A discussion on best proximity point and coupled best proximity point in partially ordered metric spaces
 Binayak S Choudhury^{1},
 Nikhilesh Metiya^{2},
 Mihai Postolache^{3}Email author and
 Pulak Konar^{4}
https://doi.org/10.1186/s1366301504231
© Choudhury et al. 2015
Received: 4 June 2015
Accepted: 8 September 2015
Published: 18 September 2015
Abstract
In this paper we establish some best proximity point results using generalized weak contractions with discontinuous control functions. The theorems are established in metric spaces with a partial order. We view the main problem in the paper as a problem of finding an optimal approximate solution of a fixed point equation. We also discuss several corollaries and give an illustrative example. We apply our result to obtain some coupled best proximity point results.
Keywords
partially ordered set control function best proximity point coupled best proximity pointMSC
47H10 54H10 54H251 Introduction and mathematical preliminaries
In this work we consider a problem of global optimization in the context of partially ordered metric spaces. Specifically it is a problem of finding the minimum distance between two subsets of a partially ordered metric space. We utilize a generalized weakly contractive nonselfmap for this purpose. In fact nonselfmaps have been utilized for the said purposes under a category of problems which has been termed the proximity point problems. This category of problems had its origin in the work of Eldred and Veeramani [1] in 2006 and has, in subsequent times, developed vastly through a large number of works. The following is the description of this problem. Let \((X, d)\) be a metric space. Let A and B be two subset of X. A pair \((a, b)\in A\times B\) is called a best proximity pair if \(d(a, b) = d(A, B)= \inf \{d(x, y) : x \in A \text{ and } y \in B\}\). If A and B are two nonempty subsets of a metric space \((X, d)\) and T is a mapping from A to B, then \(d(x, Tx) \geq d(A, B)\) for all \(x \in A\). A point \(z \in A\) is called a best proximity point (with respect to T) if at the point z the function \(d(x, Tx)\) attains its global minimum with the value \(d(A, B)\); that is, \(d(z, Tz) = d(A, B)\). Thus the problem is a problem of global minimization. In another approach to this problem, it can be viewed as an approximate fixed point problem [2]. We adopt this approach in this paper. The description of this viewpoint is in the following. For the mapping \(T \colon A \rightarrow B\), the idea of a fixed point, that is, a point for which \(x = Tx\) is not pertinent when A and B are disjoint. Even in the cases where \(A \cap B \neq\emptyset\), a fixed point of the function T only exists under special conditions. But it may be possible to find some sort of approximate fixed point of T by minimizing the function \(d(x, Tx)\). If the minimized value is \(d(A, B)\), then we obtain a proximity point at which the proximity pair is realized. Thus the proximity point problem is to find an optimal approximate solution of the fixed point equation \(Tx = x\). Thus several methodologies available in the fixed point theory can be adapted to the situation. It is pertinent to point out that proximity point problems are different from best approximation problems which are not necessarily a global optimization problem. A best approximation theorem provides us with best approximate solutions which is not necessarily optimal. As an instance we consider the following Ky Fan best approximation theorem.
Theorem 1.1
([3])
The point x in the above theorem need not provide with the optimum value of \(\ x  Tx\\). On the contrary the best proximity point theorems assert that the approximate solution of the fixed point equation is also globally optimal. Technically, through a best proximity point result we obtain the global minima of the real valued function \(x \mapsto d(x, Tx)\) by constraining an approximate solution of \(x = Tx\) to satisfy \(d(x, Tx) = \operatorname{dist}(A, B)\).
