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Abstract
In this paper, first we introduce the notion of proximally weakly compatible mappings and we extend the CLRg property (CLRg-Common limit in the range of g) to the case of non-self mappings. Then we prove the existence of proximally coincident point for this new class of mappings with the assumption of proximal CLRg property in fuzzy metric space. Further, we establish new common best proximity point result for proximally weakly compatible mappings in the setting of fuzzy metric space.
1. Introduction and Preliminaries
Fixed point theory is one of the most powerful and fruitful tools in nonlinear analysis and it has many applications in optimisation theory, economics, control theory and game theory. So, the researchers showed more interest to prove fixed point theorems for different kind of contractions in different domains and spaces. Later on, the study of notion of common fixed points of mappings satisfying certain contractive conditions gets attention by many researchers. In the sequel, in 1982, Sessa [Citation1] proved the results on existence of common fixed points for weakly commuting pair of mappings in metric space. Further, Jungck [Citation2] extended the concept weakly commuting pair of mappings to compatible mappings and obtained theorems for common fixed points in metric space. In 1996, Jungck [Citation3] studied the notion of weakly compatible mappings to find fixed point results for set-valued non continuous mappings.
In case of non-self mappings, there is no assurance for the solution of fixed point equation . In this situation, one often tries to find an element
such that
is closest to
. Such a point is called best proximity point. The best proximity point theorems possess the sufficient conditions that ensure the existence of approximate solutions for fixed point equations. For existence of best proximity point theorems in metric space, one can refer [Citation4–7].
On the other hand, Zadeh [Citation8] introduced the concept of fuzzy sets and studied some properties of fuzzy sets and its membership values. Later, George and Veeramani [Citation9] introduced the fuzzy metric space and proved some basic results of metric spaces in the setting of fuzzy metric space. Fang [Citation10] proved the theorems on common fixed point under -contraction for compatible and weakly compatible mappings in Menger probabilistic metric space. Xin-Qi Hu [Citation11] gave common coupled fixed point theorems for mappings under
-contractive conditions in fuzzy metric spaces and these results are generalisation of S. Sedghi et al. [Citation12]. Later on, Aamri and Moutawakil [Citation13] gave the concept of E. A property for mappings in metric space and derived results on existence of unique common fixed point for weakly compatible pairs. The notion of CLRg(Common limit in the range of g) property introduced by Sintunavarat and Kumam [Citation14] to prove existence of common fixed point for weakly compatible mappings in fuzzy metric spaces in the sense of Kramosil and Michalek and in the sense of George and Veeramani. Then, M. Jain et al. [Citation15] extended the concept of E. A property and (CLRg) property for coupled mappings and proved theorems on common fixed point for weakly compatible maps in fuzzy metric spaces, which are generalisation of the results in [Citation11]. Recently, in [Citation16], the authors derived new common fixed point theorems for weakly compatible mappings in fuzzy metric space using CLRg property and deduced some corollaries. These results extend the corresponding results in [Citation15]. In the case of non-self mappings on fuzzy metric space, recently, the researchers desire to prove the existence and uniqueness of best proximity point for different kind of contractions. For more details, we refer [Citation17–20].
In the light of above works, we are motivated to think that how one can get common fixed point for non-self weakly compatible mappings. In case of non-self mappings, one can identify that we cannot get common fixed point. At this moment, to find approximate common fixed point (called common best proximity point) we extend the notions weakly compatible mappings and CLRg property to the case of non-self mappings. So, in this research work, first we define the notions proximally weakly compatible and proximal CLRg property for non-self mappings in fuzzy metric space. Using this proximal CLRg property, we establish the existence of common best proximity point for proximally weakly compatible mappings in the setting of fuzzy metric space. The proposed results in this article generalise and extend some of results in [Citation16].
First, we recall the following terminologies from the work of [Citation9,Citation11,Citation21]:
Definition 1.1:
[Citation9] A binary operation is called a continuous
-norm if itsatisfies the following conditions:
* is commutative, continuous and associative;
for all
whenever
and
for all
Definition 1.2:
[Citation9] A fuzzy metric space is an ordered triple such that
is a (nonempty)set,
is a continuous
-norm and
is a fuzzy set on
satisfying the following conditions, for all
if and only if
is continuous;
is left continuous.
In the Definition 1.2, if we assume the conditions (i), (iii), (iv), (v), (vii) then the triple is called a KM fuzzy metric space (in the sense of Kramosiland Michálek) and if we assume the conditions (ii), (iii), (iv), (v), (vi) then the triple
is called a GV fuzzy metric space (in the sense of Georgeand Veeramani).
For convenience, we denote , for all
Definition 1.3:
[Citation9] Let be a fuzzy metric space, then a sequence
in
is said to be convergent to
if
for all
Definition 1.4:
[Citation21] For each the sequence
is defined by
and
A
-norm
is said to be of
-type if the sequence of functions
is equicontinuous at
Definition 1.5:
[Citation11] Define where
and each
satisfies the following condition:
is nondecreasing;
is upper semicontinuous from the right;
for all
where
It is easy to prove that, if then
for all
Here, we remind the notion of fuzzy distance in fuzzy metric space. Let be a nonempty subsets of a fuzzy metric space
The fuzzy distance of a point
from a nonempty set
for
is defined as
and the fuzzy distance between two nonempty sets
and
for
is defined as
Definition 1.6:
[Citation18] Let and
be two nonempty subsets of a fuzzy metric space
We define
and
as follows:
We extend the Definition 1.1 in [Citation16] in the setting of fuzzy metric space.
