Abstract
This paper addresses a problem of global optimization in a non-Archimedean fuzzy metric space context without fuzzy P-property. Specifically, it concerns the determination of the fuzzy distance between two subsets of a non-Archimedean fuzzy metric space. Our approach to solving this problem is to find an optimal approximate solution to a fixed point equation. This approach has been well studied within a category of problems called proximity point problems. We explore some new types of -weak proximal contractions and investigate the existence of the unique best proximity point for such kinds of mappings. Subsequently, some fixed point results for corresponding contractions are proved, and some illustrative examples are presented to support the validity of the main results. Moreover, an interesting application in computer science, particularly in the domain of words has been provided. Our work is a fuzzy generalization of the proximity point problem by means of fuzzy fixed point method.
Keywords:
best proximity point; global optimization; (ψ − ϕ)-weak proximal contraction; fuzzy P-property; non-Archimedean fuzzy metric space; domain of words MSC:
47H10; 47H09; 54H25
1. Introduction and Preliminaries
Let be a metric space, and be two nonempty subsets of X. Suppose that is a non-self mapping. An element is called a best approximate point of T if . One of the distinguished best approximation results attributed to Fan [1] assures the existence of a best approximation point of a continuous mapping of a nonempty compact convex subset of a Hausdorff locally convex topological vector space. As a generalization of the concept of a best approximated point, Basha and Veeramani [2] introduced the concept of best proximity point. We recall that an element is called a best proximity point of T if . The best proximity point represents an optimal solution to the fixed point equation , when the non-self mapping T has no fixed point, that is, to find an element such that the distance between and is minimum.
On the opposite hand, fuzzy set theory has been concerned in mathematics as a crucial tool to resolve the various uncertainties problems and ambiguities since Zadeh [3] introduced the idea of fuzzy set. Inspired by the idea of Zadeh, Kramosil and Michalek [4] introduced the concept of fuzzy metric space in 1975. Later on, George and Veermani [5] modified the definition in [4] to generate a Hausdorff topology. Such a topology is metrizable, and each metric will induce a fuzzy metric in Hausdorff topology. Fuzzy metric has some blessings over regular metric because of the flexibility and versatility that the fuzzy notions inherently possess. Fuzzy metrics are powerful tools for modeling various problems with uncertainties in reality. For instance, Gregori and Sapena [6] applied fuzzy metrics to the color image process to filter noisy images and to some other engineering problems of special interest. A fuzzy metric was applied to improve the color image filtering. Some filters were improved when some classical metrics were replaced with fuzzy metrics. For further details, readers are suggested to see [7,8]. Further to this, several fixed point results were established in fuzzy metric spaces. Some remarkable studies in this field are referred to [6,9,10,11,12]. More than that, the non-Archimedean property, an additional assumption, was added to the notion of fuzzy metric spaces to overcome some shortcomings in the study of fixed point theory. The non-Archimedean property weakens the criterion in the notion of fuzzy metric spaces; that is, the same real parameter can relate to the fuzzy distance between any three points of the underlying space. This property is very useful in practice because the main examples of fuzzy metric spaces that are handled in applications satisfy such a constraint. Many authors established several fixed point results which generalized fuzzy Banach contraction in fuzzy settings. For more details, readers are referred to [13,14,15,16].
Attempts to generalize the Banach contraction principle have been around for a long time. Nowadays, it remains an active branch of fixed point theory (for example, see [17,18,19]). Among these works, one such generalization is the concept of the weak contraction principle, which was first introduced by Alber et al. [20] in Hilbert spaces and later adapted to complete metric spaces by Rhoades [21]. A weak contraction mapping is intermediate between a contraction mapping and a non-expansive mapping. Later on, several authors created many results using weak contractions; see [22,23,24,25]. Saha et al. [26] introduced a weak contraction including two control functions in fuzzy metric spaces. In fuzzy metric spaces, control functions are used in similar ways to produce similar outcomes [27]. Compared with those results, in Saha’s work, control functions are supposed to satisfy some other conditions suitable for fuzzy metric spaces. In 2019, Saha et al. [28] investigated the existence of the unique best proximity point for such weak contraction in fuzzy metric spaces by exploring P-property, which provided a way to obtain some proximity points after the unavailability of fixed points and approximate points for non-self mappings, extending and fuzzifying the existing results in metric spaces.