In the proximity point problems, there are several uses of functions satisfying contraction conditions as, for instances, in [4–16]. The contraction condition in the context for fixed point theory first appeared in the celebrated work of Banach [17], which, incidentally, is also recognized as the source of fixed point theory. Afterwards, contractive conditions have taken a large place in metric fixed point theory. For a survey of this development we refer to [18]. Weak contractions were introduced in Hilbert spaces by Alber and GuerreDelabriere [19] and subsequently extended to metric spaces by Rhoades [20]. This is a condition which is intermediate to contraction and nonexpansion. Weak contractions were studied in metric spaces and in partially ordered metric spaces through the works [21–30]. Particularly in 2013, a generalized weak contraction inequality was given by Choudhury et al. [24] which was utilized to obtain coincidence and coupled coincidence point theorems in partially ordered metric spaces.
It may be mentioned that coupled fixed point problems, and their allied problems, have attracted a large general interest amongst mathematician after the appearance of the work of Gnana Bhaskar and Lakshmikantham [31] in 2006 in which a coupled contraction mapping theorem was established although the concept of coupled fixed point was introduced in 1987 in the work of Guo and Lakshmikantham [32]. Amongst several works in the above mentioned area there are also coupled weak contraction results as, for instance, in [33] and [24]. Coupled contractions have also been utilized in best proximity problems in works like [34–37].
The purpose of the paper is to obtain proximity point results in partially ordered metric spaces by utilizing the weak contractive inequality obtained in [24]. In this context it is to be mentioned that the weak contraction has already been used to obtain proximity point theorem by Sankar Raj [16]. We have an application of our main result to a product space through which we obtain a coupled proximity point result. An illustrative example is also discussed.
The following are the requisite mathematical concepts for the discussions in this paper.
Definition 1.1
(Pproperty [16])
In [4], Abkar and Gabeleh show that every nonempty, bounded, closed, and convex pair of subsets of a uniformly convex Banach space has the Pproperty. Some nontrivial examples of a nonempty pair of subsets which satisfies the Pproperty are given in [4].
Definition 1.2
Definition 1.3
([7])
One can see that, for a selfmapping, the notion of proximally increasing reduces to that of an increasing mapping.
Definition 1.4
Definition 1.5
An element \(x^{*}\in A\) is said to be best proximity point of the mapping \(T \colon A \rightarrow B\) if \(d(x^{*}, Tx^{*}) = d(A, B)\).
Definition 1.6
([31])
Definition 1.7
([35])
One can see that, if \(A = B\) in the above definition, the notion of the proximal mixed monotone property reduces to that of the mixed monotone property.
In Section 3, while applying the results of Section 2 to obtain coupled best proximity point results, we will require the property in Definition 1.7 to be satisfied only on an appropriate subset of \(A\times A\). For that purpose we introduce the following definition.
Definition 1.8
Definition 1.9
([36])
An element \((x^{*}, y^{*})\in A \times A\), is called a coupled best proximity point of the mapping \(F \colon A \times A \rightarrow B\) if \(d(x^{*}, F(x^{*}, y^{*})) = d(A, B)\) and \(d(y^{*}, F(y^{*}, x^{*})) = d(A, B)\).
The speciality of coupled proximity points is that they provide for the realization of the minimum distance in two ways simultaneously.
 (i_{ ψ }):

