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Noncyclic contractions and relatively nonexpansive mappings in strictly convex fuzzy metric spaces

  • A concept of fuzzy projection operator is introduced and use to investigate the non-emptiness of the fuzzy proximal pairs. We then consider the classes of noncyclic contractions and noncyclic relatively nonexpansive mappings and survey the existence of best proximity pairs for such mappings. In the case that the considered mapping is noncyclic relatively nonexpansive, we need a geometric notion of fuzzy proximal normal structure defined on a nonempty and convex pair in a convex fuzzy metric space. We also prove that every nonempty, compact and convex pair of subsets of a strictly convex fuzzy metric space has the fuzzy proximal normal structure.

    Citation: Moosa Gabeleh, Elif Uyanık Ekici, Manuel De La Sen. Noncyclic contractions and relatively nonexpansive mappings in strictly convex fuzzy metric spaces[J]. AIMS Mathematics, 2022, 7(11): 20230-20246. doi: 10.3934/math.20221107

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  • A concept of fuzzy projection operator is introduced and use to investigate the non-emptiness of the fuzzy proximal pairs. We then consider the classes of noncyclic contractions and noncyclic relatively nonexpansive mappings and survey the existence of best proximity pairs for such mappings. In the case that the considered mapping is noncyclic relatively nonexpansive, we need a geometric notion of fuzzy proximal normal structure defined on a nonempty and convex pair in a convex fuzzy metric space. We also prove that every nonempty, compact and convex pair of subsets of a strictly convex fuzzy metric space has the fuzzy proximal normal structure.



    The notion of normal structure was introduced by Brodskil and Milman [1] in 1948 in order to study the existence of common fixed points of certain sets of isometries. Later, the notion of normal structure was generalized for the weak topology.

    Definition 1.1. A Banach space X is said to have normal structure (NS) (res., weak normal structure (WNS)) if for every bounded, closed (res., weakly compact) and convex subset K of X such that diam(K):=sup{xy:x,yK}>0, there is a point pK which is not a diametral point, that is, sup{px:xK}<diam(K).

    It is well-known that compact and convex subset of a Banach space X has normal structure. In 1965, Kirk proved the following important celebrated result.

    Theorem 1.2. (Kirk's fixed point theorem [2]) Let A be a nonempty, bounded, closed (res., weakly compact) and convex subset of a Banach space X and T:AA be a nonexpansive self-mapping, that is,

    TxTyxy,x,yA.

    If X is a reflexive Banach space with NS (res., a Banach space with WNS), then T has a fixed point.

    There are many interesting extensions of Kirk's fixed point theorem. One of them is due to Je˘siˊc, where he generalized Kirk's fixed point theorem to fuzzy metric spaces [3]. To present the main theorem of [3], we recall some related concepts as below.

    In 1965, Zadeh [4] introduced the notion of fuzzy set. After this pioneering work, the concept of a fuzzy metric space which is closely related to the class of probabilistic metric spaces was introduced by Kramosil and Michalek in 1975 [5]. George and Veeramani [6] modified the notion of fuzzy metric space given in [5] with the help of continuous t-norm and described a Hausdorff topology on the modified fuzzy metric space (see also [7,8] for more general discussions).

    In [3], Je˘siˊc defined strict convexity and normal structure in fuzzy metric space and proved a fixed point theorem for a nonexpansive self-mapping on a strictly convex fuzzy metric space. In this work, we study the existence of best proximity pairs by considering noncyclic contractions defined on a union of two nonempty subset of a strictly convex fuzzy metric space. We also consider the noncyclic relatively nonexpansive mappings and obtain a best proximity pair theorem which is a real extension of the aforesaid theorem in [3]. In this way, we provide an important basis for the existence of the best proximity pairs in the setting of strictly convex fuzzy metric spaces. To this end, we need the following definitions and notions.

    Definition 1.3. (Schweizer and Sklar [9]) A binary operation :[0,1]×[0,1][0,1] is a continuous triangular norm (t-norm) if satisfies the following conditions:

    (i) is commutative and associative;

    (ii) is continuous;

    (iii) a1=a for all a[0,1];

    (iv) abcd whenever ac and bd, and a,b,c,d[0,1].

    Some of continuous t-norms are as below:

    ab=min{a,b},ab=ab,ab={min{a,b},ifmax{a,b}=1,0,otherwise.

    Definition 1.4. (George and Veeramani [6]) A 3-tuple (X,M,) is said to be an fuzzy metric space if X is an arbitrary set, is a continuous t-norm, M is a fuzzy set on X2×(0,) satisfying the following conditions: For all x,y,zX,s,t>0,

    (a) M(x,y,t)>0;

    (b) M(x,y,t)=1 if and only if x=y;

    (c) M(x,y,t)=M(y,x,t);

    (d) M(x,z,t+s)M(x,y,t)M(y,z,s);

    (e) M(x,y,):(0,)(0,1] is continuous.

    It is worth noticing that if (X,d) is a metric space and we define ab=ab for any a,b[0,1] and M(x,y,t)=tt+d(x,y) for all x,yX and t>0, then (X,M,) is a fuzzy metric space (see [6] for more information).

