Abstract
This paper introduces a new concept of random Sehgal contraction in the setting of random cone metric spaces. We explore the modern advancements of traditional fixed-point theorems in a random setting, elaborating on the Sehgal–Guseman fixed-point theorem within the realm of random cone metric spaces. A significant aspect of our research is the interplay between symmetry and randomness; while symmetry provides a framework for understanding structural properties, randomness introduces complexity, which can lead to unexpected behaviors. Our research provides a deeper understanding of the classical results and incorporates a detailed example to illustrate our findings. In addition, major random fixed-point results are also established, which could be applied to nonlinear random fractional differential equations (FDEs) and integral equations as well as to random boundary value problems (BVPs) related to homogeneous random transverse bars.
Keywords:
random fixed point; measurable space; random operator; random Sehgal contraction; random cone metric space MSC:
47H10; 54H25
1. Introduction
The Banach contraction principle (BCP), introduced in 1922 by esteemed mathematician Stefan Banach [1], stands as a cornerstone in analysis, serving as a fixed-point theorem across many fields. Specific contractive conditions apply to a mapping within a complete metric space, which can be easily assessed in various contexts. The versatility and applicability of this principle have been demonstrated by numerous generalizations over the years, providing insights into the convergence rate toward the fixed point as well as establishing the existence of fixed points. The BCP proves the existence of fixed points by a sequence of approximations. According to Bryant [2], the BCP was interpreted in a broader way in 1968. Self-mappings, even if they are not strictly contractions, could still meet the contraction criteria for the power, thereby establishing that they have fixed points.
Sehgal contractions are generalizations of the BCP. They were first proposed by Sehgal [3] in 1969. Based on Bryant’s idea, Sehgal created a contraction in which each point g in the underlying space is represented by a natural number . This contraction satisfies the contraction criterion and yields the fixed point for the self-mapping, being characterized by the power of the self-mapping. Fixed-point techniques for cone metric spaces (CMSs) were provided by Raja and Vaezpour [4].
In 2007, Huang and Zhang [5] presented CMSs, which enhance conventional metric spaces through the incorporation of a cone structure. They established fixed-point theorems for contractive mappings within this novel framework. These CMSs have spurred various extensions of fixed-point theorems, including those by Kannan and Chatterjea. More recently, Du [6] introduced a more generalized framework known as the topological vector space and CMS, which remains an active area of research in fixed-point theory. Subsequent research by authors such as Abbas [7], Ilic [8], Rezapour [9], and Vetro [10] further explored the existence of fixed points which meet contractive-type conditions in normal CMSs. Fixed-point results for multi-valued mappings in cone metric spaces were obtained in [11]. More related applications in this direction can be seen in [12,13,14].
Probabilistic functional analysis is a rapidly evolving field in mathematical research, fueled by its practical applications in probabilistic models. Proficiency in random operator theory becomes imperative when tackling various forms of random equations. Indeed, many mathematical models utilized in physics, engineering, and the biological sciences involve unspecified parameters or coefficients of particular significance. Therefore, it is more practical to view these equations as random operator equations. Solving these equations numerically presents considerably more difficulties compared with deterministic equations. Among others, the authors of [15,16] made significant contributions to understanding the mathematical properties of random equations.
Random nonlinear analysis constitutes a vital field of mathematics essential for investigating various types of random equations, predominantly focusing on random nonlinear operators and their characteristics. The exploration of random fixed-point theory traces back to the 1950s with the emergence of the Prague School of Probabilities [17,18,19]. By extending classical common fixed-point theorems to stochastic settings, one can derive common random fixed-point theorems. The application of random fixed-point theory proves particularly relevant in addressing numerous challenges arising in economic theory (as exemplified in, for instance, [17]) and its associated research.
The survey paper authored by Bharucha-Reid [20] ignited substantial interest among mathematicians, infusing fresh energy into the theory. Itoh [21] expanded the theorem established by Spacek and Hans to multi-valued contraction mappings (also referenced in [22,23,24,25]). Recently, there has been a surge in research focusing on applying concepts from random fixed-point theory to nonlinear random systems. This emerging area has seen significant developments, with studies exploring various aspects and implications (refer to [26,27,28,29]).