ψ is continuous and \(\psi(t) = 0\) if and only if \(t = 0\);
 (i_{ α }):

α is bounded on any bounded interval in \([0, \infty)\),
 (ii_{ α }):

α is continuous at 0 and \(\alpha(0) = 0\).
2 Main results
Theorem 2.1
 (a)
T is continuous or
 (b)
if \(\{x_{n}\}\) is a nondecreasing sequence in X such that \(x_{n} \rightarrow x\), then \(x_{n} \preceq x\) for all \(n \geq0\).
Also, suppose that there exist elements \(x_{0}, x_{1} \in A_{0}\) such that \(d(x_{1}, Tx_{0}) = d(A, B)\) and \(x_{0} \preceq x_{1}\). Then T has a best proximity point in \(A_{0}\); that is, there exists an element \(x^{*} \in A_{0}\) such that \(d(x^{*}, Tx^{*}) = d(A, B)\).
Proof
Next we show that \(\{x_{n}\}\) is a Cauchy sequence.
Let the condition (a) hold.
Taking \(n\rightarrow \infty\) in (2.4) and using the continuity of T, we have \(d(x^{*}, Tx^{*}) = d(A, B)\); that is, \(x^{*}\) is best proximity point of T.
Let the condition (b) hold.
By the condition (b) of the theorem, (2.5), and (2.12), we have \(x_{n} \preceq x^{*}\) for all \(n \geq N\).
Considering ψ to be the identity mapping and \(\theta(t) = 0\) for all \(t \in[0, \infty)\) in Theorem 2.1 we have the following corollary.
Corollary 2.1
 (a)
T is continuous or
 (b)
if \(\{x_{n}\}\) is a nondecreasing sequence in \(A_{0}\) such that \(x_{n} \rightarrow x\), then \(x_{n} \preceq x\) for all \(n \geq0\).
Also, suppose that there exist elements \(x_{0}, x_{1} \in A_{0}\) such that \(d(x_{1}, Tx_{0}) = d(A, B)\) and \(x_{0} \preceq x_{1}\). Then T has a best proximity point in \(A_{0}\).
Considering φ to be the function ψ in Theorem 2.1 we have the following corollary.
Corollary 2.2
 (a)
T is continuous or
 (b)
if \(\{x_{n}\}\) is a nondecreasing sequence in \(A_{0}\) such that \(x_{n} \rightarrow x\), then \(x_{n} \preceq x\) for all \(n \geq0\).
Also, suppose that there exist elements \(x_{0}, x_{1} \in A_{0}\) such that \(d(x_{1}, Tx_{0}) = d(A, B)\) and \(x_{0} \preceq x_{1}\). Then T has a best proximity point in \(A_{0}\).
Considering ψ and φ to be the identity mappings and \(\theta(t)= (1k) t\), where \(0\leq k < 1\) in Theorem 2.1, we have the following corollary.
Corollary 2.3
 (a)
T is continuous or
 (b)
if \(\{x_{n}\}\) is a nondecreasing sequence in \(A_{0}\) such that \(x_{n} \rightarrow x\), then \(x_{n} \preceq x\) for all \(n \geq0\).
Also, suppose that there exist elements \(x_{0}, x_{1} \in A_{0}\) such that \(d(x_{1}, Tx_{0}) = d(A, B)\) and \(x_{0} \preceq x_{1}\). Then T has a best proximity point in \(A_{0}\).
In the following, our aim is to prove the existence and uniqueness of the best proximity point in Theorem 2.1.
Theorem 2.2
In addition to the hypotheses of Theorem 2.1, suppose that for every \(x, y \in A_{0}\) there exists \(u \in A_{0}\) such that u is comparable to x and y. Then T has a unique best proximity point.
Proof
By the assumption, there exists \(u \in A_{0}\) such that u is comparable with x and y.
Example 2.1
Assume the complete metric space \((X = {\mathbb{R}}^{2}, d)\), where the metric d is defined as \(d(x, y) =  x_{1}  x_{2}+  y_{1}  y_{2}\), for \(x = (x_{1}, y_{1}), y = (y_{1}, y_{2})\in X\). We define a partial order ⪯ on X such that \((x, y) \preceq(u, v)\) if and only if \(x \leq u\) and \(y \leq v\), for all \((x, y), (u, v) \in X\).
Let \(A = \{(x, 1) : 0 \leq x \leq1\}\), \(B = \{(x,  1) : 0 \leq x \leq1\}\), \(A_{0} = \{(\frac{x}{2}, 1) : 0 \leq x \leq1\}\), and \(B_{0} = B\). Consider \(T \colon A \rightarrow B\), \(T(x, 1) = (\frac{x}{2}, 1 )\). It is clear that \(d(A, B) = 2\), \(A_{0} \subseteq A\), and \(T(A_{0}) \subseteq B_{0}\). Also \((A, B)\) satisfies Pproperty.
Now we show that T is proximally increasing on \(A_{0}\). In this respect, let \((\frac{x}{2}, 1), (\frac{u}{2}, 1), (\frac{y}{2}, 1), (\frac{v}{2}, 1) \in A_{0}\) with \((\frac{y}{2}, 1) \preceq(\frac{v}{2}, 1)\).
We see that \((\frac{y}{2}, 1) \preceq(\frac{v}{2}, 1)\) implies \(\frac{y}{2} \leq\frac{v}{2}\); that is, \(\frac{y}{4} \leq \frac{v}{4}\); that is, \(x \leq u\). Now \(x \leq u\) implies \((\frac{x}{2}, 1) \preceq(\frac{u}{2}, 1)\). Hence T is proximally increasing on \(A_{0}\).
Here all of the conditions of Theorems 2.1 and 2.2 are satisfied and it is seen that \(x^{*} = (0, 1)\) in A is the unique best proximity point of T in \(A_{0}\).
3 Applications to coupled best proximity point results
In this section we make an application of the results of Section 2 to obtain new coupled proximity point results. The results are obtained through the construction of a product space to which we apply our theorem.