For the sake of completeness, we will recall and present some basic definitions, notations, lemmas and propositions used in the following.
Definition 1
([5,29]). A fuzzy metric space is a 3-tuple , where X is a nonempty set, M is a fuzzy set on such that for all and :
- ;
- if and only if;
- ;
- ;
- is continuous,
where * is a continuous t-norm. We recall that t-norm is a binary operation for which
- * is commutative and associative;
- * is continuous;
- for all;
- whenandwith.
If we replaceby:or then the triple is called a non-Archimedean fuzzy metric space. Note that since implies , each non-Archimedean fuzzy metric space is a fuzzy metric space.
Definition 2
([5]). Let be a fuzzy metric space (or a non-Archimedean fuzzy metric space). Then
- A sequencein X is said to be convergent to x if, for all;
- A sequencein X is said to be a Cauchy sequence if for any, there exists, such that, for alland;
- A fuzzy metric space is said to be complete if and only if every Cauchy sequence in X is convergent.
The following lemmas were proved by Grabiec [11] for fuzzy metric spaces in the sense of Kramosil and Michálek. The conclusions are also valid for the fuzzy metric space given in Definition 1.
Lemma 1
([11]). Let be a fuzzy metric space. Then, is nondecreasing for all .
Lemma 2
([30]). M is a continuous function on .
For two given nonempty subsets A and B of a non-Archimedean fuzzy metric space , the following notions are used throughout this paper:
Remark 1.
Similar to the above notations, there are notions of and that have been used in work [27,31,32]. The difference between them is that here, they are independent from the real parameter t.
Most of the contractive conditions in fixed point theory make use of two metric terms: the distance between two distinct points, , and the distance between their images, , under the self-mapping T. However, for best proximity point theory, since is not a self-mapping, for any x in A, one can not define , that is, for a fixed , it is not possible to define the iterated sequence , for each , in a usual way. To overcome this shortcoming, recently, Sankar Raj [33,34] introduced a new property called P-property for a pair of disjoint nonempty subsets of a metric space. It is an essential geometrical property. The P-property is automatically valid for the pair . It has been proved in [34] that the P-property holds for any pair of nonempty closed and convex subsets in a Hilbert space but not in an arbitrary Banach space. In metric fixed point theory, such a property for a pair of subsets is separately assumed for specific purposes. To solve the best proximity point problem in the setting of fuzzy metric spaces, Saha et al. [35] provided a fuzzy extension of P-property stated as follows.
Definition 3
([35]). Let be a pair of nonempty disjoint subsets of a fuzzy metric space . Then, the pair is said to satisfy the fuzzy P-property if for all and , and jointly imply that .
Definition 4
([27]). Let A and B be two nonempty subsets of X where is a fuzzy metric space. An element is defined as a best proximity point of the mapping if it satisfies the condition that for all
Definition 5
([28]). Let A and B be two subsets of X, where is a complete fuzzy metric space. Let be a mapping that satisfies the following inequality
where and are such that
- ψ is monotone decreasing and continuous with ;
- ϕ is lower semi continuous with .
Then, T is a weak contraction.
It is noted that in the case that , the above definition reduces to that of a weak contraction introduced in [26] which is weaker than a fuzzy Banach contraction but stronger than a fuzzy non-expansive mapping.
Saha et al. in [28] established the existence and uniqueness of best proximity point for the weak contractions mentioned above in the frame of fuzzy metric spaces, as stated in the following theorem.
Theorem 1
([28]). Let be a complete fuzzy metric space. Let A and B be two closed subsets of X and be a non-self weak contraction mapping such that the following conditions are satisfied:
- satisfies the fuzzy P-property;
- ;
- is nonempty.
Then, there exists an elementwhich is a fuzzy best proximity point of T.
