively tackles the aforementioned challenges. The introduction of the
reg. term serves to simplify the solution, facilitating the recovery of high-quality images. Through the reduction in estimated parameters and the application of inverse gradients for estimating the regularization parameter, it enables global adaptation, thereby improving the denoising effect in conjunction with the TV reg. term. Initially, the application of energy-density modeling for strongly interacting substances, such as atomic nuclei and dense stars, may seem unrelated to the exploration of ideal convergence in random 2-normed spaces. Nevertheless, it is plausible to identify certain conceptual connections between the two. Acknowledging these potential correlations, the researchers in the study by Papakonstantinou and Hyun [
25] establish a foundation for interdisciplinary collaborations that leverage the respective strengths of each field. This collaborative endeavor aims to advance the understanding of complex systems, ultimately promoting advancements in both theoretical and practical research.
In this research, we focus on examining the rough convergence of triple sequences within the context of 2-normed spaces rather than in random environments. Furthermore, we introduce and analyze the concepts of -convergence, -convergence, -limit points, and -cluster points for random 2-normed triple sequences. We establish a noteworthy result, demonstrating that -convergence implies -convergence in the context of random 2-normed spaces, highlighting the interplay between these two forms of convergence. The study of the ideal of convergence in random 2-normed spaces is crucial across various disciplines such as functional analysis, probability theory, and stochastic processes. This significance stems from its ability to generalize classical spaces, enabling the analysis of random variables and sequences. Moreover, these spaces provide a suitable mathematical structure for modeling stochastic processes, aiding in the development of accurate models for random phenomena. Understanding the ideal of convergence is vital for statistical analysis, facilitating the formulation of robust methods for handling data with inherent randomness. Additionally, its relevance in functional analysis, particularly in relation to linear operators and function spaces, has implications for fields like quantum mechanics and signal processing. Furthermore, its contribution to the advancement of probability theory, particularly concerning random variable convergence and limit theorems, establishes a strong theoretical foundation for various probabilistic concepts and results. Ultimately, this investigation serves as a fundamental basis for the development of sophisticated models, analysis techniques, and mathematical tools to address real-world challenges associated with randomness and uncertainty.
This paper is organized as follows: The next section introduces and discusses fundamental definitions and early discoveries concerning a random 2-normed space.
Section 3 establishes that, assuming a general condition, the condition (AP3) is both necessary and sufficient for the equivalence of the
and
-Cauchy criteria. Moreover, it includes a specific example illustrating that the
-Cauchy condition is not always met.
Section 4 explores various significant and previously unexplored aspects of
- and
-convergence concerning triple sequences within a random 2-normed space. It also investigates related implications, including the characterization of compactness in terms of
-cluster points, which is discussed in
Section 5.
Section 6 focuses on presenting some applications of the ideal of convergence of triple sequences in the context of a random 2-normed space. In
Section 7, the study presents its findings and offers specific recommendations to other researchers regarding potential future research directions based on the study’s results.
2. Definitions, Notations and Preliminary Results
In this section, we will revisit fundamental definitions and notations that serve as the foundation for the current investigation.
A distribution function is a member of the set
, where
is defined as follows:
Within this context, the subset
can be described as
, where
represents the left limit of the function
f at the point
. The space
can be partially ordered using the standard pointwise ordering of functions, which means that
if and only if
holds for every
in the real numbers. For any
, we can define a distribution function
, as follows:
The set , along with its subsets, can be subjected to partial ordering using the conventional pointwise order. In this ordering, represents the maximum element within .
Definition 1 ([
20])
. A t-norm is a continuous mapping such that is an abelian monoid with unit one and if and for all . A triangle function γ is a binary operation on , which is commutative, associative and for every . The concept of a 2-normed space was first introduced by Gähler.
Definition 2 ([
4])
. Suppose X is a linear space with dimension , where . A 2-norm on X is defined as a function that satisfies the following conditions for any : (i) if and only if ξ and ζ are linearly dependent. (ii) . (iii) for all . (iv) .In this context, we refer to as a 2-normed space.
Example 1 ([
4])
. Take being equipped with the 2-norm the area of the parallelogram spanned by the vectors ξ and ζ, which may be given explicitly by the formula In a recent development, Golet introduced the concept of a RTNS in the following manner.
