Abstract
Best proximity point theorem furnishes sufficient conditions for the existence and computation of an approximate solution that is optimal in the sense that the error assumes the global minimum value . The aim of this paper is to define the notion of Suzuki --proximal multivalued contraction and prove the existence of best proximity points satisfying where is assumed to be continuous or the space is regular. We derive some best proximity results on a metric space with graphs and ordered metric spaces as consequences. We also provide a non trivial example to support our main results. As applications of our main results, we discuss some variational inequality problems and dynamical programming problems.
Keywords:
nonlinear dynamical systems; best proximity point; α-proximal contraction; multi-valued mappings; graphs MSC:
46S40; 47H10; 54H25
1. Introduction and Preliminaries
In 1969, Fan [1] initiated and obtained a classical best approximation result, that is, if is a nonempty compact convex subset of a Hausdorff locally convex topological vector space and is a continuous mapping, then there exists in such that . In 2010, Basha [2] introduced the notion of best proximity point of a non-self mapping. Additionally he gave a generalization of the Banach fixed point theorems by a best proximity theorem. Let and be nonempty subsets of a metric space . A point is called a best proximity point of mapping if where Sankar Raj [3] and Zhang et al. [4] defined the notion of P-property and weak P-property respectively. Jleli et al. [5] defined the concept of -proximal admissible for non self mapping Hussain et al. [6] utilized the concept of -proximal admissible and introduced Suzuki type - proximal contraction to generalize several best proximity results. Chen et al. [7] defined -admissible Meir-Keeler-type set contractions which have KKM property on almost convex sets and established some generalized fixed point theorems. In 2014, Ali et al. [8] gave the conception of -proximal admissible for multivalued mapping and obtained some best proximity point theorems for multivalued mappings. The goal of this article is to define Suzuki --proximal multivalued contraction and establish some generalized best proximity point results.
2. Preliminaries
In this section, we give some preliminaries.
Definition 1.
Let θ and ϑ be nonempty subsets of a metric space , and define and by
- (Sankar Raj [3]) The pair is said to satisfy the P-property if and the following condition is satisfied:
- (Zhang et al. [4]) The pair is said to have the weak P-property if and the following condition is satisfied:
Jleli et al. [5] defined the concept of -proximal admissible for non self mapping as follows:
Definition 2.
Let θ and ϑ be be two nonempty subsets of a metric space . A mapping is called α-proximal admissible if there exists a mapping such that
where
Later on, Ali et al. [8] extended the notion of -proximal admissible for multivalued mapping in this way.
Definition 3
([8]). Let θ and ϑ be two nonempty subsets of a metric space . A mapping is called α-proximal admissible if there exists a mapping such that
where and .
For more details in the direction of best proximity, we refere the following [9,10,11].
On the other hand, Jleli et al. [12] introduced a new type of contraction called -contraction and established some new fixed point theorems for such a contraction in the context of generalized metric spaces.
Definition 4.
Let be a mapping such that:
- ()
- is nondecreasing;
- ()
- ∀,
- ()
- ∃ and such that
A mapping is called a -contraction if there exist some mapping satisfying ()-() and a constant such that
∀. Following Jleli et al. [12], the set of all continuous functions satisfying conditions is represented by . For more details in the direction of -contractions, we refer the readers to [13,14,15].
Hancer et al. [16] altered the above definitions by summing a broad condition () which is supplied as follows:
- ()
- for all with
Following Hancer et al. [16], we represent the set of all continuous functions satisfying conditions by .
Motivated by [6,8,12], we introduce the notion of Suzuki --proximal multivalued contraction by using the concept of -proximal admissibilty for multivalued mappings and -contraction to prove some new results. Our results extend some best proximity results of literature.
3. Results and Discussions
Throughout this paper, is a complete metric space and , and denote the families of all nonempty closed subsets, nonempty closed and bounded subsets and compact subsets of respectively. For any , let the mapping ·,·) be the generalized Hausdorff metric with respect to defined by
Definition 5.
Let be a metric space and be a non-empty subsets of A multi-valued mapping is said to be Suzuki α-Θ-proximal multivalued contraction if there exist functions , and some contant such that
∀, where satisfying .
Note that, if C be a compact subset of a metric space and , then there exists such that
Theorem 1.