    Remark 1.5. (Mariusz [10]) In a fuzzy metric space (X,M,), the function M(x,y,) is non-decreasing for all x,yX.

    Remark 1.6 ([6]) A sequence {xn} in a fuzzy metric space (X,M,) is said to be convergent to x in X if and only if limnM(xn,x,t)=1 for all t>0.

    The next lemma will be used in our coming discussions.

    Lemma 1.7. ([11]) If (X,M,) is a fuzzy metric space and limnxn=x, limnyn=y, then

    limnM(xn,yn,t)=M(x,y,t).

    Definition 1.8. ([6]) Let (X,M,) be a fuzzy metric space and r(0,1), t>0 and xX. The set

    B(x,r,t)={yX:M(x,y,t)>1r},

    is called an open ball with center x and radius r with respect to t.

    The closed ball with center x and radius r with respect to t is given by

    B[x,r,t]={yX:M(x,y,t)1r}.

    It was announced in [6] that every fuzzy metric space (X,M,) generates a Hausdorff first countable topology, where its basis is the family of {B(x,r,t):xX,r(0,1),t>0}.

    Definition 1.9. ([11]) Let (X,M,) be a fuzzy metric space and A be a nonempty subset of X. The mappings δA(t):(0,)[0,1] is defined as

    δA(t):=infx,yAsupε<tM(x,y,ε).

    The constant δA=supt>0δA(t) is called fuzzy diameter of nearness of the set A.

    Lemma 1.10. ([12]) Let (X,M,) be a fuzzy metric space. A subset A of X is said to be fuzzy bounded (F-bounded) if there exist t>0 and r(0,1) such that M(x,y,t)>1r for all x,yA.

    Proposition 1.11. ([6]) Every compact subset of a fuzzy metric space is F-bounded.

    Definition 1.12. ([3]) Let A be a nonempty subset of a fuzzy metric space (X,M,). A point pA is called a diametral point if

    infyAsupε<tM(p,y,ε)=δA(t),t>0.

    Therefore, a point uA is nondiametral whenever there exists t0>0 such that

    infyAsupε<t0M(u,y,ε)>δA(t0).

    In 1970, Takahashi introduced a convex structure on metric spaces [13]. It was generalized by S.N. Je˘siˊc to fuzzy metric spaces as follows.

    Definition 1.13. ([3]) A fuzzy metric space (X,M,) possesses a convex structure if there exists a function W:X×X×[0,1]X, satisfying W(x,y,0)=y, W(x,y,1)=x and for all x,y,zX, θ(0,1) and t>0

    M(W(x,y,θ),z,2t)M(x,z,tθ)M(y,z,t1θ).

    Throughout this article (X,M,;W) stand to denote a fuzzy metric space equipped with a convex structure W:X×X×[0,1]X and we call it a convex fuzzy metric space.

    Definition 1.14. A subset K of a convex fuzzy metric space (X,M,;W) is said to be a convex set if for every x,yK and θ[0,1] it follows that W(x,y,θ)K.

    Lemma 1.15. ([3]) Let (X,M,;W) be a convex fuzzy metric space and {Kα}αΛ be a family of convex subsets of X. Then the intersection K=αΛKα is a convex set.

    Definition 1.16. A convex fuzzy metric space (X,M,;W) is said to have property  (C) if every decreasing net consists of nonempty, F-bounded, closed and convex subsets of X has a nonempty intersection.

    For instance if a convex fuzzy metric space (X,M,;W) is compact, then it has the property  (C). Furthermore, if X is a reflexive Banach space, ab=min{a,b} and for any x,yX and t>0,θ(0,1),

    M(x,y,t)=tt+xy,W(x,y,θ)=θx+(1θ)y,

    then from the Eberlein-Šmulian's theorem (X,M,;W) is a convex fuzzy metric space having property  (C).

    Definition 1.17. ([3]) A convex fuzzy metric space (X,M,;W) is said to be strictly convex provided that for every x,yX, and θ(0,1) there exists a unique element z=W(x,y,θ)X for which

    M(x,y,tθ)=M(z,y,t),M(x,y,t1θ)=M(x,z,t),

    for all t>0.

    We will use the following useful lemmas in our coming discussions.

    Lemma 1.18. Let (X,M,;W) be a convex fuzzy metric space. Suppose that for every θ(0,1),t>0 and x,y,zX the following condition holds

    M(W(x,y,θ),z,t)>min{M(z,x,t),M(z,y,t)},()

    If there exists uX for which

    M(W(x,y,θ),u,t)=min{M(u,x,t),M(u,y,t)},

    for all t>0, then W(x,y,θ){x,y}.

    Proof. Since for any t>0 we have

    M(W(x,y,θ),u,t)=min{M(u,x,t),M(u,y,t)}

    for some uX, by using the condition () we must have θ=0 or θ=1 which ensures that W(x,y,0)=y or W(x,y,1)=x and this completes the proof.

    Lemma 1.19. ([3]) Let (X,M,;W) be a strictly convex fuzzy metric space. Then for any x,yX with xy there exists θ(0,1) such that W(x,y,θ){x,y}.

    Lemma 1.20. ([3]) Let (X,M,;W) be a fuzzy metric space which satisfies the condition (). Then the closed balls B[x,r,t] are convex sets.