Beg [30,31] and Papageorgiou [32,33] introduced fixed-point theorems for contractive random operators by leveraging common random fixed points of pairs of compatible random operators in Polish spaces. This development was centered around utilizing correlations between these operators to establish the fixed-point results.
Scientists have been able to simulate situations with uncertainty and randomness by extending the Banach contraction principle to include randomness. Nonetheless, there are still many problems for which a random operator cannot be made to converge and stabilize. Improving the random BCP to encourage faster convergence of the sequence toward the solution is one way to tackle this problem.
Rezapour and Hamlbarani [9] made a significant advance in fixed-point theory for cone metric spaces in 2008 by doing away with the need for normalcy. The idea of a random cone metric space (RCMS) was first presented by Mehta et al. [34], who proved that a random fixed point exists in weak contraction scenarios.
Building on Ri’s research, Aydi et al. [35] extensively analyzed Ri’s findings, specifically focusing on exploring random solutions for the nonlinear Caputo-type FDE. Meanwhile, during the same timeframe, Baleanu et al. [36] directed their attention toward confirming the existence of a solution for the FDE. Moreover, their research extended to exploration of the dynamic behavior of a bead in random motion along a wire [37].
The motivation for this research arises from the need to expand fixed-point theory by exploring random Sehgal contractions in RCMSs. This study builds on the foundational Sehgal–Guseman fixed-point theorem ([38], Theorem 1) by extending it to encompass random operators and contractions. It provides a modern perspective which extends beyond traditional approaches like Sehgal and Banach contractions in metric spaces. The exploration of random Sehgal contractions strengthens the theoretical basis of fixed-point theorems. Additionally, it reveals practical applications for addressing nonlinear random FDEs and random BVPs. A detailed example highlights the accelerated convergence properties of random Sehgal contractions, emphasizing their effectiveness in solving complex mathematical problems involving uncertainty and randomness. By establishing significant random fixed-point results and showcasing the utility of random Sehgal contractions in nonlinear systems, this research advances the field. It offers fresh insights and solutions which extend beyond conventional contraction principles in metric spaces.
The strength of the current work on random Sehgal contractions in RCMSs lies in its ability to handle uncertainty and randomness more effectively than traditional Sehgal and Banach contractions in metric spaces. By extending the contraction principles to random operators, this approach demonstrates accelerated convergence and unique solution capabilities, particularly in solving nonlinear random FDEs and random BVPs. The adaptability of random Sehgal contractions to stochastic environments enhances their performance, making them a powerful tool in modern fixed-point theory for addressing complex mathematical problems with varying degrees of uncertainty.
This paper extends the Sehgal–Guseman-type theorems [38] to a random operator which satisfies a random contraction condition for a particular natural number . It is within this framework that we establish the existence of a random fixed point for a given random operator. An alternative approach to identifying random fixed points is presented here by introducing the concept of random Sehgal contraction. Although the random operator may not adhere to a random BCP, there exists a corresponding which fulfills the contraction condition. By examining a nontrivial case, we illustrate the effectiveness of random Sehgal contraction, which reveals accelerated convergence. In this investigation, we establish significant results regarding random fixed points, employing them to address nonlinear FDEs involving random fractions. Additionally, we utilize these findings to tackle random BVPs linked with homogeneous, random transverse bars.
2. Some Basic Results and Definitions
In this section, we present definitions, notation, and preliminary results concerning random variables. These foundational elements will be employed consistently throughout our investigation.
Definition 1
([34]). Consider a topological vector space E. A subset is said to be a cone if it fulfills the following criteria:
- (C1)
- is a nonempty closed set with ;
- (C2)
- For any and any non-negative scalars , ;
- (C3)
- If and , then .
For a given cone in E, one defines a partial ordering on E as follows:
if and only if , and if and only if , where denotes the interior of .
Definition 2
([5]). Let be a cone in a topological vector space E. Consider a nonempty set Q and a mapping satisfying the following conditions:
- (1)
- For any , , and if and only if ;
- (2)
- For any , ;
- (3)
- For any , .
The pair is called a cone metric space (in relation to a cone in E).
Throughout this paper, we suppose that the cone is such that and use the notation ≤ instead of and ≪ instead of .
