1. Introduction and Preliminaries
Sequence spaces have been fundamental to the development of functional analysis from its early stages. Among the most significant spaces are
, and
which emerged as important specific cases of Banach spaces. These sequence spaces are widely utilized across various areas within functional analysis, including the theory of functions, locally convex spaces, summability invariance, and matrix transformations. Their significance in these areas is well-documented in the literature (see [
1,
2,
3,
4,
5] and references therein), highlighting their crucial role in advancing both theoretical and applied aspects of the field.
In numerous investigations, including the work of Mursaleen et al. [
6], sequence spaces defined by Orlicz functions have been a primary focus. The concept of
-convergence, introduced by Kostyrko et al. [
7], extends statistical convergence by utilizing an ideal on the set of natural numbers to establish this generalization. Subsequent studies, such as those by [
8,
9], have explored sequence spaces using the notion of
-convergence. Additionally, Kuratowski [
10] introduced the concept of an ideal on a nonempty set, which has further contributed to the development and understanding of these mathematical structures. Sequence spaces can be further generalized through the introduction of paranorms, as seen in paranormed sequence spaces, which extend traditional norm concepts by relaxing certain norm properties. Additionally, the incorporation of various convergence criteria, such as
-convergence and Musielak–Orlicz functions, provides new insights into sequence behavior and broadens the scope of analysis. These extensions allow for the exploration of more complex relationships and properties within sequence spaces.
Definition 1. A paranormed space is a vector space equipped with a function called a paranorm, which satisfies the following conditions:
- 1.
for all ;
- 2.
if and only if ;
- 3.
for all ;
- 4.
for all .
An ideal is defined as a nonempty set of subsets of X that has the following properties:
- (i)
If and then ;
- (ii)
If and then .
We define as non-trivial if it does not equal the empty set and . Additionally, is considered admissible if it satisfies the condition of being non-trivial and for each A non-trivial ideal is maximal if there does not exist any nontrivial ideal such that .
Definition 2. A sequence is said to be -convergent to a number L if for every , . In this case we write
The concept of difference sequence spaces was first introduced by Kızmaz [
11], who studied the space
, where
represents the spaces
, and
,
for all
and
denotes the space of all real or complex sequences. This concept was later expanded by Tripathy et al. [
12], who introduced generalized difference operators. For non-negative integers
n and
m, they defined the sequence spaces
, for
where
and
for each
. The difference operator
can be expressed using the binomial formula as in [
13] by
Further, in the context of paranormed spaces and Musielak–Orlicz function spaces,
-convergence has been studied to explore new sequence spaces that offer more refined properties. Khan and Tuba [
14] explored the application of ideal convergence within the context of paranormed sequence spaces, specifically those defined by the Jordan totient function. Their work contributes to the growing body of research that investigates the interplay between ideal convergence and functional spaces, expanding its applicability to new mathematical structures and providing deeper insights into the nature of convergence under different norms. The study of
-convergence has also been enriched by examining its interaction with other sequence space constructions, such as those defined by Musielak–Orlicz functions and modulus functions. These developments have opened new avenues for research in both theoretical and applied mathematics. Mursaleen [
15] introduced several sequence spaces by employing the concepts of ideal convergence and Musielak–Orlicz functions. The concept of
-convergence has been significantly generalized and extended in various directions in recent years. For instance, Tripathy et al. [
16] investigate generalized difference ideal convergence within the framework of generalized probabilistic
n-normed spaces. Their study aims to bridge the gap between ideal convergence and probabilistic normed spaces, providing new insights into how sequences converge under different probabilistic norms.
Malik and Das [
17] expanded on this concept by investigating the
-convergence of sequences of subspaces in inner product spaces, a crucial area in functional analysis with implications for various applications, including quantum mechanics and signal processing. Their work offers a fresh perspective on the behavior of subspaces under ideal convergence, exploring how this generalized notion can capture convergence phenomena that are not observable through classical methods. For further details on ideal convergence, please refer to references ([
18,
19,
20,
21]) and references therein.
Tang and Xiong’s [
22] study investigates how sequences in quasi-metric spaces can be analyzed through the lens of
-convergence, focusing on the conditions under which a sequence is
-convergent. They explore the implications of
-convergence for the structure of quasi-metric spaces, providing new theoretical results and extending the application of
-convergence to these more general spaces. These studies underscore the rich interplay between
-convergence and paranormed spaces, expanding the theoretical framework and offering new insights into their practical applications. For more information about the ideal convergent sequence, we refer to ([
23,
24,
25,
26,
27,
28,
29,
30,
31,
32]) and references therein. In recent developments, various authors have extended the classical difference sequence spaces by incorporating more generalized difference operators and examining their interaction with various functional constructs such as Musielak–Orlicz functions and ideal convergence. These advancements have led to the creation of new sequence spaces that offer greater flexibility and applicability in both theoretical and practical settings. For more information about these, see ([
33,
34,
35,
36]) and references therein.
Suppose that
X is a linear space. A function
is known to be convex, if the following inequality holds:
for all
, and
.
A sequence space X is solid (or normal) if whenever for all sequences of scalars with for all .
