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Best proximity point theorems for reckoning optimal approximate solutions
Fixed Point Theory and Applications volume 2012, Article number: 202 (2012)
Abstract
Given a nonself mapping from A to B, where A and B are subsets of a metric space, in order to compute an optimal approximate solution of the equation Sx=x, a best proximity point theorem probes into the global minimization of the error function x\u27f6d(x,Sx) corresponding to approximate solutions of the equation Sx=x. This paper presents a best proximity point theorem for generalized contractions, thereby furnishing optimal approximate solutions, called best proximity points, to some nonlinear equations. Also, an iterative algorithm is presented to compute such optimal approximate solutions.
MSC:90C26, 90C30, 41A65, 46B20, 47H10, 54H25.
1 Introduction
Best proximity point theory involves an intertwining of approximation and global optimization. Indeed, it explores the existence and computation of an optimal approximate solution of nonlinear equations of the form Sx=x, where S is a nonself mapping in some framework. Such equations are confronted when we attempt the mathematical formulation of several problems. Given a nonself mapping S:A\u27f6B, where A and B are nonempty subsets of a metric space, the equation Sx=x does not necessarily have a solution because of the fact that a solution of the preceding equation constrains the equality between an element in the domain and an element in the range of the mapping. In such circumstances, one raises the following questions:

Is it possible to find an optimal approximate solution with the least possible error?

If an approximate solution exists, is there any iterative algorithm to compute such a solution?

Can one have more than one approximate solution with the least possible error?
Best proximity point theory is an outgrowth of attempts in many directions to answer previously posed questions for various families of nonself mappings. In fact, a best proximity point theorem furnishes sufficient conditions for the existence and computation of an approximate solution {x}^{\ast} that is optimal in the sense that the error d({x}^{\ast},S{x}^{\ast}) assumes the global minimum value d(A,B). Such an optimal approximate solution is known as a best proximity point of the mapping S. It is straightforward to observe that a best proximity point becomes a solution of the equation in the special case that the domain of the mapping intersects the codomain of the mapping. In essence, a best proximity point theorem delves into the global minimization of the error function x\u27f6d(x,Sx) corresponding to the approximate solutions of the equation Sx=x. Many interesting best proximity point theorems for various classes of nonself mappings in different frameworks and best approximation theorems have been elicited in [1–17] and [18–40]. The main objective of this article is to present, in the framework of complete metric spaces, a best proximity point theorem for a new family of nonself mappings, known as generalized contractions, thereby computing an optimal approximate solution to the equation Sx=x, where S is a generalized contraction. Further, some results in the literature are realizable as special cases from the preceding result.
2 Preliminaries
Throughout this section, we assume that A and B are nonempty subsets of a metric space. We recall the following notions that will be used in the sequel.
Definition 2.1 ([41])
A mapping S:A\u27f6B is called a generalized contraction if, given real numbers a and b with 0<a\le b, there exists a real number \alpha (a,b)\in [0,1) such that
for all {x}_{1}, {x}_{2} in A.
It is apparent that every generalized contraction is a contractive mapping and hence it must be continuous.
Definition 2.2 An element {x}^{\ast} in A is called a best proximity point of a mapping S:A\u27f6B if it satisfies the condition that
Due to the fact that d(x,Sx)\ge d(A,B) for all x in A, the global minimum of the error function x\u27f6d(x,Sx) corresponding to approximate solutions of the equation Sx=x is attained at any best proximity point. Moreover, if the mapping under consideration is a selfmapping, a best proximity point reduces to a fixed point.
Definition 2.3 ([21])
Given mappings S:A\u27f6B and T:B\u27f6A, it is stated that the pair (S,T) satisfies the minmax condition if, for all x\in A and y\in B, we have
where min(Sx,Ty) and max(Sx,Ty) are defined as
It is quite easy to observe that the minmax condition is less restrictive so that several classes of pairs of mappings meet this requirement.
Definition 2.4 ([21])
Given nonself mappings S:A\u27f6B and T:B\u27f6A, the pair (S,T) is said to be