In this paper, we aim to enrich the study in the domain of fuzzy global optimization by the use of fuzzy fixed point methods. To this aim, we will provide four types of -weak proximal contractions inspired by Saha’s contraction and investigate the existence and uniqueness of the best proximity points for these types of weak proximal contractions without fuzzy P-property assumption in the frame of non-Archimedean fuzzy metric spaces (in the sense of George and Veermani). As a consequence, some fixed point results for corresponding contractions are proved, and some illustrative examples are presented to support the validity of the main results. Moreover, an interesting application in computer science, particularly in the domain of words, has been provided.
2. Best Proximity Point Results without Fuzzy -Property
Let us begin with some definitions and propositions in the setting of the non-Archimedean fuzzy metric, which will be used to prove our main results.
Definition 6
([36]). A t-norm * is said to be 1-boundary continuous if it is continuous at each point of the type where (that is, if and , then ).
Obviously, each continuous t-norm is 1-boundary continuous. Moreover, a t-norm called the Drastic norm defined by is the one which is not 1-boundary continuous.
Proposition 1
([36]). Let be five sequences and let be a number such that and . Suppose that * is a 1-boundary continuous t-norm and that for all . Then, converges to L.
Proof.
The conclusion can be drawn from the proof of Proposition 5 in [36]. □
Proposition 2.
The limit of a convergent sequence in a non-Archimedean fuzzy metric space (in the sense of George and Veeramani) whose t-norm is only 1-boundary continuous is unique.
Proof.
Let be a convergent sequence with in a non-Archimedean fuzzy metric space whose t-norm * is 1-boundary continuous.
Suppose that is another limit of . From , we have that for all
and
Taking limit as in the above inequalities, we have for all
which shows that for all , that is, . □
In 2011, Basha [37] introduced the concept of approximately compact sets in metric spaces. After that, Saleem et al. [38,39] provided the fuzzy extension of the concept illustrated in the following definition.
Definition 7
([38]). Let A and B be two nonempty subsets of a non-Archimedean fuzzy metric space. A is said to be approximately compact with respect to B if every sequence in A satisfying the condition that for some and all has a convergent subsequence.
It is noted that every set is approximately compact with respect to itself. If A intersects B, then is contained in both and . Furthermore, it can be seen that if A is compact and B is approximately compact with respect to A, then the sets and are nonempty.
Let be the set of all functions satisfying the following conditions:
- is monotone decreasing, that is, for all , ;
- is continuous;
- if and only if .
Here are some functions belonging to :
- , for ;
- , for ;
- , for for .
Let be the set of all functions satisfying the following conditions:
- is lower semi continuous;
- if and only if .
Here are some functions belonging to :
- , for and ;
- , for ;
- , for .
From now and onward, let A and B be nonempty subsets of X such that is nonempty. On the other hand, in the George and Veeramani’s original definition, the t-norm should be continuous. In the following discussion, we will consider the more general case of a George and Veeramani’s fuzzy metric space in which the t-norm is only 1-boundary continuous. Now, we are ready to introduce four new types of -weak contractions in a fuzzy metric space and prove the corresponding best proximity point results in the setting of a non-Archimedean fuzzy metric space.
2.1. -Weak Proximal Contraction of Type I and Its Best Proximity Point Results
Definition 8.
Let be a fuzzy metric space. A non-self mapping is called a -weak proximal contraction of type I, if there exist and such that for all and
.
Theorem 2.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is 1-boundary continuous. Let A and B be two nonempty closed subsets of X such that is nonempty. Let be a non-self mapping satisfying the following conditions:
- ;
- T is a -weak proximal contraction of type I;
- For any sequence and satisfying as , then .
Then, T has a unique best proximity point in .
Proof.
Let be an arbitrary element. Since , we can find such that
Furthermore, since , it follows that there exists such that
Recursively, we obtain a sequence satisfying
If there exists for which for all , it follows that
Hence, for all , so is a best proximity point of T.
Without loss of generality, in the following, we may assume that for all , that is, for all .
Since T is a -weak proximal contraction of type I, we obtain
Since is monotone decreasing, then , so the sequence is an increasing sequence in which is bounded above by 1 for all .
Taking , we shall claim that for all .
