Abstract
A wide range of new research articles in artificial intelligence, logic programming, and other applied sciences are based on fixed-point theorems. The aim of this article is to present an approximation method for finding the fixed point of generalized Suzuki nonexpansive mappings on hyperbolic spaces. Strong and -convergence theorems are proved using the Noor iterative process for generalized Suzuki nonexpansive mappings (GSNM) on uniform convex hyperbolic spaces. Due to the richness of uniform convex hyperbolic spaces, the results of this paper can be used as an extension and generalization of many famous results in Banach spaces together with spaces.
MSC:
46S40; 54H25; 47H09; 47H10
1. Introduction and Motivation
Metric fixed-point theory has emerged as a powerful tool to represent the virtual space as a digital environment [1] and explore web topology [2]. New research on fixed-point theory also emphasizes the significance of solving real-world issues. Functional equations and iterative procedures are applicable to the solution of routing problems in artificial intelligence. The capacitated vehicle routing problem [3] describes a technique for selecting the optimum strategy to distribute comprehensible things from a pickup location using a huge number of carriages with a convinced adaptive volume. It certainly meets the claim of a network of clients spread across the globe. Conversely, communication engineering utilizes fixed-point theory as a tool for problem-solving. The resolution of chemical equations, genetics, algorithm testing, and control theory are additional real-world applications. These findings present pleasant opportunities for approximating the solutions of differential and integral equations that are both linear and nonlinear in nature [4,5]. The theory of fixed points has become a potent and essential tool for the study of nonlinear problems [6,7,8,9] due to its novel emergence as a confluence of analysis [10,11,12,13] and geometry [14,15,16,17]. More specifically, the fixed-point approximation for SKC mappings in hyperbolic spaces has remained a focal point of recent and past research [18]. In this research, we will prove strong and -convergence theorems by using Noor iterative process for generalized Suzuki nonexpansive mappings (GSNM) on uniform convex hyperbolic spaces. To achieve this purpose, we intend to start with the basic definitions and preliminaries in the next section.
2. Basic Definitions and Preliminaries
A family of single-valued mappings introduced by Suzuki [19] is defined as
Definition 1.
Let us consider a Banach space and a mapping on the subset of satisfying the following condition:
∀.
This mapping works as an intermediate class of mapping between nonexpansiveness and quasi-nonexpansiveness as given below:
Definition 2.
Assume , where is nonempty and is a Banach Space. Then is nonexpansive if ∀.
Definition 3.
Assume , where is nonempty and is a Banach Space. Then is quasi-nonexpansive if for every and ∀. Here represents fixed point set of
Example 1.
Let on [0, 5] be defined by;
Then clearly is not nonexpansive but it satisfies condition (1).
Example 2.
Let on [0, 5] be defined by
Then fails to fullfill condition (1), however is quasi-nonexpansive and .
Suzuki [19] conducted significant work in showing the presence of the fixed point and convergence theorem in Banach spaces equipped with mapping a satisfying condition (1).
In [20] Dhompongsa et al. enhanced the conclusions of Suzuki [19] with different conditions on Banach spaces and obtained a fixed point result in these spaces equipped with mapping satisfying condition C.
Nanjaras et al. [21] rendered sundry characterization of existing fixed point results equipped with mappings satisfying condition C in the skeleton of CAT(0) spaces. Abbas et al. [22] also analyzed such spaces whereas the asymptotic regularity is discussed in [23]. Other related work can be found in [24,25,26]. There is need to generalize the result of Suzuki-type nonexpansive mappings which were efficiently conducted by Karapınar et al. [20] in 2011 as given below.
Definition 4.
Assume , where is nonempty and represents metric space, equipped with mapping and if
where ∀. Then is considered to be a Suzuki–Ciric mapping (SCC) [27].
Definition 5.
Assume , where is nonempty and represents metric space, equipped with mapping and if
where ∀.
Then is considered to be a Suzuki–KC mapping (SKC).
Definition 6.
Assume , where is nonempty and represents metric space, equipped with mapping and if
∀.
Then is considered to be a Kannan–Suzuki mapping (KSC).
Definition 7.
Assume , where is nonempty and represents metric space, equipped with mapping and if
∀.
Then is considered to be a Chatterjea–Suzuki mapping (CSC).
Clearly every nonexpansive mapping is SKC, but the converse may not true [27].
Example 3.
Set on [0, 6] by:
Clearly is not nonexpansive but fullfill both the SCC and SKC conditions.
