1. Introduction
A set, according to classical set theory, is a well-defined collection of objects, where the order of elements is irrelevant and each element occurs only once. However, in some practical situations, the repetition of certain elements is meaningful. For example, a digit in a cellphone number can appear multiple times, and duplicates often arise during different phases of information retrieval. At this point, the concept of multiset becomes important. Let
X represent a non-empty set. A multiset
M derived from
X includes elements
with a multiplicity function
, where
. The positive integer
signifies the multiplicity of element
a. For instance, the collection
and
forms a multiset since 4 appears twice, 9 appears four times, and 8 appears twice. This multiset can be represented as
, indicating that
, and
. Multiset theory addresses situations where classical set theory may be inadequate. In 1980, Hickman [
1] investigated algebraic operations on multisets, and in 1981, Knuth examined the applications of multisets in computer programming [
2]. Bender [
3] focused on the partitioning of multisets, while Lake [
4] provided an axiomatic framework for multiset theory in 1976. Majumdar [
5] introduced soft multisets, exploring the concepts of distance and similarity between them. The theory of multisets has several applications in computer and information sciences, as discussed in references such as [
3,
6,
7,
8,
9]. In 2021, Pachilangode and John [
10] introduced a distance metric
in a metric space
, and further investigated Wijsman convergence and Hausdorff convergence in relation to multisets.
The concept of statistical convergence has evolved from the idea of convergence for real number sequences, initially proposed independently by Fast [
11] and Steinhaus [
12], and subsequently explored by [
13]. Statistical convergence is characterised by the asymptotic density of certain subsets of natural numbers. Specifically, for a subset
, the asymptotic density is defined as
, provided this limit exists. In 1980, Šalát [
14] analysed the collection of all statistically convergent sequences in
under the supremum norm, proving that this collection is dense in
. In 1985, Fridy [
15] introduced the idea of statistically Cauchy sequences and investigated the relationship between statistical convergence and these sequences. In 2000, Kostyrko et al. [
16] further developed statistical convergence for real number sequences by introducing
-convergence, where
denotes an ideal on the natural numbers
, applicable to sequences in a metric space
. They also defined and studied
-limit points and
-cluster points for sequences of elements in a metric space
. In [
17], Gürdal defined the notion of
-Cauchy sequences of real numbers, highlighting their connection to
-convergence for real number sequences (see also [
18]). In this work, he also provided definitions for the
-Cauchy sequence and
-Cauchy sequence of elements in a metric space
and established various relationships between
-convergence and
-Cauchy (see also [
19]). Various papers on their associated sequence spaces may be found in [
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31].
In 2021, Debnath and Debnath [
32] introduced the notion of statistical convergence specifically for multiset sequences, detailing several associated properties of this type of convergence. They also established a definition of statistical boundedness for multiset sequences and investigated the connections between statistical boundedness and statistical convergence. In 2023, Demir and Gümüş [
33] focused on the concept of ideal convergence in multiset sequences.
The necessary definitions and properties for this study can be found in [
10,
16,
19,
24,
32].
The purpose of this research is to explore the concept of multisets, which, unlike in classical set theory where elements are listed only once, allow for the repetition of elements in a set, with each repetition being essential. This phenomenon is commonly observed in real-world situations, highlighting the significance of multisets. Additionally, the study investigates ideal convergence, a convergence type that generalises several other forms of convergence. The aim is to understand how ideal convergence can be applied to multiset sequences and to identify the properties it offers in this context.
The framework of multisets plays an integral role in computer science and information theory, and recent years have witnessed a surge in studies investigating multiset sequences. Inspired by these developments, this work expands upon the results presented in [
33]. The structure of this paper is organised as follows.
Section 2 provides foundational definitions and properties related to ideals and multiset sequences.
Section 3 delves into the concepts of
-lacunary statistical limits (
) and
-lacunary statistical cluster (
) points for multiset sequences and examines their characteristics. Additionally, we analyse the interplay between
-lacunary statistical convergence and
-lacunary statistical limit points, as well as
-statistical cluster points. Moreover, we establish the definitions of
-lacunary statistical Cauchy and
-lacunary statistical Cauchy multiset sequences. Our findings demonstrate that a multiset sequence is
-lacunary statistically convergent if and only if it satisfies the condition of being
-lacunary statistically Cauchy (
). We further show that every
-lacunary statistically Cauchy (
) multiset sequence is also
-lacunary statistically Cauchy. An illustrative example is provided to highlight the existence of ideals
for which the notions of
-lacunary statistical Cauchy and
-lacunary statistical Cauchy are different. Furthermore, it is proven that the concepts of
-lacunary statistical Cauchy and
-lacunary statistical are equivalent when
adheres to the weak additive property.