Indeed, taking limit as in (2), using the continuity of and the lower semi continuity of , we obtain
which implies that for all .
Now, we show that is a Cauchy sequence.
Suppose on contrary that is not a Cauchy sequence. Then, there exist and such that for all positive integer k, there are with and
Assume that is the least integer exceeding satisfying the above inequality, that is, equivalently,
So, for all , we obtain
Since and * is continuous at 1-boundary, we deduce that
Next, for all , we have
Clearly, , , and .
Thus, from Proposition 1, we can obtain
As a consequence,
From (1), we know that
Next, by the contractive condition of T, we have
Using (7), the continuity of and lower semi continuity of , passing to the limit as in the above inequality, we obtain
which is a contradiction since .
Hence, is a Cauchy sequence. The completeness of ensures that the sequence converges to some , that is, for all . Moreover,
This implies that
Taking limit as in the above inequality, we obtain
that is
and so by condition , .
Since and , then there exists such that
Passing to the limit as in the above inequality and using the continuity of and M, the lower semi continuity of , we obtain
which implies that for all .
By Proposition 2, we conclude that , that is, , for all .
To prove the uniqueness of , assume to the contrary that for all and is another best proximity point of T, i.e., for all .
Then, from the contractive condition of T, we have
which is a contradiction and hence for all , that is, . This completes the proof. □
Example 1.
Let be endowed with a standard fuzzy metric given by
where is the metric defined by
for all and . It is obvious that the non-Archimedean property holds true provided by for all . Thus, is a non-Archimedean fuzzy metric space. Moreover, the completeness of can be obtained from the the completeness of the metric space (see Result 2.9 in [40]).
Define
So that, and for all . Obviously, are nonempty closed subsets of X.
Define a mapping by . Clearly, , and the condition of Theorem 2 holds true.
Let us consider such that
are satisfied for all .
Solving the above two equations, we have and .
Define and by and , for all .
Indeed, for all ,
So that, we have .
Then, the non-self mapping is the -weak proximal contraction of type I.
All conditions of Theorem 2 are satisfied. After simple computation, we can conclude that is the unique best proximity point of T.
In view of Theorem 2, we can obtain the following theorem.
Theorem 3.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is continuous at the 1-boundary. Let A and B be two nonempty closed subsets of X such that is nonempty and closed. Let be a non-self mapping satisfying the following conditions:
- ;
- T is a -weak proximal contraction of type I.
Then, T has a unique best proximity point , which in fact belongs to .
Proof.
Repeating the proof of Theorem 2, we can construct a Cauchy sequence in . Since is closed, the completeness of guarantees that the sequence converges to some . Hence, the final conclusion can be drawn by running the same lines as the rest of the proof of Theorem 2. □
2.2. -Weak Proximal Contraction of Type and Its Best Proximity Point Results
Definition 9.
Let be a fuzzy metric space. A non-self mapping is called a -weak proximal contraction of type , if there exist and such that for all and ,
where .
Theorem 4.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is continuous at the 1-boundary. Let A and B be two nonempty closed subsets of X such that is nonempty. Let be a non-self mapping satisfying the following conditions:
- ;
- T is a -weak proximal contraction of type ;
- For any sequence and satisfying as , then .
Then, T has a unique best proximity point , which in fact belongs to .
Proof.
Let be an arbitrary element of . Following the same routines in the proof of Theorem 2, we can construct a sequence in satisfying
If there exists such that for all , it follows that
Hence, for all , so is a best proximity point of T.
Without loss of generality, in the following, we may assume that for all , that is, for all .
Since T is a -weak proximal contraction of type , we obtain that for all
where
Case 1. Assume that , that is,
It follows from (10) that
From the monotone property of , we have
Case 2. Assume that .
It follows from (10) that
In addition, from the monotone property of , we have
From Case 1 and Case 2, we obtain that is an increasing sequence in which is bounded above by 1. Then, there exists such that for all .
Meanwhile, again from (10) and Proposition 1, we have
Let us prove that for all .
Indeed, passing to the limit as in (12) and (15), using (16), and the continuity of and M, and lower semi continuity of , we have
which implies that for all .
Therefore, for all .