(a)
a cyclic inequality pair if d(A,B)<d(x,y)\u27f9d(Sx,Ty)\ne d(x,y)

(b)
a cyclic contractive pair if d(A,B)<d(x,y)\u27f9d(Sx,Ty)<d(x,y)

(c)
a cyclic expansive pair if d(A,B)<d(x,y)\u27f9d(Sx,Ty)>d(x,y)
for all x\in A and y\in B.
It is remarked that cyclic inequality pairs, cyclic contractive pairs, and cyclic expansive pairs satisfy the minmax condition.
Definition 2.5 Given nonself mappings S:A\u27f6B and T:B\u27f6A, the pair (S,T) is said to form a generalized cyclic contraction if, given real numbers a and b with 0<a\le b, there exists a real number \alpha (a,b)\in [0,1) such that
for all x in A and y in B.
It is straightforward to see that every generalized cyclic contraction forms a cyclic contractive pair and hence satisfies the minmax condition.
3 Generalized contractions
We are now ready to establish the following interesting best proximity point theorem for nonself generalized contractions.
Theorem 3.1 Let A and B be nonempty, closed subsets of a complete metric space. Let S:A\u27f6B and T:B\u27f6A satisfy the following conditions.

(a)
S is a generalized contraction.

(b)
T is a nonexpansive mapping.

(c)
The pair (S,T) satisfies the minmax condition.
Further, for a fixed element {x}_{0} in A, let
Then the sequence \{{x}_{2n}\} must converge to a best proximity point {x}^{\ast} of S and the sequence \{{x}_{2n+1}\} must converge to a best proximity point {y}^{\ast} of T such that
Further, if S has two distinct best proximity points, then d(A,B) does not vanish and hence the sets A and B should be disjoint.
Proof We define a sequence \{{b}_{n}\} of real numbers as follows:
In light of the fact that S is a generalized contraction mapping and T is a nonexpansive mapping, it follows that \{{b}_{n}\} is a bounded below, decreasing sequence of nonnegative real numbers and hence converges to some nonnegative real number, say b. Next, we shall prove that b vanishes. If b is positive, then choose a positive integer N such that
Because S is a generalized contraction mapping and T is a nonexpansive mapping, we have
Similarly, we can prove that
In general, we shall obtain that
Letting k\u27f6\mathrm{\infty}, we deduce that b vanishes, which is incompatible with our assumption. Therefore, it can be concluded that {b}_{n}\u27f60 as n\u27f6\mathrm{\infty}.
Next, we shall prove that \{{x}_{2n}\} is a Cauchy sequence. Let \u03f5>0 be given. Since {b}_{n}\u27f60, it is possible to choose a positive integer N such that
We define
It suffices to prove that if x is an element of B[{x}_{2N},\u03f5], then TSx must also be an element of B[{x}_{2N},\u03f5]. We shall consider two different cases to ascertain the preceding assertion. Let x be an element of B[{x}_{2N},\u03f5]. If x satisfies the condition that
then it follows that
On the other hand, if x satisfies the condition that
then it follows that
Therefore, TSx should be an element of B[{x}_{2N},\u03f5]. Consequently, {x}_{2n}\in B[{x}_{2N},\u03f5] for all n\ge N. Thus, \{{x}_{2n}\} should be a Cauchy sequence. In view of the completeness of the space, the sequence \{{x}_{2n}\} should converge to some element {x}^{\ast} in A. In light of the continuity of S, it results that the sequence \{{x}_{2n+1}\} should converge to some element {y}^{\ast} in B and {y}^{\ast}=S{x}^{\ast}. Further, because of the continuity of T, \{{x}_{2n}\} should converge to T{y}^{\ast}. So, T{y}^{\ast} and {x}^{\ast} should be identical. Also, we can deduce that TS{x}^{\ast}={x}^{\ast} and ST{y}^{\ast}={y}^{\ast}. As a result, we have
On account of the fact that the pair (S,T) satisfies the minmax condition, we obtain that
Thus, we can deduce that
We shall assume that the nonself mapping S has two distinct best proximity points {x}^{\prime} and {x}^{\u2033}. Then it follows that
Thus, d(A,B)>0 and hence the sets A and B are disjoint. This completes the proof of the theorem. □
We illustrate the preceding best proximity point theorem by means of the following example.
Example 3.2 We shall consider the complete metric space C[0,2] with the supremum metric. For n=\pm 1,\pm 2,\pm 3,\dots , define {f}_{n}:[0,2]\u27f6\mathbf{R} as
Let {f}_{0}:[0,2]\u27f6\mathbf{R} be defined as
Let A:=\{{f}_{n}:n=0,\pm 1,\pm 2,\pm 3,\dots \} and B:=\{f:f\in A\}.
Let S:A\u27f6B be defined as
Let T:B\u27f6A be defined as
Then, it is easy to see that S is a generalized contraction and T is a nonexpansive mapping. Also, the pair (S,T) satisfies the minmax condition. However, (S,T) is neither a cyclic contractive pair nor a cyclic expansive pair. Finally, we can note that the element {x}^{\ast}={f}_{0} in A is a best proximity point of the mapping S and the element {y}^{\ast}={f}_{0} in B is a best proximity point of the mapping T such that d({x}^{\ast},{y}^{\ast})=d(A,B)=2.
One can easily see that best proximity point Theorem 3.1 subsumes the following result.
Corollary 3.3 Let A and B be nonempty, closed subsets of a complete metric space. Let the nonself mappings S:A\u27f6B and T:B\u27f6A satisfy the following conditions.