Let be a cone metric space. A sequence in Q has the following properties:
- (1)
- It is convergent to if for every such that , there is such that for every ;
- (2)
- There is a Cauchy sequence if for every with , there is such that for every .
- (3)
- is complete if every Cauchy sequence in Q converges.
The Bryant fixed-point theorem, initially introduced by Bryant in 1968 and referenced in [2], serves as a notable extension of the BCP.
Theorem 1
([2]). Let f be a self-mapping of a complete metric space . If the iterated mapping demonstrates contraction-like behavior for a specific , then the set contains exactly one element.
It is worth noting that although is continuous, this does not automatically ensure the continuity of f itself.
In 1969, Sehgal ([3], Theorem) made a significant contribution by unveiling a novel result regarding the contractive iteration of every point within complete metric spaces. This discovery extended the principles of Banach’s contraction.
Theorem 2
([3]). Let be a complete metric space, be a continuous self-mapping of M, and . If for each there exists such that
for all , then the set contains exactly one element.
Random Variable
In this subsection, will be a measurable space, where is a -algebra on , M will be a nonempty subset of a cone metric space (related to a cone ), and will denote the set of all nonempty closed subsets of M.
Definition 3
([5]). A mapping is measurable if for each open subset U of M, the inverse image , where .
Definition 4
([34]). A measurable mapping is called a measurable selector of a measurable mapping if for each , belongs to .
Definition 5
([34]). A mapping is a random operator (continuous random operator) if for any fixed , the mapping is measurable (continuous).
Definition 6
([34]). A measurable mapping is said to be a random fixed point of a random operator if holds for every .
The concepts of symmetry and random fixed points have been explored through various mathematical frameworks, particularly in symmetric spaces. Symmetric spaces provide a structured environment where fixed-point theorems can be applied under certain contraction conditions and offer insights into the distribution of fixed points in random permutations. These studies reveal the intricate relationship between symmetry and fixed points, highlighting both theoretical and practical implications.
Definition 7
([34]). Consider a mapping which fulfills the following criteria for a selector :
- (1)
- is nonnegative, and it equals zero if and only if for all ;
- (2)
- for all , , and ;
- (3)
- for all , and as a selector.
- (4)
- The function is non-increasing and left-continuous for any , and .
The mapping ρ is called a random cone metric, and the pair is called a random cone metric space (shortly RCMS).
3. Main Results
This section presents the main results, followed by the necessary definitions and concepts.
Definition 8.
Consider an RCMS defined with respect to a cone . Let M be a nonempty, separable closed subset of Q, and define a continuous random operator as a random Sehgal contraction under the condition that for each , there exists such that
Lemma 1.
Consider a complete RCMS denoted by associated with a cone , where is a continuous random operator, and . For each , there exists such that
Then,
is finite.
Proof.
Let , and let
For every positive integer n, there is an integer such that s times the value of n in terms of is less than n and is less than or equal to times the value of n in terms of :
Thus, is finite. □
Now, we will introduce Guseman’s random fixed-point theorem by relaxing the requirement of continuity.
Theorem 3.
Let be a complete separable RCMS associated with a cone . Let represent a random operator for some . Suppose that for all , there exists such that
The function f possesses a unique random fixed point , and as the number of iterations tends toward infinity, applying f repeatedly to an initial value converges to for all .
Proof.
Given , we define the sequence as follows:
We establish that the sequence converges as a Cauchy sequence. Referring to Equation (2), this can be expressed as
By applying the triangle inequality, we can establish the following relationship for :
Based on Lemma 1, it follows that for , we have
This implies that the distance between and tends toward zero as n approaches infinity, and we establish that forms a Cauchy sequence. Through the completeness of , we conclude that converges to for some . Our next goal is to show that .
For any given , there exists a natural number such that
Furthermore, as .
Therefore, according to [5], It follows that
Subsequently, from Equation (2), we can derive
By iterating this reasoning n times, we arrive at the following expression:
Since , this suggests that converges to zero in the RCMS as n tends toward infinity. Thus, . Therefore, ; that is, . Following Equation (2), it can be concluded that is the unique random fixed point of . However, for , we have the following:
In other words, is also the random fixed point of . Therefore, .