Next, we shall prove that is a Cauchy sequence.
Assuming that this is not true and proceeding as in the proof of Theorem 2, there exist and such that for all , there are with such that
Applying the contractive condition of T, we obtain
where
Using the continuity of and M and the lower semi continuity of , passing to the limit as in the above inequality, we have
and so , contradictorily. Thus, is a Cauchy sequence.
The completeness of ensures that there exist such that for all .
Again, using condition and proceeding as in the proof of Theorem 2, we can find such that
Consequently, from the contractive condition of T, we have
where
Taking limit as in the above inequality and using the continuity of and M, and lower semi continuity of , we have
where implies that for all .
By Proposition 2, we conclude that , that is, for all .
We proceed to prove that is the unique best proximity point of T.
Assume to the contrary that for all and is another best proximity point of T, i.e., for all .
Then, from the contractive condition of T, we have
where
Hence, for all .
This implies that for all , that is, . This completes the proof. □
Similar to Theorem 3, we can provide the following theorem.
Theorem 5.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is continuous at the 1-boundary. Let A and B be two nonempty closed subsets of X such that is nonempty and closed. Let be a non-self mapping satisfying the following conditions:
- ;
- T is a -weak proximal contraction of type .
Then, T has a unique best proximity point , which in fact belongs to .
Proof.
Repeating the proof of Theorem 4, we can construct a Cauchy sequence in . Since is closed, the completeness of guarantees that there exists a point such that . The rest of the proof is same as the one of Theorem 4. For brevity, we omit it. □
Example 2.
Let be endowed with a fuzzy metric given by
for all , where is a metric defined by
Thus, the non-Archimedean property holds true provided by for all and the triangle inequality property of d.
Moreover, let be a Cauchy sequence in . Then, for , there exists such that for all . Therefore, for each , converges to 1 as which implies converges to 0 as . Thus, is also a Cauchy sequence in . Take and . It is obvious that is a nested sequence of nonempty closed sets with diameter tending to 0. From the construct of X, we have that . Take . If is arbitrary, then there exists such that and thus, , for . This implies that for , so that converges to x which implies that is complete. Thus, the completeness of follows from the Theorem 3.3 in [40].
Define the following two closed subsets of X as follows:
Define a mapping by , and by and .
Then, and , for all .
Clearly, it is easy to check that
and is closed with .
Now, let such that for all .
Then, we have , , , , with . After simple calculation, we have
It follows from the above equations that
and
Since , then T is a -weak proximal contraction of type . Thus, all the hypothesis of Theorem 5 are fulfilled. Therefore, we can deduce that the mapping T has a unique best proximity point in (which is the point 2).
2.3. -Weak Proximal Contraction of Type and Its Best Proximity Point Results
Definition 10.
Let be a fuzzy metric space. A non-self mapping is called a -weak proximal contraction of type if there exist and such that for all and
.
Theorem 6.
Letbe a complete non-Archimedean fuzzy metric space whose t-norm is continuous at the 1-boundary. Let A and B be two nonempty closed subsets of X such that such thatandare nonempty. Letbe a non-self mapping satisfying the following conditions:
- ;
- T is a-weak proximal contraction of type;
- A is approximately compact with respect to B.
Then, T has a best proximity point, which in fact belongs to. In addition, if T is injective, then the best proximity point of T is unique.
Proof.
As in the proof of Theorem 2, we can construct a sequence in such that
Without loss of generality, we may assume that for all , that is, for all .
Taking advantages from the contractive condition of T, we have
From the monotone property of , we have
Hence, the sequence is an increasing sequence in which is bounded above by 1 and there exists such that for all .
Using the continuity of and M, the lower semi continuity of , passing to the limit as in (18), we obtain
which implies that for all .
Next, we shall prove that is a Cauchy sequence.
Assume that this is not true and applying the similar arguments as in the proof of Theorem 2, there exist and such that for all , there are with such that
Using the contractive condition of T, we obtain that
Taking limit as in the above inequality, from the continuity of and M and the semi continuity of , we have
which is a contradiction since .
Hence, is a Cauchy sequence. The completeness of ensures that is convergent to some element in B.