    The fuzzy version of the notion of normal structure was introduced in [3] as below.

    Definition 1.21. A convex fuzzy metric space (X,M,;W) is said to have fuzzy normal structure if for every closed, F-bounded and convex subset K of X which consists of at least two different points, there exists a point pK which is a non-diametral point.

    Definition 1.22. A self-mapping f defined on a fuzzy metric space (X,M,) is said to be nonexpansive provided that

    M(fx,fy,t)M(x,y,t),x,yX, t>0.

    Example 1.23. For any x,yN and t>0, let

    M(x,y,t)={min{x,y}max{x,y},t>0, xy1,t>0, x=y,

    and define ab=ab. Then (N,M,) is a fuzzy metric space. Define a function f:NN with f(x)=x+1. Then f is nonexpansive:

    If x=y, then M(fx,fy,t)=1=M(x,y,t), for all t>0.

    If xy and x<y, then

    M(fx,fy,t)=x+1y+1>xy=M(x,y,t).

    Therefore, M(fx,fy,t)M(x,y,t) for all x,yN,t>0.

    The next theorem is a main result of ([3]).

    Theorem 1.24. ([3]) Let (X,M,;W) be a strictly convex fuzzy metric space which satisfies the condition (). Assume that K is a nonempty, compact and convex subset of X and f:KK is a nonexpansive self-mapping. Then f has at least one fixed point in K.

    It is remarkable to note that the proof of Theorem 1.24 is based on the fact that every nonempty, compact and convex subset of a strictly convex fuzzy metric space X satisfying the condition () has the fuzzy normal structure.

    The main purpose of this article is to extend Theorem 1.24 from nonexpansive self-mappings to noncyclic relatively nonexpansive mappings in order to study the existence of best proximity pairs.

    This article is organized as follows: In Section 2, we define the fuzzy projection operators and survey the nonemptiness of fuzzy proximal pairs under some sufficient conditions. In Section 3, we consider the class of noncyclic contractions defined on a union of two nonempty subset of a fuzzy metric space and study the existence of best proximity pairs for such mappings. Finally, in Section 4, a concept of fuzzy proximal normal structure is introduced and used to investigate a best proximity pair theorem for noncyclic relatively nonexpansive mappings which is a real extension of Theorem 1.24. We also show that every nonempty, compact and convex pair of subsets of a strictly convex metric space which satisfies the condition () has the fuzzy proximal normal structure.

    Let A and B be two nonempty subsets of a convex fuzzy metric space (X,M,;W). We shall say that a pair (A,B) in X satisfies a property if both A and B satisfy that property. For instance, (A,B) is convex if and only if both A and B are convex; (A,B)(C,D)AC, and BD.

    We shall also adopt the following notations:

    Δ(x,B)(t):=infyBsupε<tM(x,y,ε),xX, t>0,Δ(A,B)(t):=inf(x,y)A×Bsupε<tM(x,y,ε),t>0,Δ(A,B):=supt>0Δ(A,B)(t).

    The closed and convex hull of a set A will be denoted by ¯con(A) and defined as below

    ¯con(A):={C:C is a closed and convex subset of X such that CA}.

    The F-distance between A and B is defined by

    ϱAB(t):=sup(x,y)A×Bsupϵ<tM(x,y,ϵ),t>0.

    Moreover, the F-distance between an element xX and the set B will be denoted by ϱxB(t) for all t>0. A point (x,y)A×B is called F-proximal provided that

    M(x,y,t)=ϱAB(t),t>0.

    The F-proximal pair of (A,B) is denoted by (A0,B0) which is defined as follows:

    A0:={xA: M(x,y,t)=ϱAB(t), for all t>0,  for some yB},
    B0:={yB: M(x,y,t)=ϱAB(t), for all t>0,  for some xA}.

    It is remarkable to note that the F-proximal pairs may be empty. Next example illustrates this fact.

    Example 2.1. Consider the Banach space X=p, (1p<) with the canonical basis {en}. Let k(0,1) be fixed and suppose A={((1+k2n)e2n):nN} and B={((1+k2n+1)e2n+1):nN}. Assume that M(x,y,t)=tt+xyp for all (x,y)A×B and t>0 and let ab=ab for any a,b[0,1]. Then (X,M,) is a fuzzy metric space and the sets A and B are bounded, closed and we have

    ϱAB(t)=sup(x,y)A×Bsupε<t(εε+xyp)=supε<t(εε+(2)1/p).

    In view of the fact that for any (x,y)A×B and t>0 we have M(x,y,t)<ϱAB(t), then A0=B0=.

    Definition 2.2. Let (X,M,;W) be a convex fuzzy metric space and E be a nonempty subset of X. The fuzzy projection operator (briefly F-projection operator) PE:X2E is defined as

    PE(x):={yE:M(x,y,t)=ϱx,E(t), t>0},

    where 2E denotes the set of all subsets of E.

    Proposition 2.3. Let E be a nonempty, F-bounded, closed and convex subset of a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition (). If X has the property  (C), then the F-projection operator PE is single-valued.

    Proof. Consider an arbitrary element xX and let r(0,1) be fixed. Define

    Pr={yE:M(x,y,t)ϱx,E(t)(1r), t>0}.