We now show that . When n is large enough, this can be expressed as , where and :
This demonstrates that
Since both n and tend toward infinity, and , we can conclude that
□
The following remark will help in proving the next theorem.
Remark 1.
- (1)
- if and ;
- (2)
- if Int();
- (3)
- if for all such that . Then, it holds that and .
Theorem 4.
For a complete, separable RCMS , consider to be a continuous random operator. There exists such that for every and some , the inequality
holds for each . If forms a bounded random orbit around a random point , then f possesses a unique random fixed point .
Proof.
A randomly selected point, denoted by , moves within a bounded orbit represented by the set Q. Specifically, the orbit defined by has a finite diameter . Therefore, it yields
Furthermore, following the proof of Theorem 3, consider the sequence
Notice that , and for , we obtain
The sequence fulfills
Considering that serves as an upper bound for the sequence , we observe that the sequence of vectors converges to zero (in the norm of the space E) as n tends toward infinity. Hence, leveraging the remarks given above yields . Thus, forms a Cauchy sequence, implying that it converges to a particular point .
Next, our objective is to prove the equality . For every , there exists a mapping , as defined by Equation (3).
Let with be given. We select a natural number such that
holds for every .
In line with Remark, we have
This implies that
Moreover, we have the inequality
Consequently, there is such that for any , we have
Consequently, there is such that for every , it holds that
By applying Remark 1, we have . The conclusion of the proof can be derived from Theorem 3. □
Theorem 5.
Let be a complete, separable RCMS. Let M be a nonempty, separable, and closed subset of Q, and let f be a continuous random operator defined on M (for ). Suppose that for each , there exist and as well as
satisfying
Then, there is a random fixed point f denoted by . Moreover, for each
Proof.
Assume that is arbitrary. Let , and inductively,
We prove that the sequence converges. With a routine calculation we have
Thus, it can be deduced from Lemma 1 that
Thus, for ,
Therefore, the sequence forms a Cauchy sequence. Let
We are going to prove that . Suppose, to the contrary, that
In such a case, there exist two disjoint closed neighborhoods U and V such that , , and there exists a positive value defined as the infimum of
Since f is continuous, we conclude that and for all sufficiently large n values. However, we have
This contradicts Equation (6). Therefore, , showing the uniqueness of the random fixed point relying on the provided hypothesis.
To demonstrate that , we set
If n is large enough, then it can be expressed as , where and . Therefore, we have
Observe that implies , and thus as n tends toward infinity. This completes the proof. □
4. Example
In this section, we give an example to show that our results are distinct from existing ones. It also shows that the random fixed-point problem cannot be solved in metric spaces, as in the random cone metric setting.
Example 1.
Suppose , and let Σ denote the sigma algebra of Lebesgue measurable subsets of . Suppose we have and a cone equipped with the random cone metric
for all and , which is continuous and has a positive value on . The random mapping is defined. The set can be represented as the union of intervals for natural numbers n, along with the singleton set . A random mapping f is defined for each natural number n as follows:
This mapping f operates within the specified intervals for each n, ensuring that the output falls within the range , defined by
with
To check if f is non-decreasing, let with
Now, , ; that is, . Observe that f is constant on the segment .
On the other hand, when taking with , we have
which means that f is non-decreasing.
Now, we show that f is sequentially continuous (and we know that in this case, every sequentially continuous mapping is continuous). Let Then, we have
Therefore, we obtain
This means f is sequentially continuous and hence continuous.
The above explanation shows that f is a non-decreasing and continuous function defined on the interval .
We will now demonstrate that the given mapping does not meet the existing contraction conditions, but it does satisfy our proposed random Sehgal contraction.
Assume that we select . Then, , and for , we have . From here, we obtain
and thus
It is evident that holds for , which implies that
Therefore, we conclude that f is not a random contraction.
Now, we will check the random Sehgal contraction for the given mapping.
Clearly, and which gives
Consequently, it is evident that
Hence, satisfies the random Sehgal contraction condition. Consequently, we can assert that f is a random Sehgal contraction, and it possesses a distinct random fixed point in the RCMS, which is zero.