Definition 1.7:
Let and
be two nonempty subsets of a fuzzy metric space
and let
and
We say the element
if proximally coincident point if
for some
and for all
Example 1.8:
Let with usual metric
and consider
and
And we define fuzzy metric on
by
So we get
Now we define
by
and
by
Then clearly, we have
So the point
is proximally coincidentof
and
Definition 1.9:
[Citation18] Let and
be two nonempty subsets of a fuzzy metric space
andlet
and
We say the element
is commonbest proximity point if
2. Main Results
In this section, first we define proximal CLRg property for non-self mappings which extends the definition (CLRg property) as in [Citation16].
Definition 2.1:
Let be a fuzzy metric space under some continuous
-norm*. Two mappings
are said to have the proximal CLRg property if there exists a sequence
and a point
with
such that
where
Example 2.2:
Let with usual metric
and consider
and
And we define fuzzy metric on
by
So we get
Now we define
by
and
by
Now we consider the sequence
for
and
So we have
Here
Therefore we obtain the sequence
such that
Clearly
as
And also
Therefore we obtain the sequence
such that
Clearly
as
Next, we introduce a new class of non-self mappings, called proximally weakly compatible mappings in the setting of fuzzy metric space.
Definition 2.3:
The mappings are proximally weakly compatible if
then
for all
Example 2.4:
Let with usual metric
and weconsider
And we define fuzzy metric on
by
So we get
Now define
by
and
by
Now we justify proximally weakly compatible of
and
via followingpossibilities:
then we get
and
then we get
Then
and
are proximally weakly compatible.
Remark 2.5:
In the above definition, suppose we assume then clearly one can identify that
. This implies that
and
Then the Definition 2.3 reduces to weakly compatible mappings in [Citation16].
The following existence theorem of coincident point and common fixed point for mappings using CLRg property were discussed in [Citation16].
Lemma 2.6:
[Citation16] Let be a fuzzy metric space under some continuous
-norm* and let
be mappings having the CLRg property, that is, there is a sequence
and
such that
and
Assume that there exist
and
such that
for all
and
for all
and all
Then
that is,
and
have a coincidence point.
Definition 2.7:
[Citation16] Define the family of all functions
such that the following properties are
for all
for all
By condition implies
for all
Clearly,
Theorem 2.8:
[Citation16] Let be a fuzzy metric space such that * is continuous
-norm of
-type and let
be weakly compatible mappingshaving the CLRg property. Assume that there exist
and
such that
for all
and all
Then
and
have a unique common fixed point.
First, we prove existence of proximally coincident point for mappings using CLRg property which improves the Lemma 2.6 and it helps to prove our main result.
Lemma 2.9:
Let be a fuzzy metric space under some continuous
-norm * and let
and
be mappings having proximal CLRg property and there exists
satisfying
provided
for all
Then there exist
such that
for some
Proof:
Since the pair satisfies proximal CLRg property there exist a sequence
and a point
with
such that
For all
we have
is non-decreasing function, then
As
we have
Since
is continuous then
Therefore, we obtain
By uniqueness of limit we get
Then
Now we prove the following existence theorem on common best proximity point for proximally weakly compatible mappings using proximal CLRg property.
Theorem 2.10:
Let be a fuzzy metric space under some continuous
-norm * of
-type and let
and
be mappings havingthe proximal CLRg property with
Assume that there exist
and
satisfying
provided
for all
Suppose the pair
is proximallyweakly compatible, then there exists a unique
such that
Proof:
Since the pair satisfies CLRg property there exist a sequence
and a point
with
such that
Then by Lemma 2.9,
Therefore
is proximally coincident point of
and
. That is,
Since the pair
is proximally weakly compatible then
Since
then there exists
such that
Now we prove that
For, fix
arbitrary. As
is of
-type, there exists
such that if
then
for all
We know that
so there exists
such that
Therefore, we have that
for all
. We note that,
Similarly, we can obtain
In general, we have
for all
As then
Also, as
there is
such that
It follows,
Since
are arbitrary, we deduce that
for all
Then
Hence we get
In the same manner, we can prove the uniqueness of common best proximity point.
The following example illustrates the above theorem.
Example 2.11:
Let with usual metric
and consider
and
And we define fuzzy metric on
by
Then
is a fuzzy metric space under
One can identify
Now we define
by
and
by
Now we consider the sequence for
and
So we get
Here we have
Therefore we obtain the sequence
such that
Clearly
as
And also
Therefore we obtain the sequence
such that
Clearly
as
It shows
and
have proximal CLRg property. By assuming
for all t
one can easily verify that
and
agree the proximal contractive condition (1) for any
Also we can observe
and
are proximally weakly compatible. Then
and
have a unique common best proximity point
in
Acknowledgments
The authors would like to thank the National Board for Higher Mathematics (NBHM), DAE, Govt. of India for providing a financial support under the grant number 02011/22/2017/R&D II/14080.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
V. Pragadeeswarar
V. Pragadeeswarar was born in 1986 in Tamil Nadu, India. He received Master, MPhil and PhD degrees in Mathematics from Bharathidasan University, Trichy in 2009, 2010 and 2015, respectively. Currently, he is an Assistant Professor (Sr. G) in the Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India. He has published 10 research papers in international journals.
R. Gopi
R. Gopi received Master and MPhil degrees in Mathematics from Bharathidasan University, Trichy in 2011 and 2012, respectively. Now, he is a PhD student in Department of Mathematics, Amrita Vishwa Vidyapeetham, Coimbatore, India.
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