2. and Points for Multiset Sequences
Let
be a metric space. On a multiset
M whose elements are from
Z, one can define different types of metric; for more details, see [
10]. In this paper, we consider the metric
on
M. Throughout the paper,
d will denote the usual metric on
,
.
Definition 1 ([10]). A multiset of real numbers, denoted by , is a set in which the repetition of real numbers is permitted. Thus, . Definition 2 ([10]). A function whose domain is the set of natural numbers and co-domain is is called a multiset sequence of (or, simply, a multisequence). We denote a multiset sequence by , where and for each . Example 1. Let . Thus, represents a multiset sequence, where the term contains elements.
Example 2. The integer i is fully decomposed into its prime factors. Let represent the multiset of these factors, including 1. For example, , , , , and . According to this definition, forms a multiset sequence.
Throughout the paper, whenever we use the term multiset sequence, we will denote the multiset sequence of real numbers by unless otherwise stated. Also, for a set , the complement of is represented as .
By a lacunary sequence, we mean an increasing integer sequence of non-negative integers such that and as . Throughout this paper, the intervals determined by will be denoted by .
A family of sets (power set of ) is said to be an ideal if is additive and hereditary. A non-empty family of sets is a filter on if and only if for each and any superset of an element of is in An ideal is called nontrivial if and Clearly, is a nontrivial ideal if and only if is a filter in , called the filter associated with the ideal . A non-trivial ideal is called admissible if and only if .
We introduce the notions of -lacunary statistical limit points and -lacunary statistical cluster points for a sequence of multisets as follows:
Definition 3. Let be a multiset sequence. An element is called an -lacunary statistical limit point of if there exists a set such that the set and the multiset sequence is lacunary statistically convergent to . Here represents the collection of all -lacunary statistical limit points of .
Definition 4. Let be a multiset sequence. An element is called an -lacunary statistical cluster point of if for all , :Here, represents the collection of all -lacunary statistical cluster points of . Definition 5. A multiset sequence is called -lacunary statistically convergent to if for every :and we write . Definition 6. A multiset sequence by is called -statistical convergent to if there exists a set such that the multisubsequence is lacunary statistically convergent to . It is represented as .
Theorem 1. Assume denotes any proper nontrivial admissible ideal. If , then .
Proof. Let
. As per the assumption, there exists a set
in which
and we have
, i.e.,
Since
is a proper nontrivial and admissible ideal, we deduce that
for any
. Then,
so,
. This gives the desired outcome. □
Theorem 2. Assume denotes any proper nontrivial admissible ideal. If is a multiset sequence, then we obtain .
Proof. Suppose
to be an
-lacunary statistical limit point of
. In this case, there exists a set
such that the set
and the multiset sequence
is lacunary statistically convergent to
. So, we have
Take
, so there exists
such that for
, we have
Let
In addition, we have
, considering that
is an admissible ideal and
. Hence, as per to the definition of
-lacunary statistical cluster point,
. □
But, the opposite of Theorem 2 is not valid. To illustrate this, consider a mutually disjoint partition
of
such that each
is infinite. If we take
then
becomes a proper nontrivial admissible ideal. Define a multiset sequence
with same multiplicity
p by
Now, consider the multiset
. Then, for any
So,
is an
-lacunary statistical cluster point of
. Also, it can be easily verified that
is not an
-lacunary statistical limit point of
.
Theorem 3. Assume is a multiset sequence and is a proper nontrivial admissible ideal on . Let be -lacunary statistically convergent to . Then, is an -lacunary statistical limit point of .
Proof. Since
is
-lacunary statistically convergent to
, for each
,
, the set
where
is a proper nontrivial admissible ideal on
. Suppose that
involves
different from
, i.e.,
. So, there is a
,
such that
is lacunary statistical convergent to
. Let
Hence,
P is a finite set, and so it belongs to
. Thus, we obtain
Again, let
So
, i.e.,
. Therefore,
, since
. Let
, and take
, so
and
, i.e., for maximum
will satisfy
and
for a very small
. Thus, we have to obtain
which is a contradiction because the neighbourhoods of
and
are disjoint. So, we obtain
. This gives the desired outcome. □
Theorem 4. Let be a multiset sequence and be a proper nontrivial admissible ideal on . Let be -lacunary statistically convergent to . Then, is an -lacunary statistical cluster point of .