Definition 3 ([
12])
. Let X be a linear space of a dimension greater than one, γ be a triangle function, and . Then, ψ is called a probabilistic 2-norm on X and a probabilistic 2-normed space if the following conditions are satisfied:- (i)
if ξ and ζ are linearly dependent, where denotes the value of at ;
- (ii)
if ξ and ζ are linearly independent;
- (iii)
for every in X;
- (iv)
for every , and ;
- (v)
whenever .
If (v) is replaced by
- (v’)
, for all and , then triple is called a RTNS.
Remark 1 ([
19])
. Note that every 2-normed space can be made a random 2-normed space in a natural way, by setting- (a)
, for every , and , ;
- (b)
, for every , and , .
Definition 4 ([
26])
. A double sequence is considered to be statistically convergent to μ according to Pringsheim’s criteria if, for every , the set , defined as:satisfies the condition . It is important to highlight that if we have a convergent double sequence , it also exhibits statistical convergence to the same limit. Conversely, when is statistically convergent, the limit is uniquely determined.
Now, let us consider a triple sequence consisting of real numbers. We define it as “bounded” if there exists a positive real number M such that for all .
We introduce a set
E that is a subset of
. We denote
as the set of indices
such that
,
, and
. If the sequence
has a limit in Pringsheim’s sense, we say that
E possesses triple natural density, and we represent it as:
Now, let us establish the concept of statistical convergence for a triple sequence
toward
. For any given
, if the triple density
, where
then we affirm that the triple sequence statistically converges to
.
Definition 5 ([
15])
. Let us consider a 2-normed space denoted as . In this space, a triple sequence is classified as a statistically Cauchy sequence if, for any element and for any positive value of ε, we satisfy the condition , whereIn such a situation, we represent it as: Definition 6 ([
20])
. Let us consider a 2-normed space denoted as . Within this space, a triple sequence is regarded as statistically convergent to an element if, for any element , and for any given positive value of ε, we satisfy the condition , whereIn this scenario, we express it as: Definition 7 ([
19])
. Let be an RTNS. A sequence is considered statistically convergent to ξ if, for all and for every and , the conditionis satisfied, or equivalently,In this scenario, we express it as .
Definition 8 ([
27])
. An ideal, denoted as , is defined as a non-empty class within the set that adheres to the following conditions:- (i)
The additive property: If two sets, S and T, belong to , then their union is also an element of .
- (ii)
The hereditary property: If a set T belongs to , and another set S is a subset of T, then S is also an element of .
Definition 9 ([
17])
. A non-trivial ideal, denoted as , is distinguished by the property that it does not encompass the entire set . Furthermore, it earns the label “admissible” when it satisfies two specific conditions: it is non-trivial, and for every natural number l, the singleton set l is included in . Definition 10 ([
17])
. For any given ideal , a corresponding filter is associated with it, denoted as . This filter is defined as follows:Moreover, when an admissible ideal is considered within the set , it is said to possess the property referred to as (AP) if, for any sequence comprising mutually exclusive sets from , there exists another sequence consisting of subsets of , such that each symmetric difference (for all ) is finite, and the union of all (i.e., ) is an element of .
Definition 11 ([
17])
. Consider a nontrivial ideal within the set of natural numbers . A sequence is deemed -convergent to L if, for every , the setIn this situation, we express it as -.
Definition 12 ([
19])
. Let be a nontrivial ideal of , and let be a random 2-normed space. A double sequence consisting of elements from X is considered -convergent to within the context of the random 2-normed space (or -convergent to ξ) if, for all and any and , the setIn this situation, we represent it as .
Definition 13 ([
28])
. A nontrivial ideal of is referred to as strongly admissible if it includes sets of the form , , and for each . It is evident that a strongly admissible ideal also qualifies as an admissible ideal. If we define , then constitutes a nontrivial strongly admissible ideal. It is noticeable that is a strongly admissible ideal if and only if .