Example 4.
Set R on [0, 6] by:
Clearly R does not fulfill the SKC condition, moreover R is quasi-nonexpansive and .
Example 5.
Let the space with metric:
Set on by:
Clearly fullfill SKC’s condition. Assume that and , then
and
thus
Clearly SKC condition is fulfilled by other points in .
Moreover , and is convex and closed.
This is significant to understand the different iterative process [28] adapted by several writers [29,30] in locating fixed points of the space equipped with nonlinear mappings, moreover solution of their operator equations.
The iteration process manufacture by Mann (see [31,32]) is explained below:
Assume , where is convex and is Banach Space, and let be a nonlinear mapping, for every point , the sequence in is defined by
called Mann iterative process.
It should be noted that represents a real sequence in [0, 1] which fulfills the conditions given below:
- :
- :
- :
One can replace by in other applications.
The Ishikawa manufacture iteration process improves the Mann iteration process (see [33,34,35]) as follows:
Setting and as in (M), for every point , the sequence in is defined by:
called the Ishikawa iterative process, where and are sequences in [0, 1] which satisfy the following conditions:
- :
- :
- :
Some authors switch condition with the general condition
, and notice that, with this switching, the iterative process defined by Ishikawa (I) is a spontaneous generalization of the iterative process given by Mann (M). It is perceived that, if the iterative process defined by Mann (M) is convergent, then the iterative process defined by Ishikawa (I) through condition is also convergent, with appropriate conditions on and .
Recently, Agarwal et al. [36] broached the S-iteration process which is independent of the above two iterative processes as follows:
For , where is convex and is linear space, and let be a mapping, for every point , the iterative sequence in is defined by the S-iteration process is given below:
where and are sequences in (0, 1) filling the condition:
It is perceived that both the S-iteration process and the Picard has the same rate of convergence, which is more rapid than the iteration process defined by Mann which is equipped with contraction mapping (see [31,36,37]).
We use the definition of a hyperbolic space given in [38,39,40], because the definition given by Reich and Shafrir [41] is a bit more repressive. The hyperbolic spaces in the Reich and Shafrir sense [41] is unbounded by taking family of metric lines M instead of metric segments. Further related research can be seen in [42,43]. Moreover, every subset of hyperbolic space is hyperbolic itself by definition, which we consider, and it gives convergence results too.
Definition 8.
Consider the metric space equipped with convex mapping then the triplet is said to be hyperbolic space if it fulfills the conditions given below:
∀ and and .
Takahashi established the convex metric space [44], in which the triplet fulfills . Goebel and Kirk in [45] gave their own definition of above space, where triplet fill conditions –.
Reich and Shafrir [41] and Kirk [46] manufactured their definition of hyperbolic space by using ’condition III’ of Itoh [47] which is equivalent to .
The class of hyperbolic spaces is rich in nature and contains different spaces, manifold of the Hadamard type and convex subsets thereof. For more see [48], and the CAT(0) spaces along with Ω as the unique geodesic path between any two points in Bruhat and Tits [49] show that hyperbolic space is a -space if and only if it fulfills the so called CN-inequality.
Wataru Takahashi [44] introduce the notion of a convex set of hyperbolic spaces if it satisfies the following condition ∀ and . We often use the notion for ,∀ and .
Assume ∀, and , and setting
and
which is considered to be a more general setting of a convex metric space [44,50].
A hyperbolic space is uniformly convex in the sense of [37] if, for any and , there exists such that, ∀,
provided , and
Setting equipped with such that and then η is said to be modulus of uniform convexity. Clearly with this setting if q decreases for stationary ε then η is monotone.
The aim of this article is to prove strong convergence and - of Noor iterative process for GSNM in uniform convex hyperbolic spaces. First, we recall the notion of -convergence and a few of its primary characteristics.
Assume , where is nonempty and represents metric space and let be any sequence in . Moreover, diam signify the diameter of . Set a continuous functional as
The asymptotic radius of is signified by in connection with and is defined to be the infimum of over
Furthermore, if
then the point signifies as an asymptotic center of the sequence in connection with .
AC signifies the set of all asymptotic centers of in connection with , which is the set of minimizers of the functional and it can be empty or a singleton or contain infinite points.
The notions and AC = AC respectively, signify the asymptotic radius and asymptotic center taken in connection with
Clearly, for if and only if
Moreover, every sequence which is bounded has a unique asymptotic center in connection with each closed convex subset in uniformly convex Banach spaces and even CAT(0) spaces.