\(d_{1}(A^{*}, B^{*}) = d_{1}(A \times A, B \times B) = d(A, B) + d(A, B)\).

Let \(x =(x_{1}, y_{1}) \in A^{*}\), \(y = (x_{2}, y_{2}) \in B^{*}\) such that \(d_{1}(x, y) = d_{1}(A^{*}, B^{*})\). Then \(d(x_{1}, x_{2}) = d(y_{1}, y_{2}) = d(A, B)\).
Proof
\(d_{1}(x, y) = d_{1}(A^{*}, B^{*})\) implies \(d(x_{1}, x_{2}) + d(y_{1}, y_{2}) = d(A, B) + d(A, B)\). Since \(d(x_{1}, x_{2}) \geq d(A, B)\) and \(d(y_{1}, y_{2}) \geq d(A, B)\), it follows \(d(x_{1}, x_{2}) = d(y_{1}, y_{2}) = d(A, B)\). □

and$$ A_{0}^{*} = \bigl\{ x = (x_{1}, y_{1}) \in A^{*} : d_{1}(x, y)= d_{1} \bigl(A^{*}, B^{*}\bigr) \text{ for some } y = (x_{2}, y_{2}) \in B^{*}\bigr\} $$It is to be noted that for every \(x\in A_{0}^{*}\) there exists \(y\in B_{0}^{*}\) such that \(d_{1}(x, y) = d_{1}(A^{*}, B^{*})\) and, conversely, for every \(y\in B_{0}^{*}\) there exists \(x\in A_{0}^{*}\) such that \(d_{1}(x, y) = d_{1}(A^{*}, B^{*})\).$$ B_{0}^{*} = \bigl\{ y = (x_{2}, y_{2})\in B^{*} : d_{1}(x, y) = d_{1}\bigl(A^{*}, B^{*}\bigr) \text{ for some } x = (x_{1}, y_{1}) \in A^{*}\bigr\} . $$
Lemma 3.1
If the pair \((A, B)\) has Pproperty, then the pair \((A^{*}, B^{*})\) has also the Pproperty.
Proof
Lemma 3.2
Let \(F \colon A \times A \rightarrow B\) be a mapping with \(F(A_{0} \times A_{0})\subseteq B_{0}\). If F has the proximal mixed monotone property on \(A_{0} \times A_{0}\), then the mapping \(T \colon A^{*} \rightarrow B^{*}\) is proximally increasing on \(A_{0}^{*}\).
Proof
Theorem 3.1
 (a)
F is continuous or
 (b)X has the following properties:
 (i)
if a nondecreasing sequence \(\{x_{n}\}\rightarrow x\), then \(x_{n} \preceq x\), for all \(n\geq0\);
 (ii)
if a nonincreasing sequence \(\{y_{n}\}\rightarrow y\), then \(y \preceq y_{n}\), for all \(n \geq0\).
 (i)
Also, suppose that there exist \((x_{0}, y_{0}), (x_{1}, y_{1}) \in A_{0} \times A_{0}\) such that \(d(x_{1}, F(x_{0}, y_{0}))= d(A, B)\) and \(d(y_{1}, F(y_{0}, x_{0}))= d(A, B)\) with \((x_{0}, y_{0}) \preceq(x_{1}, y_{1})\). Then F has a coupled best proximity point in \(A_{0} \times A_{0}\); that is, there exists an element \((x^{*}, y^{*}) \in A_{0} \times A_{0}\) such that \(d(x^{*}, F(x^{*}, y^{*})) = d(A, B)\) and \(d(y^{*}, F(y^{*}, x^{*})) = d(A, B)\).
Proof