Definition 14 ([
28])
. An admissible ideal adheres to the property (AP3) if, for every countable collection of mutually disjoint sets that belong to , there exists a countable family of sets such that the symmetric difference is contained within the finite union of rows and columns in for each , and . Consequently, it follows that for each . Remark 2. It is crucial to note that when the ideal corresponds to , -convergence coincides entirely with the conventional concept of convergence. Conversely, if we define as the collection of all subsets A of for which the triple natural density equals zero, then -convergence becomes equivalent to statistical convergence.
Triple sequences that converge with respect to
may not necessarily be bounded. For instance, consider the ideal
as
in
. If we define
as follows:
In this case, the sequence is unbounded; however, it is still -convergent.
3. and -Cauchy of Triple Sequences
In this section, we will redirect our attention towards investigating the concepts of -Cauchy and -Cauchy triple sequences within the framework of . Furthermore, we will explore the interconnections and associations among these ideas.
Definition 15. Let be a RTNS and be a strongly admissible ideal. A triple sequence of elements in Xis said to be
- (a)
An -Cauchy sequence in X if for every , and a nonzero , there exist such that - (b)
An -Cauchy sequence in X if for every , and a nonzero , there existssuch that and is ψ-Cauchy sequence in X.
The following theorem establishes a connection between triple Cauchy sequences under and .
Theorem 1. Let be a random 2-normed space and be a strongly admissible ideal. If is an -triple Cauchy sequence, then is an -triple Cauchy sequence.
Proof. For any
and
within the open interval
, and for any non-zero element
, the sequence
is an
-triple Cauchy sequence if the following conditions are met: There exists a set
and a number
, such that
for every
j and
s greater than or equal to
. Fix
,
, and
. Then, for every
in
,
, and a non-zero
, the following condition holds:
for every
j greater than or equal to
. Additionally, let
H be the complement of the set
K in
. It is evident that
, and we can establish that:
Therefore, for any in , , and a non-zero , we can find such that , implying that the sequence is an -triple Cauchy sequence. □
Theorem 2. Let us consider a countable assortment of subsets represented as within the set . Each of these subsets, which we still denote as , is part of a filter denoted as , which is linked to a strongly admissible ideal known for possessing the property (AP3). In this context, we can establish the presence of a set denoted as P that is contained within the set . Additionally, this set P is also a member of the filter and furthermore, the disparity between P and each is finite for all i.
Proof. Let , , . It is easy to observe that for each i and , when . Then, by (AP3) property of , we conclude that there exists a countable family of sets , such that , i.e., is included in a finite union of rows and columns in for each j and . Put . It is clear that .
Now, we will establish that the set
has a finite number of elements for every
Let us suppose there exists a natural number
such that the set
contains an infinite number of elements. Given that each difference
(for
) is confined within a finite combination of rows and columns, we can identify specific natural numbers
, such that:
where
. If
and
, then
and so by (
1),
.
Since and , we have for and . Consequently, for all natural numbers m satisfying , n satisfying , and k satisfying , we find that belongs to both P and . This observation demonstrates that the set contains only a finite number of elements. This contradicts our initial assumption that the set is infinite. □
Theorem 3. Let be a RTNS and be a strongly admissible ideal with property (AP3). Then, the concepts -triple Cauchy sequence and -triple Cauchy sequence coincide.
Proof. If is -triple Cauchy sequence, then it is -triple Cauchy sequence by Theorem 1 (even if does not have the (AP3) property).
Now, we need to demonstrate the reverse statement. Suppose we have an
-triple Cauchy sequence denoted as
. As per the definition, there exists specific values
,
, and
such that:
for every
and a non-zero
.
Let
,
, where
. It is clear that
for
. Since
has the property (AP3), then by Theorem 2 there exists a set
such that
, and
is finite for all
i. Now, we prove that
To prove this, let
,
and
such that
. If
, then
is a finite set, so there exists
such that
for all
. Hence, it can be concluded that for all
greater than a certain threshold
:
and
. As a result, we can infer that:
for all
.