Let θ, ϑ such that and be Suzuki α-Θ-proximal multivalued contraction and α-proximal admissible. Suppose that
(i) ∀, we have and the pair satisfies the weak P-Property;
(ii) ∃ and such that
(iii) is continuous.
Then has a best proximity point.
Proof.
By supposition (ii), ∃ and such that
If , then we obtain
and so is the required point.
Now, let . Since , we have
From we obtain
and so
By Definition, we have
This pursues that By (1), we have
Otherwise, as , so by (, we have
Since is compact, there exists such that and so
By supposition (i), we get and so ∃ such that
Since is an -proximal admissible, so it follows from (2) and (5) that
Since satisfies the weak P-Property, so by (i) we get
If , then we obtain as the required point. Suppose that . From (4), (7) and (), it follows that
If , then is the required point. Now, assume that . Since , we have
From we obtain
and so
By Definition, we have
It pursues that From (1),we have
Otherwise, as , so by (), we have
Since is compact, so ∃ such that and so
By supposition (i), we have and so ∃ such that
Since is an -proximal admissible, so it follows from (5) and (11) that
Since satisfies the weak P-Property, so by supposition (i), we have
If , then is the required best proximity point of . Assume that . From (10), (12) and (), it follows that
Hence, by induction, we have and such that
- (a)
- and ;
- (b)
- and ;
- (c)
- andfor all Which further implies thatFrom (16), we obtainThen from (), we getBy (), ∃ and such thatAssume that In this instanse, let By definition of the limit, ∃ such that∀ It implies thatThen∀ where Presently we assume that Let . By the definition of the limit, ∃ such that∀ This implies thatwhere Thus, in all cases, there exist and such thatHence by (16), we getTaking , we getThus, there exists such thatfor all Now we prove that is a Cauchy sequence in . For we have,Since, , then converges. Therefore, as Hence is Cauchy in . By (15) and (), we haveThen, likewise, we can prove that is a Cauchy sequence in . As , , so ∃ and such that and as , respectively. As for all , we conclude thatSince is continuous, we have . On the other hand, since , we haveLetting , we obtainwhich leads to . Furthermore, one hasTherefore, is the required best proximity point of . □
If is replaced with in Theorem 1, then we get this result.
Theorem 2.
Let θ, ϑ such that and be Suzuki α-Θ-proximal multivalued contraction and α-proximal admissible. Assume that
(i) ∀, we have and satisfies the weak P-Property;
(ii) ∃ and such that
(iii) is continuous
(iv) () holds.
Then has a best proximity point.
Proof.
By supposition (ii), ∃ and such that
Next, suppose that . Since , we have
From we obtain
and so
By Definition, we have
This pursues that By (1), we have
Otherwise, as , so by ( we have
From (), we can write
Hence there exists such that
Doing the same as we have done in Theorem 1, we get and such that
- (a)
- and ;
- (b)
- and ;
- (c)
- andFurthermore, we obtain in and in as Cauchy sequences. As , , so ∃ and such that and as , respectively. By the proof of Theorem 1, we can get as best proximity point of . □
The next result can given by replacing the continuity of the mapping with the property H.
- (H)
- If is a sequence in with ∀ and as , then ∃ of such that for all .
Theorem 3.
Let θ, ϑ such that and be Suzuki α-Θ-proximal multivalued contraction and α-proximal admissible. Assume that
(i) for each , we have and the pair satisfies the weak P-Property;
(ii) there exists and such that
(iii) Property holds.
Then has a best proximity point.
Proof.
By Theorem 1, we get and such that
- (a)
- and ;
- (b)
- and ;
- (c)
- andfor all Also, there exist and such that and as , respectively, and . We prove that is a best proximity point of . If of such that ∀, then we havewhich yields that,for all . Letting , we obtainHence is a best proximity point of . Without any loss, we assume that ∀. By (H), ∃ of such that ∀. By supposition (ii), we get such thatSince we obtainand soBy Definition, we haveForm (1), we haveFrom (), we obtainOn the other hand, we haveSince we obtainLetting , we obtain Hence, we haveMoreover, since and , we haveTherefore, is a best proximity point of □
Theorem 4.
Let θ, ϑ such that and be Suzuki α-Θ-proximal multivalued contraction and α-proximal admissible. Suppose that
(i) for each , we have and the pair satisfies the weak P-Property;
(ii) there exists and such that
(iii) Property holds.