    If y1,y2Pr and θ(0,1), then by the condition () for all t>0 we have

    M(x,W(y1,y2,θ),t)>min{M(x,y1,t),M(x,y2,t)}ϱx,E(t)(1r),

    which deduces that W(y1,y2,θ)Pr, that is, Pr is convex. We also note that if {yk}k1 is a sequence in Pr which converges to an element yX, then from Lemma 1.7 we obtain

    M(x,y,t)=limkM(x,yk,t)ϱx,E(t)(1r),t>0,

    which ensures that yPr, that is, Pr is closed. Thus {Pr}r is a descending chain of nonempty, F-bounded, closed and convex subsets of X. Since X has the property  (C), r>0Pr is nonempty. Let pr>0Pr. Then

    M(x,p,t)ϱx,E(t)(1r),r(0,1).

    Now, if r1, we obtain M(x,p,t)=ϱx,E(t) and so pPE(x). On the other hand, if pX is another member of PE(x), then from the strict convexity of X there exists θ0(0,1) such that EW(p,p,θ0){p,p}. Using the condition () for any t>0 we obtain

    M(x,W(p,p,θ0),t)>min{M(x,p,t),M(x,p,t)}=ϱx,E(t),

    which is a contradiction.

    In what follows we present some sufficient conditions which guarantees the nonemptiness of F-proximal pairs.

    Lemma 2.4. Let (A,B) be a nonempty, F-bounded, closed and convex pair in a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition (). If X has the property  (C), then (A0,B0) is also nonempty, F-bounded, closed and convex. Furthermore, ϱAB(t)=ϱA0B0(t) for all t>0.

    Proof. Let r(0,1) be fixed and put

    Er={xA:ϱx,B(t)ϱA,B(t)(1r), t>0}.

    By the fact that ϱA,B(t)=supuAϱu,B(t) for any t>0, the set Er is nonempty. Suppose {xn} is a sequence in Er such that xnxX. Thus ϱxn,B(t)ϱA,B(t)(1r) for all nN. So there exists an element yB such that M(xn,y,t)ϱA,B(t)(1r) for all nN. Using Lemma 1.7, we obtain

    ϱx,B(t)M(x,y,t)=limnM(xn,y,t)ϱA,B(t)(1r),

    which concludes that xEr, that is, Er is closed. Moreover, if u1,u2Er and θ(0,1), then by the condition () for any yB we have

    M(W(u1,u2,θ),y,t)>min{M(u1,y,t),M(u2,y,t)}.

    Taking supremum over all yB in above relation, we deduce that

    ϱW(u1,u2,θ),B(t)min{ϱu1,B(t),ϱu2,B(t)}ϱA,B(t)(1r),

    which implies that Er is convex. Since A is F-bounded, Er is F-bounded too. Thereby {Er}r>0 is a decreasing net consists of nonempty, F-bounded, closed and convex subsets of X. Since X has the property  (C), r>0Er is nonempty. Using Proposition 2.3 we obtain A0=r>0Er, which implies that A0 is a nonempty, closed and convex subset of X. By a similar argument we can see that B0 is a nonempty, closed and convex subsets of X.

    Definition 2.5. A pair (A,B) in a convex fuzzy metric space (X,M,;W) is said to be F-proximinal if A0=A and B0=B.

    Definition 2.6. Let (A,B) be a nonempty pair in a convex fuzzy metric space (X,M,;W). A mapping T:ABAB is said to be noncyclic if T(A)A and T(B)B. Also, a point (p,q)A×B is said to be a best proximity pair for the noncyclic mapping T whenever

    Tp=p,Tq=q,M(p,q,t)=ϱAB(t), t>0.

    The set of all best proximity pairs of the noncyclic mapping T is denoted by BestA×B(T).

    We begin our main result of this section by introducing the following class of noncyclic mappings.

    Definition 3.1. Let (A,B) be a nonempty pair in a convex fuzzy metric space (X,M,;W). A mapping T:ABAB is said to be a

    ● noncyclic contraction mapping if T is noncyclic and there exists λ(0,1) such that for all (x,y)A×B and t>0

    M(Tx,Ty,t)λM(x,y,t)+(1λ)ϱAB(t);

    ● noncyclic contraction type mapping if T is noncyclic and there exists λ(0,1) such that for all (x,y)A×B and t>0

    M(Tx,Ty,t)λmax{M(x,y,t),M(x,Ty,t),M(Tx,y,t)}+(1λ)ϱAB(t);

    ● noncyclic relatively nonexpansive mapping, if T is noncyclic and

    M(Tx,Ty,t)M(x,y,t),

    for all (x,y)A×B, t>0. In this case, if A=B, then T is called a nonexpansive self-mapping.

    It is clear that every noncyclic contraction type mapping is a noncyclic contraction. Moreover, any noncyclic contraction type mapping is a relatively nonexpansive mapping, but the reverse is not true.

    Example 3.2. Let X=R, A=[0,1] and B=[3,4]. Suppose M(x,y,t)=tt+|xy| and define T:ABAB by

    T(x)={x,xA3,xB.