Therefore, for any given
and
, there exists a threshold
such that when
, and all these values are elements of some set
P belonging to the filter
:
This holds true for every non-zero element z in the set X. Hence, it demonstrates that constitutes an -triple Cauchy sequence within the space X. □
4. -and -Convergence in RTN
In this section, our investigation is focused on the concept of ideal convergence as it pertains to triple sequences within a RTNS. We will introduce the notion of -convergence for triple sequences in this space and establish that -convergence implies -convergence, although the reverse is not necessarily valid. It is crucial to emphasize that in this section, we consistently treat as a nontrivial admissible ideal within .
Definition 16. Consider a non-trivial ideal within the set of natural numbers , and let represent a random 2-normed space. Now, let us introduce a triple sequence denoted as , consisting of elements from X. We define this triple sequence as -convergent to with respect to the random 2-normed space ψ, or, more succinctly, as -convergent to L, if, for every and , and for all , the setbelongs to the ideal . In such a context, we express this as -. Theorem 4. Let be a RTNS. Then, the following statements are equivalent:
- (a)
-;
- (b)
for every , and ;
- (c)
for every , and ;
- (d)
-.
Proof. We will refrain from presenting the proof as it can be readily comprehended. □
Theorem 5. Let be a random 2-normed space. If a triple sequence is -convergent, then the -limit is necessarily unique.
Proof. Assume that
-
and
-
. For a given
and
, and for any element
, select
such that
. We then define the following sets as:
Given that
-
, it follows that
. Moreover, by utilizing
-
, we can conclude that
. Now, let us define
. As a result, we establish that
. This, in turn, implies that its complement, denoted as
is not empty within
. If we have
, it follows that
and thus:
Given that was chosen arbitrarily, it follows that for all . Consequently, we can conclude that . □
Theorem 6. Let be a random 2-normed space and let be triple sequences in X. If ψ-, then -.
Proof. Assume that
-
. Then, for any
and
, and for all
, there exists a positive integer
N such that
for all
. Considering this, we can observe that the set
is contained in
and the ideal
is admissible; thus, we have
. Therefore,
-
. □
In the forthcoming example, it is not guaranteed that the converse of what Theorem 6 asserts holds true.
Example 2. Consider the space equipped with the Euclidean 2-norm, denoted as , defined by the vectors ξ and ζ. These vectors can be explicitly expressed using the formula:and for . For all , and nonzero , consider Now, we have as a RTNS. Next, we introduce a double sequence denoted as , defined as follows: Write , , and . We observe that Taking , we obtain Therefore, it can be observed that a triple sequence does not exhibit convergence within the space . However, if we define , then because , we have . In other words, -.
Theorem 7. Let be a random 2-normed space and let and be triple sequences in X.
- (a)
If - and -, then -.
- (b)
If -, then If -.
Proof. (a) Assume that
-
and
-
. For any given
and
, and for all
, select
such that
. We can then define the following sets as:
Given that -, we can establish that . Similarly, by employing -, we conclude that . Now, let us introduce .
Consequently, we have
. This implies that its complement
is non-empty within
. Now, our task is to demonstrate that:
If
, then we have
and
. Consequently,
Since , we have -.
(b) The case where
is straightforward. Now, consider the situation when
. For any given
,
, and for all
, we have:
We only need to demonstrate that for every
,
, and all
, we have:
Let
. Then we have
. Now,
□
Definition 17. Let be a random 2-normed space. We define that a sequence consisting of elements from X is -convergent to concerning the random 2-normed space ψ under the condition that there exists a subset H, defined as follows:of of such that H belongs to the filter (which means ), and the ψ-limit of as s approaches infinity is equal to L. In this context, we denote this as -, and we refer to L as the -limit of the triple sequence . Theorem 8. Let be a random 2-normed space and be an admissible ideal. If is a triple sequence of elements in X and -, then -.
Proof. If
-
, then
of
such that
(meaning
) and
-
. Consequently, for any
,
, and all
, there exists a positive integer
N such that
for all
. Given that
is a subset of
and considering the admissibility of the ideal
, we can conclude that:
Therefore,
for any
,
and all
and so
-
. □
The example provided below demonstrates that the reverse of Theorem 8 may not necessarily hold true.