(a)
S is a generalized contraction.

(b)
T is a nonexpansive mapping.

(c)
(S,T) is a generalized cyclic contraction.
Further, for a fixed element {x}_{0} in A, let
Then the sequence \{{x}_{2n}\} converges to a best proximity point {x}^{\ast} of S and the sequence \{{x}_{2n+1}\} converges to a best proximity point {y}^{\ast} of T such that
Moreover, if S has two distinct best proximity points, then d(A,B)>0 and hence the sets A and B must be disjoint.
Best proximity point Theorem 3.1 subsumes the following fixed point theorem, due to Krasnoselskii [41], which in turn extends the most interesting and wellknown contraction principle.
Corollary 3.4 Let X be a complete metric space. If the selfmapping T:X\u27f6X is a generalized contraction, then it has a unique fixed point {x}^{\ast}, and for every x in X, the sequence \{{T}^{n}(x)\} converges to {x}^{\ast}.
The following best proximity point theorem, due to Basha [19], which extends the contraction principle to the case of nonself mappings, is a special case of Theorem 3.1.
Corollary 3.5 Let A and B be nonempty, closed subsets of a complete metric space. Let S:A\u27f6B and T:B\u27f6A be nonself mappings satisfying the following conditions.

(a)
S is a contraction.

(b)
T is a nonexpansive mapping.

(c)
(S,T) is a cyclic contractive pair.
Further, for a fixed element {x}_{0} in A, let
Then the sequence \{{x}_{2n}\} converges to a best proximity point {x}^{\ast} of S and the sequence \{{x}_{2n+1}\} converges to a best proximity point {y}^{\ast} of T such that
Moreover, if S has two distinct best proximity points, then d(A,B)>0 and hence the sets A and B must be disjoint.
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Basha, S.S., Shahzad, N. & Jeyaraj, R. Best proximity point theorems for reckoning optimal approximate solutions. Fixed Point Theory Appl 2012, 202 (2012). https://doi.org/10.1186/168718122012202
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DOI: https://doi.org/10.1186/168718122012202
Keywords
 global optimization
 optimal approximate solution
 best proximity point
 fixed point
 contraction
 generalized contraction
 nonexpansive mapping