Proof. Since
is
-lacunary statistically convergent to
, for
,
,
Therefore,
Otherwise,
, which is not possible. This shows that
is an
-lacunary statistical cluster point of
. □
Theorem 5. For any multiset sequence , is closed.
Proof. Suppose
is a limit point of the set
; then, for every
, where
. Let
and choose
such that
. Then, we have
Therefore,
For any
Since
, then, we have
Namely,
. Hence, the theorem is proven. □
Theorem 6. Assume is a multiset sequence and is a proper nontrivial admissible ideal on . Let be -lacunary statistical convergent to . Then, is an -lacunary statistical limit point of .
Theorem 7. Let be a multiset sequence and be two ideals on with . If is an -lacunary statistical limit point (-lacunary statistical cluster point) of , then is an -lacunary statistical limit point (-lacunary statistical cluster point) of .
Proof. Let be a multiset sequence and and be two ideals in with . Let be an -lacunary statistical limit point of . Thus, there exists a set such that the multisubsequence is lacunary statistically convergent to . Since , is also an -lacunary limit point of . Similarly, we can show that is an -lacunary statistical cluster point of . □
Theorem 8. Let and be two multiset sequences such thatthen 1. and
2. .
Proof. 1. Let
. Hence, according to the definition, there exists a set
such that
and
. Since
therefore,
and
. So, we have
. This demonstrates that
, and hence,
. By symmetry,
. Therefore, we obtain
.
2. Let
. So, by definition, for all
,
Let
Now, we demonstrate that
. First, let us assume that
. Hence,
By theory, the set
belongs to
. Hence,
. In addition, it is obvious that
This means that
. This is a contradiction. Thus,
and so the outcome is proven. □
Now, we introduce the notion of
-
and
-
for a multiset sequence
with respect to a proper nontrivial admissible ideal
. The introduced notion will improve the corresponding notion in [
32] as the results will become a particular case for the density zero ideal
. We start with a multiset sequence
. Consider the two sets
and
is called the supremum of
, denoted by
, if
c is the greatest multiplicity in
under the condition
in
and
is the supremum among the different sets of real numbers bearing the multiplicity
c in
, whenever it exists. Similarly,
is defined in the same way as in [
32].
Definition 7. Let be a multiset sequence and be a proper nontrivial admissible ideal on . Then, we define Example 3. Take the ideal containing all finite subsets of . Let be defined byHere, it can be shown thatSo, . Also,So, . Theorem 9. Let be a multiset sequence and be a nontrivial proper admissible ideal on . Then, and are unique.
Proof. The proof is easy; thus, it has been removed. □
Theorem 10. Let be a multiset sequence. If , then, for every ,
(1) .
(2) .
Proof. Since
-
, then
r is the greatest multiplicities in
and
p is the supremum of all real numbers whose multiplicity is
r. Let
. So, there exists a real number
q with
and
. Since
,
This shows that
On the other side, for
, if
then
, and this will contradict the fact that
p is the supremum of all real numbers whose multiplicity is
r. So, we have
□
The proof of the following theorem can be given similarly to Theorem 10.
Theorem 11. Let be a multiset sequence. If , then, for every ,
(1) .
(2) .
3. and Multiset Sequences
This section introduces and investigates -lacunary and -lacunary statistical Cauchy sequences. We demonstrate links between the ideas of -lacunary statistical convergence, -lacunary statistical convergence, -lacunary statistical Cauchy, and -lacunary statistical Cauchy multiset sequences.
Definition 8. Let be a multiset sequence. Then, is called -lacunary statistical Cauchy if for each , , there exists such that Example 4. Let be an admissible ideal on such that , where A is an infinite subset of . Let be any -lacunary statistical Cauchy sequence of real numbers. Define a sequence of positive integers as follows:Clearly, the multiset sequence is -lacunary statistical Cauchy. Remark 1. Since is an admissible ideal, every multiset Cauchy sequence is -Cauchy. In particular, if is the ideal of all finite subsets of , then the notions of multiset Cauchy sequences and -Cauchy sequences are equivalent. Let be the collection of all density-zero subsets of . Then, is a non-trivial admissible ideal on . A multiset sequence is called lacunary statistically Cauchy if and only if it is -lacunary Cauchy.
Theorem 12. A multiset sequence is -lacunary statistically convergent if and only if it is -lacunary statistical Cauchy.
Proof. Assume
is
-lacunary statistically convergent to
. Then, for any
,
Choose
and fix it. Then,
Also, for all
, we have
Then, we have
for all
. Consequently,
Since
, we have
Hence,
is
-lacunary statistical Cauchy.