    Notice that T(A)A, T(B)B and ϱAB(t)=tt+2. Also, (x,y)A×B, t>0,

    M(Tx,Ty,t)=tt+|x3|tt+|xy|=M(x,y,t).

    Now suppose T is a noncyclic contraction type mapping. Then for some λ(0,1) and for all (x,y)A×B, we have

    M(Tx,Ty,t)ϱAB(t)λ[M(x,y,t)ϱAB(t)],t>0.

    Thus we have,

    tt+|x3|tt+2λ[tt+|xy|tt+2],t>0.

    Besides for x=0, y=3 we have

    tt+3tt+2λ[tt+3tt+2],t>0,

    which implies that λ1 and this is a contradiction. Hence, T is not a noncyclic contraction type mapping.

    It is worth noticing that the class of noncyclic relatively nonexpansive mappings may not be continuous in general.

    Example 3.3. Consider X=R and let A=[1,0], B=[0,1] and M(x,y,t)=tt+|xy|. Define T:ABAB by

    T(x)={x1,xA[1,12]x2,xA(12,0]x,xB.

    Clearly T is not continuous, T(A)A and T(B)B. We claim that T is a noncyclic relatively nonexpansive mapping.

    If xA[1,12], yB, t>0, then

    M(Tx,Ty,t)=tt+|TxTy|=tt+|x1y|tt+|xy|=M(x,y,t).

    If xA(12,0], yB, t>0, then

    M(Tx,Ty,t)=tt+|TxTy|=tt+|x2y|tt+|xy|=M(x,y,t).

    Hence T is a noncyclic relatively nonexpansive mapping.

    In 2005, Eldred et al. studied the existence of best proximity pairs for noncyclic relatively nonexpansive mappings defined on a union of two nonempty, weakly compact and convex subsets of a strictly convex Banach space X by using a geometric notion of proximal normal structure (see Theorem 2.2 of [14]). In the case that we restrict the considered mappings to noncyclic contractions, then the existence of best proximity pairs is guaranteed without the proximal normal structure [15,16].

    In what follows we present best proximity pair results in the framework of strictly convex fuzzy metric spaces. To this end we need the following useful lemmas.

    Lemma 3.4. Let (A,B) be a nonempty, F-bounded, closed, convex pair in a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition () and has the property  (C). Let T:ABAB be a noncyclic relatively nonexpansive mapping. Then there exists a pair (G1,G2)(A,B) which is minimal with respect to being nonempty, closed, convex and T-invariant pair of subsets of (A,B) such that ϱG1G2(t)=ϱAB(t), for all t>0. Also (G1,G2) is F-proximinal.

    Proof. It follows from Lemma 2.4 that (A0,B0) is a nonempty, closed and convex pair for which ϱAB(t)=ϱA0B0(t) for all t>0. Also, if xA0, then there exists a point yB0 such that M(x,y,t)=ϱAB(t) for all t>0. Since that T is relatively nonexpansive,

    ϱAB(t)M(Tx,Ty,t)M(x,y,t)=ϱAB(t), t>0,

    and so TxA0, that is, T(A0)A0. Equivalently, T(B0)B0 which concludes that (A0,B0) is T-invariant. Assume that Ξ is a collection of all nonempty sets GA0B0 such that (GA0,GB0) is a nonempty, closed and convex pair which is F-proximinal, T-invariant and

    ϱ(GA0)(GB0)(t)=ϱA0B0(t)(=ϱAB(t))

    for all t>0. Note that A0B0Ξ and so Ξ is nonempty. Suppose {Uj}jJ is a descending chain in Ξ and set U:=jJUj. Since X has the property  (C), we have

    UA0=(jJUj)A0=jJ(UjA0).

    Obviously, UA0 is closed and convex. Similarly, the set UB0 is also nonempty, closed, convex and it is easy to see that the pair (UA0,UB0) is T-invariant. We show that ϱ(UA0)(UB0)(t)=ϱAB(t) for all t>0 and that (UA0,UB0) is F-proximinal. Let xUA0. Then xUjA0 for any jJ. In view of the fact that the pair (UjA0,UjB0) is F-proximinal, there exists yUjB0 for which M(x,y,t)=ϱAB(t) for all t>0. Note that this element, y, is unique. Indeed, if there is another element yB0 such that M(x,y,t)=ϱAB(t) for all t>0 then from Lemma 1.19 there exists θ(0,1) such that W(y,y,θ){y,y}. It follows from the condition () that

    M(W(y,y,θ),x,t)=min{M(y,x,θ),x,t),M(y,x,θ),x,t)}

    for all t>0. Using Lemma 1.18 we obtain W(y,y,θ){y,y} which is a contradiction. Hence (UA0,UB0)Ξ. Now using Zorn's lemma, Ξ has a minimal element, say G. If we set G1=GA0 and G2=GB0, then the result follows. It is worth noticing that since GΞ is minimal we must have (G1,G2) is F-proximinal.

    It is remarkable to note that if in Lemma 3.4 the pair (A,B) is compact, then the condition of property (C) of X can be dropped.

    Notation: Under the hypothesis of Lemma 3.4, by MT(A,B) we denote the family of all nonempty, closed, convex, minimal and T-invariant pair (G1,G2)(A,B) for which ϱG1,G2(t)=ϱAB(t) for all t>0.