5. Applications of Transverse Oscillations in a Random Homogeneous Bar
In this section, our aim is to utilize the established results to explore the existence of a solution to the random BVP governing the transverse oscillations of a random homogeneous bar. This investigation stems from the outcomes we previously established.
Let be the collection of continuous functions from to . A random cone metric can be defined as follows:
Consequently, the pair constitutes a complete RCMS.
The transverse vibrations of a uniform bar have significant practical importance, which positioned along segment of the x axis. At any given moment, we can characterize the ordinary differential equation (ODE) governing the lateral vibrations of the homogeneous bar by examining the displacement:
where is a continuous random function defined on and is a constant. Through standard calculus techniques, it can be shown that the previously mentioned problem can be expressed as the following Fredholm integral equation:
where the Green function is defined as
Next, consider the following conditions and concerning the random mapping , which is defined as
Suppose a constant for which
where
For each and , the following holds:
Theorem 6.
Proof.
By utilizing a random mapping f with conditions and , we can obtain the following equation:
which implies the following:
Equivalently, we have
for .
Therefore, all of the conditions stated in Theorem 5 are valid for . Consequently, a unique solution exists for Equation (1) within the set Q. □
6. Application of a Random Solution for the Nonlinear FDE
We present here a random solution for the nonlinear Caputo-type FDE. Aydi [35] examined Ri’s discoveries, particularly focusing on investigating a nonlinear FDE of the Caputo type. Baleanu [36,37] concentrated on proving the existence of solutions for FDEs.
The Caputo derivative in the random sense is given as follows:
where the value of falls between and n, with .
Here, we explore a nonlinear, random integrodifferential equation of the random Caputo type
subject to the specified conditions, , and
Here, H represents a random mapping which is continuous, and satisfies the conditions and (for more details, see Baleanu et al. [36]). Solving Equation (13) leads to a random fixed point of the subsequent random integral equation:
Theorem 7.
Proof.
Consider , and denote Q as the set of all continuous functions from to , with the random metric defined as follows:
Subsequently, the combination of constitutes a complete CRMS. Furthermore, we demonstrate that f fulfills all of the conditions of Theorem 5 for . Within this framework, we contemplate and as elements of Q, while ranges over the interval . The outcome is presented as follows:
Consequently, we have
Similarly, we obtain
Therefore, we have
This gives rise to the following:
In conclusion, we have
For and belonging to Q and , all conditions are satisfied, affirming the validity of the criteria delineated in Theorem 5, particularly for . Consequently, the existence of a unique random fixed point is ensured for f, implying the presence of a unique random solution for the nonlinear Caputo-type FDE in Equation (13). □
7. Application of a Random Solution for the Integral Equation
Theorem 8.
Consider the random integral equation
Suppose that the following are true:
- (1)
- and ;
- (2)
- is increasing for every ;
- (3)
- There exists a continuous function such thatfor every
- (4)
- .
Then, the random integral in Equation (18) has a unique random solution in .
Proof.
Let , and define
for every . Define a transformation on Q as
For every , we have
Hence, for every . The conclusion follows now from Theorem 5. □
8. Conclusions
This study established random Sehgal contractions to be applied to randomly generated operators within a complete RCMS as a superior approach compared with traditional Sehgal and Banach contractions in metric spaces. The strength of the current work on random Sehgal contractions in an RCMS is its ability to handle uncertainty and randomness more effectively than traditional Sehgal and Banach contractions in metric spaces. The tailored framework of random Sehgal contractions offers accelerated convergence properties, which are particularly evident in solving nonlinear random FDEs and random BVPs. This innovative methodology outshines the existing literature by providing unique solutions and faster convergence rates, solidifying its position as a leading and powerful technique in modern fixed-point theory within stochastic environments. Our theoretical insights are reinforced by a detailed example, offering empirical validation for our main results.
Author Contributions
Conceptualization, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; methodology, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; software, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; validation, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; formal analysis, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; investigation, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; resources, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; data curation, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; writing—original draft preparation, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; writing—review and editing, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; visualization, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; supervision, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; project administration, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P.; funding acquisition, M.N., F.U.D., U.I., L.D.R.K., and I.-L.P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
Data will be available on request from the corresponding authors.
Conflicts of Interest
The authors declare no conflicts of interest.
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