Furthermore,
Therefore, . In view of the fact that A is approximately compact with respect to B, has a subsequence convergent to some .
Therefore, it can be concluded from the continuity of M that
As a consequence, is an element of , also, .
Since , then there exists such that .
From the contractive condition of T, we have
Taking the limit as in the above inequality, we obtain
Hence, for all , that is, .
Therefore, for all , which implies that is a best proximity point of T.
Suppose that T is injective and there is another element such that for all .
Since T is a -weak proximal contraction of type , we have
which implies that for all , as mapping T is an injective mapping, hence .
This completes the uniqueness of the best proximity point of T. □
Theorem 7.
Suppose that all the assumptions of Theorem 6 are satisfied except the assumption is replaced by .
: For any sequence and satisfying as , then .
Then, the conclusion of Theorem 6 still holds.
Proof.
In fact, proceeding the similar lines as in the proof of Theorem 6, we can obtain a Cauchy sequence with for all . The completeness of guarantees that there exists such that .
Taking limit as in the following inequality
Therefore, we can have for all .
In view of assumption , . Since , then there exists such that . Since , there exists such that
Again, using the contractive condition of T, we have
Using the continuity of and M and the lower semi continuity of , passing to the limit as in the above inequality, we have
which implies that for all , that is, .
As mapping T is an injective mapping, hence , also for all . This shows that is a best proximity point of T.
For brevity, we omit the rest of the proof of the uniqueness of the desired point which is the same as the one in the proof of Theorem 6. □
Example 3.
Let X be endowed with a fuzzy metric given by
where is the metric defined by
for all .
Thus, the non-Archimedean fuzzy property holds true provided by for all and the triangle inequality property of metric d.
Using analysis similar to that in the proof of Example 2, we can show that is complete.
Define the sets as follows
Thus, and for all .
Define a mapping by for all .
Notice that , , and the condition of Theorem 7 holds true.
Define by and .
Assume that there exist such that for all . Then, we have .
Since
It is easy to check that T is a -weak proximal contraction of type . Hence, all the conditions of Theorem 7 are satisfied. Therefore, T has a unique best proximity point in (which is ).
Theorem 8.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is 1-boundary continuous. Let A and B be two nonempty closed subsets of X such that and are nonempty with is closed. Let be a non-self injective mapping satisfying the assumptions (or ) presented in Theorem 6 or Theorem 7.
Then, T has a unique best proximity point , which in fact belongs to .
Proof.
Analysis similar to the proof of Theorem 6 (or Theorem 7), we obtain a Cauchy sequence in . The completeness of and the closedness of ensures that there is such that . The rest of the proof can run as the one of Theorem 6. □
2.4. -Weak Proximal Contraction of Type and Its Best Proximity Point Results
Definition 11.
Let be a fuzzy metric space. A non-self mapping is called a -weak proximal contraction of type , if there exist such that for all and
.
Theorem 9.
Letbe a complete non-Archimedean fuzzy metric space whose Product normis 1-boundary continuous. Let A and B be two nonempty closed subsets of X such thatis nonempty. Letbe a non-self mapping satisfying the following conditions:
- ;
- T is a-weak proximal contraction of type;
- A is approximately compact with respect to B.
Then, T has a unique best proximity point , which in fact belongs to .
Proof.
As in the proof of Theorem 2, we can construct a sequence such that
Without loss of generality, we may assume that for all or equivalently, for all and .
Using the contractive condition of T and the monotone property of , we have
which implies that for all .
Hence, the sequence is an increasing sequence in which is bounded above by 1 for all . Then, there exist such that .
Taking limit as in (20), we have
which implies that for all .
Taking into account the following inequalities
and passing to the limit as , we obtain
Now, we shall show that is a Cauchy sequence.
Taking advantages from the contractive condition, we obtain
Using (22) and taking the limit in the above inequality, we have
which together with the property of , implies that for all .
Hence, is a Cauchy sequence. From the completeness of , we obtain converges to some point .
In the same manner as the proof of Theorem 2, we can prove that is a unique best proximity point of T. For brevity, we omit the rest of the proof. □
Theorem 10.