Example 3. Consider equipped with the Euclidean 2-norm, denoted as , defined by the vectors ξ and ζ. These vectors can be explicitly described by the formula:and for . For all , and nonzero , considerIn this context, forms a RTNS. Consider a decomposition of denoted as , such that for any , each contains infinitely many triplets, where , and for . Now, let represent the set of all subsets of that intersect with at most a finite number of . It is important to note that qualifies as an admissible ideal. Next, let us introduce a double sequence if . Then,as and for all . Hence, -. Now, let us assume that -. In this case, there exists a subset of such that and ψ-. Furthermore, since , there exists a set such that . Now, from the definition of , there exist, say such that But then , and thereforefor infinitely many ’s from M, which contradicts the condition ψ-. Hence, the assumption that - results in a contradiction. Theorem 9. Consider a RTNS space denoted as . In this context, the following conditions are equivalent:
- (a)
-.
- (b)
There exist two sequences, namely and , both belonging to the space X, such that , ψ-, and the set is an element of the set , where θ represents the zero element of the space X.
Proof. If condition (a) is satisfied, then there exists a subset
of
such that
We define the sequences
and
as
and
for all
. For given
,
,
and
, we have
By utilizing (
2), we can deduce that
-
. Furthermore, because the set
is contained within the complement of set
K (i.e.,
), it follows that
.
Assuming condition (ii) is met, we find that constitutes an infinite set. Clearly, the set is infinite. Let us denote it as . Given that and -, it logically follows that -. Consequently, we can assert that -. Thus, this completes the proof. □
Theorem 10. Let be an RTNS.
- (a)
If X has no accumulation point, then and -convergence coincide for each strongly admissible ideal .
- (b)
If X has an accumulation point ξ, then there exists a strongly admissible ideal and a double sequence for which - but - does not exist.
Proof. (a) Consider a triple sequence
in the space
X. Assuming that
-
, we can conclude that there exists a set
M in
(i.e.,
), such that
For any positive value
, a parameter
within the range of (0,1), and a non-zero element
z in the space
X, it can be deduced from (
3) that there exists a natural number
such that
holds for all
. Consequently
where
Now, since , consequently, we have and so -.
Next, we will demonstrate that if
-
, then
-
. To establish this, given that the set
X lacks accumulation points, we can find a value
within the interval (0,1) such that for any
and a non-zero element
As
-
, we have
. This implies
Consequently, -.
(b) Since functions as an accumulation point within the space , there exists a sequence consisting of distinct points, all distinct from , within X. This sequence converges to and for a non-zero element the sequence exhibits a monotonic increase toward 1.
Now, let us delve into a partition of the set of natural numbers
into infinite sets, denoted as
, and define
as
. Consequently,
constitutes a partition of
, and the ideal
is defined as follows:
This particular ideal, denoted as
, possesses strong admissibility. Now, we will establish a connection between the sequence
and the sequence
for
and a non-zero element
a in
X. Let us assume that we have
within the interval (0,1),
and a non-zero element
a in
X. We can choose
from the set of natural numbers such that
Then, we define
as follows:
Consequently, falls under the set , and we can infer that -.
Now, assuming that - it follows that there exists H in such that resulting in -
Based on the definition of “there exists an integer l such that H is a subset of ”. This implies that is a subset of “Considering the construction of ”, we can infer that, for any given , “the inequality holds for an infinite number of with ” and “This contradicts the fact that -”.
Similarly, if we assume that - “for ”, it leads to a contradiction. □
Remark 3. As deduced from the previous outcome, it is clear that -convergence entails -convergence, but the converse is not always valid. This raises the question of when the reverse relationship may be established. If the ideal is endowed with property (AP3)," the following theorem demonstrates that the reverse relationship indeed holds true.
Theorem 11. If is an admissible ideal of having the property (AP3) and is a RTNS, then, for an arbitrary triple sequence of elements of X, - implies -.
Proof. Assume that
satisfies property (AP) and
-
Under these conditions, for any given
,
within the interval
and for every
a in
Let us establish the set
for
q belonging to the natural numbers
and for a given
, as follows:
Clearly, the set
is countable and falls within the set
. Additionally,
for
. By virtue of property (AP), there exists a countable family of sets
such that the symmetric difference
is a finite set for each
and
. From the definition of the associated filter
there is a set
such that
To prove the theorem, it suffices to demonstrate that the subsequence
converges to
L concerning the probabilistic norm
. Let
and
Choose
such that
Then,
Since
are finite, there exists
such that
If
and
then
and so
. Hence, for every
and
, we have
Since was arbitrary, we have -. □
Theorem 12. If possesses at least one accumulation point, and for any arbitrary triple sequence consisting of elements from X and for every the condition - implies -; then, it can be concluded that has property (AP3).