Conversely, let
be
-lacunary statistical Cauchy. Then, for any
,
, there exists a positive integer
such that
Set
for
. Hence, for all
there exists
such that
Define recursively,
,
and
for
. We claim that both
and
are nonempty for every
. Really, since
, for each
, we have
. Since both
and
are less than or equal to
,
and
. Similarly,
and
for all
and
. Let
and
such that
and
for each
. Then,
and
for all
. Furthermore, since
and
are nested sequences of closed sets of real numbers whose diameters tend to zero, there exist
such that
and
. We show that
is
-lacunary statistically convergent to
. Let
be given. So, there exists
such that
. Let
If
is empty, then
. Let
. Then, for all
, we have
Since
either
or
. But,
and
, that is,
and
. Therefore,
Thus,
Hence the multiset sequence
is
-lacunary statistically convergent to
. □
Definition 9. A multiset sequence is called -lacunary statistically Cauchy if there exists a set such that the submultiset sequence is lacunary statistically Cauchy.
Proposition 1. A multiset sequence is -lacunary statistically convergent if and only if it is -lacunary statistically Cauchy.
Theorem 13. If a multiset sequence is -lacunary statistically Cauchy, then it is -lacunary statistically Cauchy.
Proof. Assume
is
-lacunary statistically Cauchy. Then, there exists a set
such that for all
and for any
, there exists
such that for all
, we deduce that
Set
and
Clearly,
. Since the latter set is in
, so does
. Hence,
is
-lacunary statistically Cauchy. □
The following example shows that the concepts of -lacunary statistically Cauchy and -lacunary statistically Cauchy of multiset sequences are not generally identical:
Example 5. Consider , a decomposition of where each is infinite and for . Let be the class of all subsets A of that overlap at most a finite number of s. It is easy to verify that is a non-trivial admissible ideal of . Construct a multiset sequence as follows: if and for all . Let η and σ be greater than zero. Then, there is such that . Let be the least positive integer that belongs to . LetClearly, . Since the latter set belongs to , so does . Therefore, the multiset sequence is -lacunary statistically Cauchy. We demonstrate that is not -lacunary statistically Cauchy. Suppose, on the contrary, is not -lacunary statistically Cauchy. Hence, there exists a setsuch that for all and for any there exists such that for every , we deduce thatFrom the definition of , we have for some . Thus, for all . Therefore, there are infinitely many terms of the sequence equal to and . Let . Then, there does not exist any such thatholds for all . This is a contradiction. Thus, is not -lacunary statistically Cauchy. Definition 10. An admissible ideal on has the weakly additive property (WAP) if for any sequence of mutually disjoint sets in , there exists a set such that is finite for every .
From [
19] (Lemma 4), we observe that if an admissible ideal has the property (AP), it also has the property (WAP).
In order to give a sufficient requirement for a multiset -lacunary statistically Cauchy sequence to be an -lacunary statistically Cauchy, we now add the following theorem. Also, let be an admissible ideal and have the property (WAP) in the following two results.
Theorem 14. If a multiset sequence is -lacunary statistically Cauchy, then it is -lacunary statistically Cauchy.
Proof. Assume
is
-lacunary statistically Cauchy. Then, for all
and for any
, there exists a positive integer
such that
Set
for all
. Clearly,
for all
. Since
has the property (WAP), we have a set
such that
is finite for all
. Let
be given. Hence, there exists
such that
. Since
is finite, there exists a positive integer
such that
for every
and
. Therefore,
and
for all
and
. Hence, by the triangle inequality of the usual norm of
and the metric
d, we have
for all
and
. With the results obtained, it is proved that
is
-lacunary statistically Cauchy. □
Corollary 1. The notions of -lacunary statistically Cauchy, -statistically convergent, -lacunary statistically Cauchy, and -lacunary statistically convergent of multiset sequences are equivalent.
4. Discussion
In this section, we will specify the cases where -statistical convergence and -lacunary statistical convergence coincide with statistical and lacunary statistical convergence, as well as the types of ideals where they do not coincide.
Let
(asymptotic density) and
(logarithmic density) denote the class of all
for which
(
, respectively). Then,
and
are non-trivial admissible ideals.
-convergence coincides with the statistical convergence, while
-convergence is referred to as logarithmic statistical convergence, as given by Kostyrko et al. [
16].