    Lemma 3.5. Let (A,B) be a nonempty pair in a convex fuzzy metric space (X,M,;W). Then

    Δ(¯con(A),¯con(B))(t)=Δ(A,B)(t),t>0.

    Proof. Since (A,B)(¯con(A),¯con(B)), it is sufficient to verify that Δ(A,B)(t)Δ(¯con(A),¯con(B))(t) for all t>0. Let xA and t>0 be arbitrary and fixed. Then for any yB we have M(x,y,t)Δ(x,B)(t). Put Δ(x,B)(t):=1rx. Thus we have BB[x,rx,t] which implies that ¯con(B)B[x,rx,t]. Therefore, ¯con(B)xAB[x,rx,t]. Put Δ(A,B)(t):=1r. Now if v¯con(B), then ¯con(A)B[v,r,t]. Indeed, for all xA since v¯con(B),

    M(x,v,t)1rx=Δ(x,B)(t)Δ(A,B)(t)=1r,

    and so xB[v,r,t], that is, AB[v,r,t] which concludes that ¯con(A)B[v,r,t]. This implies that

    ¯con(A)v¯con(B)B[v,r,t],

    which ensures that Δ(¯con(A),¯con(B))(t)1r=Δ(A,B)(t) and hence the lemma.

    Next theorem is the main result of this section.

    Theorem 3.6. Let (A,B) be a nonempty, F-bounded, closed and convex pair in a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition () and has the property  (C). Assume that T:ABAB is a noncyclic contraction type mapping in the sense of Definition 3.1. Then BestA×B(T).

    Proof. Lemma 3.4 guarantees that MT(A,B) is nonempty. Suppose (G1,G2)MT(A,B). In view of the fact that T is noncyclic, (¯con(T(G1)),¯con(T(G2)))(G1,G2) and so

    T(¯con(T(G1)))T(G1)¯con(T(G1)),
    T(¯con(T(G2)))T(G2)¯con(T(G2)),

    which implies that the closed and convex pair (¯con(T(G1)),¯con(T(G2))) is T-invariant, that is,

    (¯con(T(G1)),¯con(T(G2)))MT(A,B).

    It follows from the minimality of (G1,G2) that ¯con(T(G1))=G1 and ¯con(T(G2))=G2. Since (G1,G2) is F-proximinal, there exists an element (x,y)G1×G2 for which

    M(x,y,t)=ϱG1G2(t)(=ϱAB(t)),t>0.

    Relatively nonexpansiveness of T deduces that

    ϱAB(t)ϱ¯con(T(G1))¯con(T(G2))(t)M(Tx,Ty,t)M(x,y,t)=ϱAB(t),

    for all t>0 and so, ϱ¯con(T(G1))¯con(T(G2))(t)=M(Tx,Ty,t)=ϱAB(t). Let uG1 and t>0 be an arbitrary fixed number. If vG2, because of the fact that T is a noncyclic contraction type mapping, we have

    M(Tu,Tv,t)λmax{M(u,v,t),M(u,Tv,t),M(Tu,v,t)}+(1λ)ϱAB(t)λmax{M(u,v,t),M(u,Tv,t),M(Tu,v,t)}+(1λ)M(u,v,t)λΔ(G1,G2)(t)+(1λ)ϱAB(t)

    where λ(0,1). Put

    1r:=λΔ(G1,G2)(t)+(1λ)ϱAB(t).

    Then M(Tu,Tv,t)1r, and hence TvB[Tu,r,t] for all vG2. Thus T(G2)B[Tu,r,t] which concludes that

    G2=¯con(T(G2))B[Tu,r,t].

    Thus for any wG2 we have M(Tu,w,t)1r and so

    Δ(Tu,G2)(t)=infwG2M(Tu,w,t)1r,uG1.

    Therefore, Δ(T(G1),G2)(t)=infxG1Δ(Tu,G2)(t)1r. Equivalently, Δ(G1,T(G2))(t)1r. Using Lemma 3.5 we obtain

    Δ(G1,G2)(t)=Δ(¯con(T(G1)),G2)(t)=Δ(T(G1),G2)(t)1r=λΔ(G1,G2)(t)+(1λ)ϱAB(t).

    Thereby Δ(G1,G2)(t)=ϱAB(t), which leads us to

    M(x,y,t)=ϱAB(t),(x,y)G1×G2.

    We assert that both the sets G1 and G2 are singleton. Let x1 and x2 be two distinct elements of G1. Since X is strictly convex, from Lemma 1.19, there exists θ(0,1) for which W(x1,x2,θ){x1,x2}. According to the condition (), for any yG2 we obtain

    ϱAB(t)M(W(x1,x2,θ),y,t)>min{M(x1,y,t),M(x2,y,t)}Δ(G1,G2)(t)=ϱAB(t), t>0,

    which is a contradiction. So G1 is singleton. Similarly, G2 is singleton too. Let G1={p} and G2={q} for some (p,q)G1×G2. Then (p,q)BestA×B(T) and the proof is completed.

    The following corollaries are straightforward consequences of Theorem 3.6.