Proof. Assume
is an accumulation point of
In this scenario, there exists a sequence
consisting of distinct elements from
where none of these elements are equal to
Furthermore, we have
and the sequence
is an increasing sequence that converges to 1 for a non-zero element
a in
X. Let
. Now, consider a disjoint family of nonempty sets from
denoted as
. We define a sequence
as follows:
for any
j. Let
and a non-zero
. Choose
such that
. Subsequently, we can express
as
which is a subset of
Consequently,
and thus,
-
Based on our assumption, we can then conclude that
-
Therefore, there exists a set
such that
and thus,
Consider the sets
for
It follows that
for each
Furthermore,
and thus,
Now, let us fix an arbitrary
If
is not included in the finite union of rows and columns in
then
M must contain an infinite sequence of elements
, where both
and
for all
which contradicts (
4). Therefore,
must be contained in the finite union of rows and columns in
Consequently,
is also included in the finite union of rows and columns. This confirms that the ideal
indeed possesses property (AP3). □
If is an admissible ideal contained in and satisfies condition (AP), we can straightforwardly establish that convergence with respect to implies convergence with respect to for any triple sequence in the set X. However, it is important to note that, unlike the equivalence between and -convergence for triple sequences, condition (AP) is not a prerequisite.
As an illustration, consider the ideal associated with Pringsheim’s convergence. In this case, convergence with respect to and is equivalent. However, it is worth emphasizing that the sets are elements of and collectively form a partition of . If we remove only a finite number of elements from each (or certain ’s) from the set , the resulting set does not belong to . This illustrates that the property (AP) is absent in the ideal .
Now, considering double sequences, it becomes apparent that (AP) is essentially stronger than (AP3). Consequently, the following results can be immediately derived from Theorem 11.
Corollary 1. Let be a RTNS and the ideal possesses property (AP). If is a triple sequence in X such that -, then -.
5. -Limit Points and -Cluster Points in RTN
In this section, we introduce the concepts of
-limit points and
-cluster points in the context of random 2-normed spaces. For information on statistical limit points and statistical cluster points, as well as statistical limit points and statistical cluster points of sequences in fuzzy 2-normed spaces and probabilistic normed spaces, please refer to the citations mentioned in [
21,
29,
30].
Definition 18. Let be a random 2-normed space, and . An element ζ is said to be a limit point of the sequence with respect to the random 2-norm ψ (or a ψ-limit point) if there is subsequence of the sequence ξ which converges to ζ with respect to the probabilistic norm random 2-norm ψ. By , we denote the set of all limit points of the triple sequence with respect to the random 2-norm ψ.
Definition 19. Let be a random 2-normed space, and . An element ζ is said to be an -limit point of the sequence ξ with respect to the random 2-norm ψ (or -limit point) if there is a subset of such that and ψ-. We denote by , the set of all -limit points of the sequence .
Definition 20. Let be a RTN, and . An element ζ is said to be an -cluster point of ξ with respect to the random 2-norm ψ (or -cluster point) if for each and and a non-zero By , we denote the set of all -cluster points of the sequence .
Theorem 13. Let be a RTN. Then, for every triple sequence in X, we have
Proof. Suppose
belongs to
. In that case, there exists a set
in
such that
and
-
. For each
and a non-zero
, there exists
such that for
, we have
. Hence,
and consequently,
which means that
.
If
is an element of
then for any
within the interval
and a non-zero element
a in
Let In that case, there exists a subsequence of that converges to in accordance with the random 2-norm Consequently, is a regular limit point of , meaning and thus, The proof of the theorem is now considered complete. □
Theorem 14. Let be a sequence in a randon 2-normed space . Then , provided -.