Letbe a complete non-Archimedean fuzzy metric space whose Product normis 1-boundary continuous. Let A and B be two nonempty closed subsets of X such thatis nonempty and closed. Letbe a non-self mapping satisfying the following conditions:
- ;
- T is a-weak proximal contraction of type.
Then, T has a unique best proximity point, which in fact belongs to.
Proof.
Applying the same arguments as in the proof of Theorem 9, we can construct a Cauchy sequence in . Moreover, since is closed, the completeness of guarantees that the sequence converges to some . The rest of the proof is same as the one of Theorem 2. □
Remark 2.
If we suppose that the non-self mapping T in Theorems 2, 4, 6, 7 and 9 is continuous, then we can omit the assumption(or) in these theorems mentioned above.
Example 4.
Letand letbe a fuzzy metric given by
for all, whereis the standard metric
for alland.
The non-Archimedean property holds true provided byfor alland the triangle inequality property of metric d.
Analysis similar to that in the proof of Example 2, we can show thatis complete.
Define the sets
Hence,andfor all. Clearly,are nonempty closed subsets of X.
Define a mappingby
In addition, defineand by and .
Notice thatand. So, all the conditions except conditionof Theorem 10 are satisfied.
Next, we will verify the condition.
Assume thatfor some. Then,
Now, we consider the following cases:
Case 1. Ifandfor, we have
Hence, we have
Case 2. Ifandfor all, we have
Hence, we have
Case 3. If If, we have
Hence, we have
Now, we can conclude that T is a-weak proximal contraction of type. Therefore, all the conditions of Theorem 10 are satisfied. Therefore, T has a unique best proximity point in(which is).
2.5. Consequences in Fixed Point Theory
In this section, we will discuss results which ensure the existence of fixed point for some -weak contractions in the setting of a non-Archimedean fuzzy metric space . These properties can be considered as applications of the above stated results in fixed point theory. They are obtained by considering in the second section.
We start with providing four new types of -weak contractions for self mappings in a non-Archimedean fuzzy metric space .
Definition 12.
Letbe a fuzzy metric space. A mappingis called a-contraction of type I if there existandsuch that for alland
Definition 13.
Letbe a fuzzy metric space. A mappingis called a-contraction of typeif there existandsuch that for alland
where.
Definition 14.
Letbe a fuzzy metric space. A mappingis called a-contraction of typeif there existandsuch that for alland
Definition 15.
Letbe a fuzzy metric space. A mappingis called a-contraction of typeif there existandsuch that for alland
Theorem 11.
Letbe a complete non-Archimedean fuzzy metric space whose t-norm is 1-boundary continuous. Letbe a-contraction of type I. Then, T has a unique fixed pointin X.
Proof.
Consider a self mapping which is a -contraction of type I. Proceeding the similar argument as in the proof of Theorem 2, we can generate a fuzzy Picard sequence defined by which converges to , that is, for all .
Taking in (23), we have
Passing to the limit as in the above inequality and using the continuity of and M, the lower semi continuity of , we obtain
which implies for all . Analysis similar to that in the proof of Theorem 2 shows is a unique fixed point of mapping T. □
Remark 3.
It is noted that the consequence of the best proximity result stated as Theorem 2 can be reduced to Theorem 11 which coincides with Theorem 2,2 in [26].
Theorem 12.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is 1-boundary continuous. Let be a -contraction of type . Then, T has a unique fixed point in X.
Proof.
Consider a self mapping which is a -contraction of type . Proceeding the similar argument as in the proof of Theorem 4, we can generate a fuzzy Picard sequence defined by which converges to , that is for all . Following the rest of the proof of Theorem 4, we can prove that is a unique fixed point of T. □
Theorem 13.
Let be a complete non-Archimedean fuzzy metric space whose t-norm is 1-boundary continuous. Let be a -contraction of type . Then, T has a fixed point in X. Moreover, if T is injective, the fixed point is unique.
Proof.
Consider a self mapping which is a -contraction of type . Proceeding the similar argument as in the proof of Theorem 6, we can generate a fuzzy Picard sequence defined by which converges to , that is for all . Along with the same route in the proof of Theorem 6 we can prove that is a unique fixed point of T. □
Theorem 14.