    Corollary 3.7. Let (A,B) be a nonempty, F-bounded, closed and convex pair in a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition () and has the property  (C). If T:ABAB is a noncyclic contraction mapping, then BestA×B(T).

    Corollary 3.8. Let (A,B) be a nonempty, closed and convex pair in a strictly convex and compact fuzzy metric space (X,M,;W) which satisfies the condition (). If T:ABAB is a noncyclic contraction type mapping, then BestA×B(T).

    In the next section, we present an extension version of Corollary 3.8 for noncyclic relatively nonexpansive mappings. We do that by considering a geometric concept of fuzzy proximal normal structure which is defined on a nonempty and convex pair of subsets of a convex fuzzy metric space.

    The notion of proximal normal structure (PNS for brief) was first introduced in [14] in the setting of Banach spaces in order to study the existence of best proximity pairs for noncyclic relatively nonexpansive mappings. After that, in [17], a concept of proximal quasi-normal structure as a generalization of PNS was presented in the framework of convex metric spaces for the purpose of survey the existence of best proximity points for cyclic relatively nonexpansive mappings. We also mention that a characterization of PNS was given in [18] by using proximal diametral sequences. It was announced in [14] that every nonempty, compact and convex pair in a Banach space X has the PNS (see also Theorem 3.5 of [18] for a different approach to the same problem).

    In what follows we present the concept of PNS in the setting of convex fuzzy metric spaces.

    Definition 4.1. A convex pair (A,B) in a convex fuzzy metric space (X,M,;W) is said to have Fuzzy proximal normal structure (F-PNS for brief) if for any F-bounded, closed, convex and proximinal pair (G1,G2)(A,B) such that ϱG1G2(t)=ϱAB(t) and Δ(G1,G2)(t)<ϱAB(t) for all t>0, there exist a point (u,v)G1×G2 and t0>0 such that

    min{Δ(u,G2)(t0),Δ(G1,v)(t0)}>Δ(G1,G2)(t0).

    It is clear that under the assumptions of the above definition, the sets G1 and G2 are not singleton. Moreover, if we take A=B, then we get the notion of fuzzy normal structure which was introduced in [3].

    Remark 4.2. It is remarkable to note that if X is a Banach space, ab=min{a,b} and for any x,yX and t>0,θ(0,1), we define

    M(x,y,t)=tt+xy,W(x,y,θ)=θx+(1θ)y,

    then the concepts of F-PNS and PNS coincide. In this way, every nonempty, bounded, closed and convex pair in a uniformly convex Banach space X has the F-PNS (see Proposition 2.1 of [14]). We refer to [18,19,20] for further information about the PNS in Banach spaces.

    Definition 4.3. We say that a convex fuzzy metric space (X,M,;W) is strongly convex provided that for any x1,x2,y1,y2X and θ(0,1) we have

    M(W(x1,x2,θ),W(y1,y2,θ),t)θM(x1,y1,t)+(1θ)M(x2,y2,t),t>0.

    Example 4.4. Suppose that (X,W,d) is a hyperbolic metric space in the sense of Kohlenbach ([21]). If we define ab=min{a,b} and M(x,y,t)=tt+d(x,y) for all x,yX and t>0, then (X,M,;W) is a strongly convex fuzzy metric space.

    We are now ready to state the main conclusion of this section.

    Theorem 4.5. Let (A,B) be a nonempty, F-bounded, closed and convex pair in a strongly convex fuzzy metric space (X,M,;W) which satisfies the condition () and has the property  (C). Let T:ABAB be a noncyclic relatively nonexpansive mapping. If moreover, X is strictly convex and (A,B) has F-PNS, then BestA×B(T).

    Proof. It follows from Lemma 3.4 that MT(A,B) is nonempty. Assume that (G1,G2)MT(A,B). Equivalent reasoning of the proof of Theorem 3.6 concludes that ¯con(T(G1))=G1 and ¯con(T(G2))=G2. In the case that Δ(G1,G2)(t)=ϱAB(t) for all t>0, then by a similar argument of Theorem 3.6 we are finished. So assume that Δ(G1,G2)(t)<ϱAB(t) for all t>0. Since (A,B) has F-PNS, there is a point (u,v)G1×G2,t0>0 and ν(0,1) such that

    νmin{Δ(u,G2)(t0),Δ(G1,v)(t0)}Δ(G1,G2)(t0).

    By the proximinality of the pair (G1,G2), there exists an element (u,v)G1×G2 such that M(u,v,t)=σAB(t)=M(u,v,t) for all t>0. For θ(0,1) put u:=W(u,u,θ)G1 and v:=W(v,v,θ)G2. Since X is strongly convex,

    M(u,v,t)=M(W(u,u,θ),W(v,v,θ),t)θM(u,v,t)+(1θ)M(u,v,t)=ϱAB(t),

    which implies that M(u,v,t)=ϱAB(t) for all t>0. On the other hand for any yG2 we have

    M(u,y,t0)=M(W(u,u,θ),y,t0)θM(u,y,t0)+(1θ)M(u,y,t0).

    By taking infimum of two sides of the above inequality on yG2, we conclude that

    Δ(u,G2)(t0)θΔ(u,G2)(t0)+(1θ)M(u,G2,t0)θνΔ(G1,G2)(t0)+(1θ)Δ(G1,G2)(t0)>Δ(G1,G2)(t0).