\((Y, d_{1})\) is a complete metric space,

\((A^{*}, B^{*})\) is a pair of nonempty closed subsets of Y such that \(A^{*}_{0}\) is nonempty closed and \((A^{*}, B^{*})\) satisfies the Pproperty,

\(T(A^{*}_{0}) \subseteq B^{*}_{0}\),

T is proximally increasing on \(A^{*}_{0}\),

T is continuous,

the condition (b) implies that if \(\{x_{n}\}\) is a nondecreasing sequence in Y such that \(x_{n} \rightarrow x\), then \(x_{n} \preceq x\) for all \(n \geq0\),

let \(p = (x, y), q = (u, v) \in A_{0} \times A_{0}\) such that \(p \succeq q\); then (3.1) reduces to$$ \psi\bigl(d_{1}(Tp, Tq)\bigr)\leq\varphi\bigl(d_{1}(p, q) \bigr)  \theta\bigl(d_{1}(p, q)\bigr), $$

now, the existence of \((x_{0}, y_{0}), (x_{1}, y_{1}) \in A_{0} \times A_{0}\) such that \(d(x_{1}, F(x_{0}, y_{0}))= d(A, B)\) and \(d(y_{1}, F(y_{0}, x_{0}))= d(A, B)\) with \((x_{0}, y_{0}) \preceq(x_{1}, y_{1}) \) implies the existence of points \(p_{0} = (x_{0}, y_{0}), p_{1} = (x_{1}, y_{1})\in A^{*}_{0}\) such that \(d_{1}(p_{1}, Tp_{0})= d_{1}(A^{*}, B^{*})\) with \(p_{0} \preceq p_{1}\).
Therefore, the theorem reduces to Theorem 2.1 and hence T has a best proximity point in \(A^{*}_{0}\); that is, there exists an element \(w^{*} =(x^{*}, y^{*})\in A^{*}_{0}\) such that \(d_{1}(w^{*}, Tw^{*}) = d_{1}(A^{*}, B^{*})\); that is, \(d_{1}((x^{*}, y^{*}), T(x^{*}, y^{*})) = d_{1}(A \times A, B \times B)\); that is, \(d(x^{*}, F(x^{*}, y^{*})) + d(y^{*}, F(y^{*}, x^{*})) = d(A, B) + d(A, B)\), which implies that \(d(x^{*}, F(x^{*}, y^{*})) = d(A, B)\) and \(d(y^{*}, F(y^{*}, x^{*})) = d(A, B)\); that is, \((x^{*}, y^{*})\in A_{0} \times A_{0}\) is coupled best proximity point of F. □
The following theorem gives the uniqueness of the result in Theorem 3.1 under certain conditions.
Theorem 3.2
In addition to the hypotheses of Theorem 3.1, suppose that for every \((x, y), (x^{*}, y^{*}) \in A_{0} \times A_{0}\) there exists a \((u, v) \in A_{0} \times A_{0}\) such that \((u, v)\) is comparable to \((x, y)\) and \((x^{*}, y^{*})\). Then F has a unique coupled best proximity point.
Note that several coupled proximity point results can be obtained corresponding to Corollaries 2.12.3 if we assume the particular forms of the control functions used therein.
Declarations
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Authors’ Affiliations
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