Proof. Let
, where
. Then, there exist two subsets
H and
, that is,
and
of
such that
By (
6), given
and a non-zero
, there exists
, such that for
we have
. Therefore,
Since
is an admissible ideal, it follows that
. Now, if we consider
we observe that
In the alternative scenario where
, it would imply
which contradicts (
6). Given that
-
it can be concluded that for any
within the interval
and a non-zero element
a in
As for every it holds that we can infer that Given that implies this contradicts the assertion that Consequently, we can conclude that
On the contrary, let us assume that
where
As per the definition, for any
within the interval
and a non-zero element
a in
When
it follows that
and hence,
Furthermore, considering that
-
we can deduce that
Consequently, , which contradicts the earlier statement that . Hence, we can conclude that . This completes the proof of the theorem. □
The next two instances demonstrate that the notions of a cluster point and an -cluster point are unrelated.
Example 4. This example shows how a sequence in a random 2-norm can have a cluster point without simultaneously having an -cluster point, which corresponds to a non-trivial ideal of . Define in
Consider with , where and let for all . For and Consider, Then, is a random 2-norm space. Let be a sequence in X defined byNow, to show that this sequence has a cluster point but no -cluster, we can demonstrate as follows: The sequence has a cluster point: Given any small neighborhood around 0, the sequence will have points that are arbitrarily close to 0 for infinitely many terms, implying that 0 is a cluster point.
The sequence does not have an -cluster: For the given sequence, the elements do not satisfy the pattern required by , since the elements of should be of the form , where and . The sequence does not strictly adhere to this structure, thus not forming an -cluster. This example illustrates the existence of a cluster point without an -cluster in defined by the given norm and operation.
Example 5. Let . Given the sequence defined as for all , we need to examine its behavior in the context of the defined 2-normed space. The norm is defined by for and in . The operation ∗ is defined as the standard multiplication for all . The function is given by Then, is a random 2-normed space. To better illustrate this, let us first clarify the definition of . We have . The statement suggests that the sequence lacks a cluster point in but every odd positive integer turns into an -cluster point. We need to verify this fact considering the definitions provided. First, the -cluster points are those that belong to the set , which is defined as . It follows that any term of the form will belong to this set if and only if . This indicates that all terms of the form , where will be -cluster points.
On the other hand, to show that the sequence does not have a cluster point in , we need to demonstrate that for any , there exists an , such that the ball contains at most finitely many terms of the sequence . However, since this condition is not met, the set of cluster points remains empty.
7. Conclusions and Future Work
This conclusion hints at a comprehensive investigation into the concepts of -Cauchy and -Cauchy for triple sequences within the context of random 2-normed spaces. It also introduces and analyzes the ideas of -convergence, -convergence, -limit points, and -cluster points in the same context. The study seems to have uncovered an interesting relationship between -convergence and -convergence in the framework of random 2-normed spaces, emphasizing how they are interconnected. The example demonstrating the possibility that -convergence does not imply -convergence adds depth to the findings, underscoring the importance of condition (AP3) in the context of summability using ideals. Additionally, the exploration of the relationship between properties (AP) and (AP3) further enriches the understanding of these conditions, showcasing how the latter is comparatively less stringent than the former.
To further advance this research, future efforts should consider the following avenues: Firstly, we should explore the potential applicability of the established findings in diverse mathematical contexts, thereby broadening the scope of analysis to encompass more general frameworks and associated structures. Secondly, we should delve into additional case studies and counterexamples that can effectively elucidate the intricacies and subtleties inherent in the defined concepts, thereby fostering a deeper comprehension of the conditions and their implications. Thirdly, we should undertake a more rigorous examination of the relationship between conditions (AP) and (AP3), taking into account their ramifications across various theoretical frameworks and their potential impact on pertinent theorems and conjectures. Lastly, we should conduct comparative studies to juxtapose the results obtained within the realm of random 2-normed spaces with analogous research in alternative settings, such as normed spaces, metric spaces, and other correlated mathematical structures, thus facilitating a comprehensive understanding of the distinctive characteristics of the discoveries. By addressing these aspects, this research has the potential to significantly contribute to the comprehension of the interplay between -convergence, -convergence, and related concepts within the domain of random 2-normed spaces, thereby fostering enrichment within the broader landscape of mathematical analysis.