    Similarly, we can see that

    Δ(G1,v)(t0)>Δ(G1,G2)(t0).

    Therefore, min{Δ(u,G2)(t0),Δ(G1,v)(t0)}>Δ(G1,G2)(t0). Put 1r1:=Δ(u,G2)(t0) and 1r2:=Δ(G1,v)(t0). If we define

    G1=(yG2B[y,r1,t0])G1andG2=(xG1B[x,r2,t0])G2,

    then (G1,G2) is a closed and convex subset of (G1,G2) and (u,v)G1×G2 which implies that ϱG1G2(t)=ϱAB(t) for any t>0. We show that T is noncyclic on G1G2. Suppose xG1. Then xG1 and for any yG2 we have M(x,y,t0)1r1. Since T is relatively nonexpansive, M(Tx,Ty,t0)M(x,y,t0)1r1, that is, TyB[Tx,r1,t0] for all yG2. Thus T(G2)B[Tx,r1,t0]. Thereby, G2=¯con(T(G2))B[Tx,r1,t0] and so TxG1 for all xG1, which ensures that T(G1)G1. Equivalently, T(G2)G2, that is, T is noncyclic on G1G2. Minimality of (G1,G2) deduces that G1=G1 and G2=G2. Hence

    G1(yG2B[y,r1,t0])andG2(xG1B[x,r2,t0]).

    Thus, for any (x,y)G1×G2 we have M(x,y,t0)max{1r1,1r2} and so

    min{1r1,1r2}>Δ(G1,G2)(t0)max{1r1,1r2},

    which is impossible.

    To obtain a real extension of Theorem 1.24, we need the following proposition.

    Proposition 4.6. Every nonempty, compact and convex pair (A,B) in a strictly convex fuzzy metric space (X,M,;W) which satisfies the condition () has the F-PNS.

    Proof. Suppose the contrary, that is, there exists a closed, convex and proximinal pair (G1,G2)(A,B) such that

    {ϱG1G2(t)=ϱAB(t)>Δ(G1,G2)(t),t>0,Δ(u,G2)(t)=Δ(G1,G2)(t),uG1,t>0.

    Notice that if G2={v}, for some vX, then from the proximinality of the pair (G1,G2), there exists an element uG1 such that M(u,v,t)=ϱAB(t) for all t>0. Then

    ϱAB(t)=M(u,v,t)=Δ(u,G2)(t)=Δ(G1,G2)(t),

    which is impossible. So, assume that v1,v2G2. Strict convexity of X implies that there exists θ0(0,1) for which G2W(v1,v2,θ0){v1,v2}. Proximinality of (G1,G2) deduces that there are u1,u2G1 for which M(u1,v1,t)=ϱAB(t)=M(u2,v2,t) for any t>0. If u1=u2, then by the condition (),

    M(u1,W(v1,v2,θ0),t)>min{M(u1,v1,t),M(u2,v2,t)}=ϱAB(t),

    which is impossible. Thus u1u2. Again from the strict convexity of X there exists θ1(0,1) such that G2W(u1,u2,θ1){u1,u2}. Since G2 is compact and M(W(u1,u2,θ1), .t) is continuous on G2, there exists an element v3G2 for which

    M(W(u1,u2,θ1),v3,t)=Δ(W(u1,u2,θ1),G2)(t)=Δ(G1,G2)(t),t>0.

    Besides, from the condition () we have

    Δ(G1,G2)(t)=M(W(u1,u2,θ1),v3,t)>min{M(u1,v3,t),M(u2,v3,t)},

    which is a contradiction.

    By an equivalent manner if Δ(G1,v)(t)=Δ(G1,G2)(t) for all vG2 and t>0, then we get a contradiction and the result follows.

    The next result is a generalization of Theorem 1.24 and Corollary 3.8.

    Corollary 4.7. Let (A,B) be a nonempty, compact, convex pair in a strongly convex fuzzy metric space (X,M,;W) which is strictly convex and satisfies the condition (). Assume that T:ABAB is a noncyclic relatively nonexpansive mapping. Then BestA×B(T).

    As a consequence of Corollary 4.7 we obtain the following best proximity pair theorem which is a main result of [14].

    Corollary 4.8. Let (A,B) be a nonempty, compact, convex pair in a strictly convex Banach space X. Assume that T:ABAB is a noncyclic relatively nonexpansive mapping. Then BestA×B(T).

    In this article, we have considered the concept of fuzzy projection operator and used to ensure the nonemptiness of proximal pairs of F-bounded, closed and convex pair of subsets of a strictly convex fuzzy metric space. Then we have established a best proximity pair theorem for noncyclic contractions. Finally by using a geometric property of fuzzy proximal normal structure, we have presented a new extension of Kirk's fixed point theorem ([2]) for noncyclic relatively nonexpansive mappings in the framework of strictly convex fuzzy metric spaces.

    The authors would like to thank the anonymous referees for the careful reading of the manuscript and useful comments. The third author is thankful for the support of Basque Government (Grant No. 1207-19)

    The authors declare that they have no conflicts of interest.



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