(iv) ( holds.
Then has a best proximity point.
Proof.
The proof of this Theorem can easily be done like Theorem 3 and so we omit the proof here. □
The following result if a direct consequence of Theorem 1 for non self mapping.
Corollary 1.
Let θ, ϑ such that and be a non self mapping such that
Suppose that the following conditions hold:
(i) is α-proximal admissible,
(ii) and the pair satisfies the weak P-Property;
(iii) there exists such that
(iv) is continuous or Property holds.
Then has a best proximity point.
Example 1.
Let be endowed with the usual metric σ, and . Define by
and a function α as follows:
Take by for and Note that , and for all . Also satisfies the weak P-property. Let Then we have
Consider and such that and . Then we have Hence implies that is an α-proximal admissible. For and , we have such that and Further, we have
Since , we obtain
and so
By definition, we have
Since we have
Since for all Assume that and and consider
Then, we have
and so
Similarly, we have
This yields that
Since Θ is increasing, we have
with Hence (1) is satisfied. Also, is continuous and supposition (ii) of Theorem 4 is verified. Indeed, for , and , we obtain
and Thus all the supposition of Theorem 4 are satisfied and is the required proximity point.
4. Consequences
4.1. Fixed Point Results in Complete Metric Space
In this section, we deduce some fixed point results for multi-valued and single-valued mappings from our main results.
Theorem 5.
Let () be such that
Suppose that the following conditions hold:
(i) there exist and such that ,
(ii) is α-admissible,
(iii) is continuous or Property holds ( holds).
Then has a fixed point.
Proof.
By supposition (i), and such that If then is fixed point of . Now
If then is the requited fixed point and we have nothing to prove. So we suppose that By assumption, we have
Since is compact, there exists such that Thus we have
Since and is -admissible, so we have Thus we have Now
If then is the requited fixed point and we have nothing to prove. So we suppose that By assumption, we have
Since is compact, there exists such that Thus we have
Thus by induction, we have and
for all Now is easy to satisfy all the assertions of Theorems 1 and 3 and thus we get the conclusion. □
Corollary 2.
Let be such that
Suppose that the following conditions hold:
(i) there exist and such that ,
(ii) is α-admissible,
(iii) is continuous or Property holds.
Then has a fixed point.
4.2. Some Results in Partially Ordered Metric Spaces
In this section, we derive following new results in partially ordered metric spaces from our main results.
Definition 6
([17]). Let θ and ϑ be tow non empty subsets of a partially ordered metric space . A mapping is said to be proximal nondreasing if
where and .
The property () will be need in the next result.
- (H)
- If is a sequence in such that ∀ and as , then ∃ of such that , .
Theorem 6.
Let θ, ϑ such that and , , and some contant such that
for all with , where satisfying Suppose that
(i) for each , we have and the pair satisfies the weak P-Property;
(ii) there exists and such that
(iii) is proximal nondecreasing;
(iv) is continuous or Property () holds.
Then has a best proximity point.
Proof.
Consider such that
By supposition (ii), ∃ and such that
Since we obtain . Next, suppose that . Since , we have
Since we obtain
and so
By Definition, we have
It pursues that and
Otherwise, as , so by ( we have
From (), we can write Hence there exists such that
From the proof of Theorem 2, we get and such that
- (a)
- and ;
- (b)
- and ;
- (c)
- and∀ Hence by the proof of Theorem 2, we get the conclusion when is continuous. Similary by the proof of Theorem 4 if () is satisfied. □
4.3. Some Results on Graphic Contraction
Let be a metric space and be the direct graph such that and contains all loops, i.e.,
Definition 7
([18]). A mapping is called G-continuous if for and sequence in such that as and for all implies .
Definition 8
([8]). Let θ and ϑ be nonempty subsets of a metric space endowed with a graph G. A mapping is said to be G-proximal if
where and . If , then we say that preserves the edges of G.
Assume the following:
- (H)
- If is a sequence in such that (∀ and as , then ∃ of such that for all .
Theorem 7.
Let θ, ϑ such that and , , and some constant such that
for all with (, where satisfying Suppose that
(i) for each , we have and the pair satisfies the weak P-Property;
(ii) there exists and such that
(iii) is G-proximal
(iv) is G-continuous or Property () holds.
Then has a best proximity point.
Proof.