The following lemma is due to Leuştean [51] and we know that this property also holds in a complete uniformly convex hyperbolic space.
Lemma 1
([51]). Assume , where is nonempty. Moreover, is also closed and convex. Furthermore, the triplet represents uniformly convex hyperbolic space, which is complete and has η as a monotone modulus of uniform convexity. Then every sequence in which is bounded, has a unique asymptotic center referring to as defined above.
Definition 9.
Let be hyperbolic space and in any sequence in . If u is the unique asymptotic center of every subsequence of then is considered to be Δ- to . In such a case, we set Δ- and we refer u the Δ- of .
Lemma 2
([42]). The triplet represents uniformly convex hyperbolic space having η as a monotone modulus of uniform convexity. Moreover, assume and be a sequence in with . If and are any two sequences in so that
- ,
- ,
- ,
- for some , then .
3. Main Results
First, we will give the definition of monotone sequences.
Definition 10.
Assume , where is nonempty and is a hyperbolic space. Moreover, suppose that be a sequence in Then the sequence is said to be monotone in connection with if ∀ and ,
Proposition 1
([40]). Assume , where is nonempty and is a hyperbolic space. Moreover, suppose that be a monotone sequence in connection with Then the following conditions hold:
- (1)
- is bounded;
- (2)
- the sequence is decreasing and convergent ∀
We are now able to present the iterative process defined by Noor in hyperbolic spaces (see [40]):
Assume , where is nonempty, moreover is closed and convex, and is hyperbolic space. Furthermore, is a mapping. For any the sequence of the Noor iteration process is defined by:
where and are real sequences such that and .
We are able to manufacture the proof of the following lemma from the definition of SKC mapping.
Lemma 3.
Assume , where is nonempty and is a hyperbolic space. Moreover, suppose that be an SKC mapping. If is a sequence given by , then is monotone sequence in connection with .
Proof.
Let . Then by , we have
∀, which completes the proof. □
Lemma 4.
Assume , where is nonempty, closed, and convex. Furthermore, the triplet represents uniformly convex hyperbolic space, which is complete, having η as a monotone modulus of uniform convexity, and let be an SKC mapping. If the sequence is defined by , then is nonempty if and only if is bounded and .
Proof.
Suppose is nonempty and . Then, the sequence is monotone with respect to by using by Lemma 3. Furthermore, is bounded and by using Proposition 1.
Set . If , then clearly we have
Applying the limit supremum, we have
Set . Moreover, is an SKC mapping, then
and
Therefore,
for every . Applying the limit supremum, we get
for . Further, we have
Applying the limit supremum, we get
Since
we have
Thus,
for . Consequently it occurs from the Lemma 3 that
Next,
Hence, we have
Notice that
which produces
From the above inequalities, we get
Thus, we get
which implies
Conversely, assume that the sequence is bounded and .
Set be a singleton. Then . Further is an
which implies that
By utilizing the uniqueness of the asymptotic center, , so u is a fixed point of Consequently, is nonempty. □
Now, we are able to prove the - theorem.
Theorem 1.
Assume , where is nonempty, closed, and convex. Furthermore, the triplet represents uniformly convex hyperbolic space, which is complete and has η as monotone modulus of uniform convexity, and let be a mapping such that . If the sequence is defined by (2), then the sequence is Δ- to a fixed point of
Proof.
Suppose is an SKC mapping. We observe that be a bounded sequence. Therefore, has a - subsequence. We have to show that every - subsequence of has a unique - in . To prove this claim, suppose s and t be - of the subsequences and of , respectively. Since and by using Lemma 1. Now by Lemma 3, is a bounded sequence and .
We have to show that s is a fixed point of
Applying the limit supremum, we get
Hence, we have
By uniqueness of the asymptotic center,
By using same argument, we can show that . Consequently, s and t are fixed points of Now, we show that . Suppose on contrary that , moreover by the uniqueness of the asymptotic center,
which is a contradiction. Therefore s = t. □
Now, we will introduce the strong convergence theorems in hyperbolic spaces.
Theorem 2.
Assume , where is nonempty, closed, and convex. Furthermore, the triplet represents uniformly convex hyperbolic space, which is complete and has η as the monotone modulus of uniform convexity and let be an SKC mapping. If the sequence is defined by (2), then the sequence converges strongly to some fixed point of if and only if
where .
Proof.