Savaş and Das [
20] demonstrated that a sequence is
-statistically convergent (according to Theorem 2.3 in [
20]) but not statistically convergent (see Remark 2 in [
20]) by selecting
(the ideal of density zero sets of
). They raised an open problem, suggesting that for an arbitrary admissible ideal,
-lacunary statistical convergence does not coincide with lacunary statistical convergence.
In ([
34], Problem 6.1), Das raised the question of identifying ideals where
-statistical convergence diverges from statistical convergence. Filipów and Tryba offered a partial resolution to this problem (refer to ([
35], Theorem 7.16)) by employing the concept of gap density introduced by Grekos and Volkmann [
36].
Definition 11 ([36]). The gap density of a set is defined aswhere represents the increasing enumeration of the set . Filipów and Tryba [
35] introduced two classes of ideals related to Das’s problem by utilising the concept of gap density.
Definition 12 ([35]). An ideal is said to have property (D) if, for any M, there exists such that . Definition 13 ([35]). An ideal is said to have property () if there exists such that . (Sets with infinite gap density are referred to as thin sets in [37]). The property (D) ((), resp.) serves as a necessary (or sufficient, respectively) condition for an ideal to differentiate between -statistical convergence and statistical convergence.
Proposition 2 ([35]). If there exists an -statistically convergent sequence that is not statistically convergent, then has the property (D). Proposition 3 ([35]). If has the property (), then there exists an -statistically convergent sequence that is not statistically convergent. An ideal is a P-ideal if for every countable family , there is such that is finite for every .
For
P-ideals, the property (
D) characterises these ideals (Theorem 15), offering a partial solution to a problem posed by Das ([
34], Problem 6.1).
Theorem 15 ([35]). Let be a P-ideal. There exists an -statistically convergent sequence that is not statistically convergent if and only if has the property (D). Balcerzak and Leonetti [
23] showed that
-statistical convergence coincides with
-convergence, for some unique ideal
, and in the same study, they proved that if
is the summable ideal
or the density-zero ideal
, then
-statistical convergence coincides with statistical convergence.
Corollary 2 ([23]). Let be an ideal such that . Then, -statistical convergence coincides with statistical convergence. Moreover, as a special case of Corollary 2, they derived the following result:
Corollary 3 ([23]). -statistical convergence coincides with statistical convergence if or Fin, where Fin= Finally, they demonstrated that the result of Corollary 2 cannot be extended to encompass the entire class of ideals . In particular, this is not possible when is a maximal ideal.
Theorem 16 ([23]). Let be a maximal ideal. Then, -statistical convergence does not coincide with statistical convergence. Our study is developed by considering the classes of ideals for which -statistical convergence and -lacunary statistical convergence do not coincide with statistical and lacunary statistical convergence, respectively. All theorem proofs are conducted in this context.
Now, using the results in the literature given above, we can say that different proofs can be obtained for our alternative main theorems, Theorem 3, Theorem 4, Theorem 9, Theorem 10, and Theorem 11. In the literature, it has been mentioned that the concepts coincide in the case of zero-density ideals. However, this is not the case for arbitrary admissible ideals, as pointed out in the works of Balcerzak-Leonetti [
23], Das [
34], and Savaş and Das [
20]. Throughout our study, we have developed and proved our theorems mentioned above using arbitrary admissible ideals.
Therefore, in our study,
-statistical convergence and
-lacunary statistical convergence do not coincide with statistical and lacunary statistical convergence, respectively. If we take an ideal with ideal
as in Balcerzak and Leonetti [
23], our results are reduced to the results obtained with the concept of statistical convergence. On the other hand, if we take the ideal maximum instead of the ideal used in our main theorems as in [
23], a different proof can be carried out by means of a submeasured sequence and results related to the
-statistical concept can be obtained, which again, does not coincide with statistical convergence.
Also, in the above literature, Filipów and Tryba [
35] introduced two classes of ideals, defined as (
D) and (
) properties, related to Das’s problem using the notion of gap density. If, instead of the ideal used in our main theorems Theorem 3, Theorem 4, Theorem 9, Theorem 10, and Theorem 11, we take the
P-ideal satisfying certain conditions for each countable family and the ideal satisfying property (
D), we can obtain
-statistical results with a different proof method that does not conflict with the statistical concept.
Considering the existing works in the above literature [
23,
35], we leave open questions for the interested reader to “characterise” the class of ideals
for which
-statistical convergence coincides with statistical convergence for the class of ideals considered in our main theorems, and to determine whether the case where
-statistical convergence does not coincide with statistical convergence for the maximal ideal holds for unmeasurable ideals or ideals without the Baire property.