Taking such that
By supposition (ii), ∃ and such that
Since we obtain . Next, suppose that . Since , we have
Since we obtain
and so
By Definition, we have
It pursues that so from (1), we have
Otherwise, as so from (, we have
Hence there exists such that
Proceeding the same as we have done in Theorem 2, we get and such that
- (a)
- ( and ;
- (b)
- and ;
- (c)
- and∀
Hence from the Theorem 2, we get the deduction when is G-continuous or also if () is satisfied then we get conclusion from the proof of Theorem 4. □
5. Some Applications
In this section we present the applications of our results for variational inequality problems and dynamical programming.
5.1. Application to Variational Inequality Problem
Let C be nonempty, closed and convex subset of real Hilbert space H with inner product and induced norm . Recall that an operator is called monotone if . We consider a monotone variational inequality problem as follows:
Problem 1.
Find such that for all , where is a monotone operator.
The interest for variational inequalities theory is due to the fact that a wide class of equilibrium problems, arising in pure and applied sciences, can be treated in an unified framework [19]. Now, we recall the metric projection, say , which is a powerful tool for solving a variational inequality problem. Referring to classical books on approximation theory in inner product spaces, (see [20]), we recall that for each , there exists a unique nearest point such that
We need the following crucial lemmas.
Lemma 1.
Let . Then if and only if for all .
Lemma 2.
Let be monotone. Then is a solution of for all if and only if , .
Now we prove the results for the solution of Problem 1.
Theorem 8.
Let C be a non-empty, closed and convex subset of a real Hilbert space H. Suppose that , , where is the identity operator on C, and satisfies the following assumption;
- ∃ such that ,
- is α-admissible,
- there exists and such that for all
Then there exists a unique element such that for all .
Proof.
Define by for all , then satisfies all the hypothesis of Corollary 2 and so has a unique fixed point . Hence by Lemma 2, is solution of for all if and only if is a fixed point of . This completes the proof. □
Corollary 3.
Let C be a non-empty, closed and convex subset of a real Hilbert space H. Assume that there exists and such that for , satisfies
where is the identity operator on C. Then there exists a unique element such that for all .
5.2. Application to Nonlinear Dynamical System
Here, we apply our results in order to prove the existence of a solution of the following functional equation:
where and are bounded, , W and D are Banach spaces. These types of equations have their application in computer programming, mathematical optimization and dynamic programming, which allow instruments for answering boundary value problems emanating in physical sciences and engineering.
Let denotes the set of bounded real-valued functions on W. The pair , where
is a Banach space with , a distance associated to the norm.
In order to prove the existence of a solution of Equation (25), we take of the type
∀ and . Clearly, F is well defined, since f and G are bounded.
We establish the following result:
Theorem 9.
Let be an operator defined by (26) and assume that the following conditions are satisfied:
- (A)
- G and β are bounded;
- (B)
- for and
Then, the functional equation 5.2 has a unique and bounded solution.
Proof.
Define and by and for all , . Let be any positive number and . Pick arbitrarily and choose such that
where , for
By definition of F, we have
Now, from (27)–(29), we have
Similarly,
Assume that , then from (30) and (31) with (B), we have
which implies
Which is equivalent to
Since is arbitrary, we get
Thus we have
Thus, all the suppositions of Corollary 2 are satisfied for . Therefore, there exists p, such that , which is the bounded solution of the functional Equation (25). □
6. Conclusions
In the present paper, we defined the notion of Suzuki --proximal multivalued contractions to discuss the existence of best proximity points in the context of complete metric spaces. We derived some best proximity theorems on a metric space with graphs and ordered metric spaces as consequences. We discussed some variational inequality problems and dynamical programming problems as applications of our main results. We also gave a significant example to support our main results. We hope that the results contained in this article will build new connections for those who are working in -proximal contractions (or its generalizations) and its applications to variational inequality problems and dynamical programming problems.
Similar generalizations of such contractions for the fuzzy mappings would be a special topic for future study. Another direction of future work would be to apply our results in the solution of fractional differential inclusions.
Author Contributions
All authors contributed equally and significantly in writing this paper. All authors read and approved the final paper.
Funding
This research received no external funding.
Acknowledgments
This Project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. G: 392-130-1439. The authors, therefore, acknowledge with thanks DSR for technical and financial support.
Conflicts of Interest
The authors declare that they have no competing interests.
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