Clearly the necessary condition is trivial. The proof completes only by showing the sufficient condition. So we show that is closed. Assume that is SKC mapping, moreover let be any sequence in which converges to some point .
Applying the limit, we get
Since the limit is unique, we get , which shows that is closed.
Assume that
Moreover, we obtain
Thus, exists by applying Lemma 3 and using Proposition 1. Consequently, we know that
Consequently, we can set a subsequence of so that
for every where .
Applying Lemma 3, we get
from which we can deduce that
Thus, is a Cauchy sequence. Whereas is closed. Then is a convergent sequence.
Suppose . Then, the proof completes by showing that converges to q. In fact,
as .
We have
Since exists, the sequence converges to q. □
Next, we will give one more strong convergence theorem by usingTheorem 2. We call up the definition of condition (I) broached by Senter and Doston [52].
Assume be a metric space and which is nonempty, be equipped with a mapping . Then is claimed to fulfill condition (I), if ∃ a nondecreasing function with ∀ so that
∀, where
Theorem 3.
Assume , where is nonempty, closed, and convex. Furthermore, the triplet represents uniformly convex hyperbolic space, which is complete and has η as monotone modulus of uniform convexity, and let be an SKC mapping with condition (I) and . Then, the sequence , which is defined by (2) converges strongly to some fixed point of
Proof.
From Theorem 2, and applying Lemma 4, we have
The condition (I) gives us
for ,which is nondecreasing with , for t, such that
Consequently, we get
Whereas f is a nondecreasing mapping filling for every t, such that , we get
Which completes the proof from Theorem. □
Example 6.
Consider the real line R with usual metric ρ defined as , moreover suppose . Set
for every
Then is a complete uniformly convex hyperbolic space with a monotone modulus of uniform convexity and clearly , which is nonempty closed and convex. Set a mapping as defined in Example 2.
Clearly fulfills the SKC condition with as a fixed point of Moreover, it is noticed that it fulfills all conditions in Theorem 2. Suppose and be constant sequences such that for every . We encounter the following cases for
Case 1: Set ; for the sake of simplicity, we suppose that . Moreover, by the iterative process defined in (Definition 10) and the definition of Ω, we get
and
and
Case 2: Set u = 4, for the sake of simplicity, we suppose that . Moreover, by the iterative process manufactured in (Definition 10) and the definition of Ω, we get
and
and
where ; and
Consequently, by simple calculations, it can be seen that the sequence converges to .
4. Discussion
Fixed-point theorems are the foundation of numerous recent research publications in applied sciences, logic programming, and artificial intelligence. This article’s aim is to provide an approximate technique for determining the fixed point of generalized Suzuki nonexpansive mappings on hyperbolic spaces. For generalized Suzuki nonexpansive mappings (GSNM) on uniform convex hyperbolic spaces, the theorems of strong and -convergence are demonstrated using the Noor iterative method. The findings of this study can be used as an extension and generalization of numerous well-known conclusions in Banach spaces along with CAT(0) spaces due to the richness of uniform convex hyperbolic spaces.
5. Conclusions
Fixed-point theory is a tool for problem-solving in communication engineering. Additional real-world applications include genetics, testing of algorithms, control theory, and the solving of chemical equations. These results offer interesting possibilities for approximate solutions of linear and nonlinear differential and integral equations. We conclude our results with some open questions and future directions:
- Whether the condition of boundedness of sequence in Lemma 4 can be relaxed?
- Whether in Lemma 4 a convergent sequence is not enough to prove it?
- In Theorem 1, is it possible that the conditions on will be removed or replaced with less strong conditions?
- What about the proof of Theorem 2 if is KSC mapping instead of SKC mapping?
- What about the proof of Theorem 3 if is CSC mapping instead of SKC mapping?
Author Contributions
Conceptualization, A.T. and F.L.; methodology, R.S.; software, S.K.; validation, A.T., F.L. and S.K.; formal analysis, A.T.; investigation, A.T.; resources, F.L; data curation, S.K.; writing—original draft preparation, A.T.; writing—review and editing, S.K; visualization, F.L.; supervision, R.S.; project administration, A.T. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The research is theoretical in nature. As a result, no data were used.
Acknowledgments
Asifa Tassaddiq would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. R-2023-128. The authors are also thankful to the worthy reviewers and editors for their useful and valuable suggestions for the improvement of this paper which led to a better presentation.
Conflicts of Interest
The authors declare no conflict of interest.
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