Theorem 1 ([
13,
37])
. Let f be a convex function with . Then for all . An Orlicz function is a function
which is continuous, non-decreasing, and convex, with
, and
, as
An Orlicz function
M is said to satisfy the
-condition, if for each
there exists a constant
such that
Musielak–Orlicz functions are known to be sequence
of Orlicz functions ([
38,
39]). A sequence
is defined by
which is called the complementary function of a Musielak–Orlicz function
; the Musielak–Orlicz sequence space
and its subspace
are defined as follows,
where
w is the space of all real or complex sequences and
is a convex modular defined by
Theorem 2 ([
13,
37])
. An Orlicz function M satisfies the -condition if and only if for each and each there exists a constant such that Notice that the function where and , is an Orlicz function that satisfies the -condition, as
Recent advancements have expanded this field by incorporating Musielak–Orlicz functions and ideal convergence. Musielak–Orlicz functions, which generalize the classical Orlicz functions, allow for a broader and more flexible approach to defining and studying sequence spaces. These functions enable the construction of sequence spaces that accommodate a wide range of growth conditions and convergence criteria. The primary objective of this paper is to present the concept of -convergence for sequences along with an investigation of Musielak–Orlicz functions of order .
The sequence spaces we are defining play a crucial role in extending the theory of sequence spaces by incorporating -convergence and Musielak–Orlicz functions. These spaces are significant in several areas, including summability theory, where they refine the analysis of series convergence; functional analysis, by providing new frameworks for understanding operator behavior; and approximation theory, where they enhance methods for approximating functions. Their importance extends to practical applications such as mathematical modeling and numerical methods, where they offer sophisticated tools for handling various convergence criteria and sequence behaviors.
The spaces , , and are essential in various mathematical and applied fields. They are utilized in approximation theory to assess function convergence and approximation properties. In functional analysis, they help in understanding the behavior of linear operators and functionals, particularly in generalized settings where traditional norms might be too restrictive, and in the theory of differential equations to analyze solution properties and stability. Additionally, these spaces find applications in control theory for system design and signal processing for analyzing signal behavior and transformations. Their ability to handle different types of convergence and function spaces makes them valuable tools in both theoretical and practical contexts.
3. Main Results
Let
be a Musielak–Orlicz function and
be a bounded sequence and
For each
Then we define the following sequence spaces as the following:
and
In this section, we study some topological and algebraic properties of the above defined sequence spaces.
Firstly, we construct some examples related these spaces:
Example 1. Consider the sequence defined by for and Let be a Musielak–Orlicz function and . Then the sequence ξ belongs to the space ifThis condition is satisfied if , i.e., for sufficiently large p. Thus, Example 2. Consider the sequence for and Let be a Musielak–Orlicz function and . If , then because Example 3. Let for with Musielak–Orlicz function and . For , the sequence is bounded and belongs to the space because Example 4. Take and Musielak–Orlicz function with . The sequence if Theorem 3. If be a Musielak–Orlicz function, then and are linear spaces over the field of complex number .
Proof. Suppose that , and .
For
to be given, we have to show that there exist
and
such that
Since
, there exist two numbers
and
such that
and
where
and
Assume that and Then, by using the triangular inequality and also the fact is non-decreasing, we have
Since
is convex. Thus, we have
Let
Then,
If
, then from last inequality, we have
this implies
. Thus, if
and
, it follows that
. Therefore,
and hence,
is a linear space. Similarly, we establish that
is a linear space. □
Theorem 4. If is a Musielak–Orlicz function, then is a linear space over the field of complex number .
Proof. Suppose that
, and
We have to show that there exist two positive numbers
Q and
such that
By hypothesis, there exist four positive numbers
, and
such that
and
where
and
.
Suppose that and Thus, we have
Let
Then,
If
then from last inequality, we have
this implies that
. Thus, if
and hence,
, it follows that
Therefore
. Hence,
is a linear space over the field of complex number
. □
Corollary 1. If be a Musielak–Orlicz function, then and are linear spaces over the field of complex number .
Theorem 5. The spaces and are paranormed spaces with paranorm defined bywhere Proof. It is clear that Since we get . Let us take and in .
Let
Let
and
. If
, then we have
Thus
and
Suppose that
where
, and
as
.
We have to show that as .
Let
If
and
, then we observe that
From the above inequality, it follows that
and consequently,
This completes the proof. □
Theorem 6. Let and be two Musielak–Orlicz functions which satisfies the -condition. The following statement holds:
- 1.
If be a bounded sequence with then for
- 2.
for
Proof. (1) Let
. There exists
such that
Given
. Since each
is continuous at 0 from right, there exists
such that
implies that
Suppose that
for each
. Defining the sets
and
Observe that
If
then
By Theorem
, we have
and so,
For
we have
Since each
satisfies the
-condition and non-decreasing for each
, by Theorem
, there exists a constant
such that
Thus,
Therefore, from the above result, it follows that
Since
we have
Therefore, and hence
(2) Let
There exist two numbers
and
such that
and
Let
and
. By applying Maddox’s inequality, we get
Therefore, from the above inequality, we get
Hence,
Therefore,
Theorem 6 is proved. □
Theorem 7. If then the following inclusion holds:
- 1.
,
- 2.
,
- 3.
,
- 4.
,
- 5.
.
Proof. We have to prove
only. The proof of remaining parts directly follows from
. Assume that
. For
to be given, we have to show that there exists
and
such that
Since
, there exists
and
such that
where
and
Suppose
and
. Since
is non-decreasing and convex for each
. Put
, we have
Let
Then,
If
then from above inequality, we have
this implies that
. Therefore,
and hence
. Therefore,
and so
. □
Theorem 8. The sequence spaces and are solid and hence monotone.
Proof. Let
and let
be a sequence of scalars with
for each
. There exist two positive numbers
and
Q such that
Let
If
, then
which implies that
. Thus,
and hence,
This shows that
for all sequence of scalars
with
for each
, whenever
. Therefore,
is solid. Similarly, we provided a proof for
. □