Let be a complete non-Archimedean fuzzy metric space whose Product t-norm is 1-boundary continuous. Let be a -contraction of type . Then, T has a unique fixed point in X.
Proof.
Consider a self mapping which is a -contraction of type . Proceeding the similar argument as in the proof of Theorem 9, we can generate a fuzzy Picard sequence defined by which converges to , that is for all . Along with the same route in the proof of Theorem 9, we can prove that is a unique fixed point of T. □
3. Application in Domain of Words
As fuzzy metric spaces are linked in a very natural way with applications in the computer sciences, in this section, we will show the existence of fixed point for -contraction of type I mappings on the domain of words when it is endowed with a non-Archimedean fuzzy metric.
Suppose that a nonempty set of alphabets is denoted by and represents the set of all finite and infinite words over . Note that represents the empty words/sequence contained in . Suppose a prefix order on denoted by ⋁ and defined as
For every nonempty (word) , and the length of a is and . Furthermore, if is finite, then and we write
otherwise we write
Now, for the common prefix of a and b is represented by . It is to be noted that if and only if and and . Define by
If , then and if , then . Therefore, for all , we can write
is a complete Baire metric [41] on . Define a fuzzy metric over as:
Then, represents a complete non-Archimedean fuzzy metric space, where the t-norm is The Quicksort algorithm gives the recurrence relation
For more details, we refer the readers to [42,43]. For , in correspondence to the above sequence, we define the function that assigns to and is defined by
Note that
for all and in particular
whenever . By definition of f, we have
and this implies that
for all . Hence, for all . We apply Theorem 11 and prove that the functional f has a fixed point. Also, define and by and . Then, consider the following two cases:
Case 1: If , then we have
and
Case 2: If then for all we have
that is, further
Now,
for all and all .
After a simple computation, we have
Thus, all conditions of Theorem 11 are satisfied and f has a fixed point which is the required fixed point of f. Hence, we have
Remark 4.
The prefix order ⋁ on defined as above is a partial order on (domain of words) which is associated with the graph via the relation
where is the set of edges of graph G and the graph with . Some problems related to the domain of words can be considered in connection with the graphs as well to solve some problems related to networks.
4. Conclusions
To find an optimal distance between two objects is considered an important problem in mathematics, which is at the root of several studies related to fields such as geometry, mechanics, computer sciences, optimization, etc. Here, our candidates are sets and the framework of study is the non-Archimedean fuzzy metric spaces. In this paper, we defined some new classes of -weak proximal contractions and proved the existence of unique best proximity point/fixed point for such contractions in complete non-Archimedean fuzzy metric spaces without fuzzy P-property. Moreover, we also present some examples to illustrate the main results and an application in computer science, particularly in the domain of words as well. The prospects of the present study lie in the possibilities of further generalizations as well as in its possible applications in the domains of fuzzy geometry, fuzzy optimization, etc. The present paper is a fuzzy generalization of the proximity point problem by means of fuzzy fixed point methodologies. Meanwhile, we put forward some directions worthy further consider in the future:
- To generalize the existing proximal contractions and to prove the existence of the unique best proximity point in non-Archimedean fuzzy spaces;
- Under the assumption that and are dependent from the real parameter t, what proximal contractions and other properties can guarantee the existence of the unique best proximity point;
- To study the best proximity point results and related applications for the -cyclic proximal contractions in the fuzzy settings;
- As fuzzy quasi metric spaces are linked in a very natural way with applications in computer sciences (see [41]), so the results in this paper can be investigated in connection with fuzzy quasi metric spaces with some applications.
Author Contributions
Conceptualization, M.Z. and N.S.; formal analysis, M.Z., N.S.; investigation, M.Z., N.S., A.F.R.L.d.H. and X.L.; writing—original draft preparation, M.Z. and N.S.; writing—review and editing, M.Z., N.S., A.F.R.L.d.H. and X.L. All authors have read and agreed to the published version of the manuscript.
Funding
There is no funding to support this work.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare that they have no competing interests.
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