1 Introduction

Probabilistic metric spaces (in short PM-spaces) are a probabilistic generalization of metric spaces which are appropriate to carry out the study of those situations wherein distances are measured in terms of distribution functions rather than non-negative real numbers. The study of PM-spaces was initiated by Menger [1]. Schweizer and Sklar [2] further enriched this concept and provided a new impetus by proving some fundamental results on this theme.

The first result on fixed point theory in PM-spaces was given by Sehgal and Bharueha-Reid [3] wherein the notion of probabilistic B-contraction was introduced and a generalization of the classical Banach fixed point principle to complete Menger PM-spaces was given. In [3], it was proved that any B-contraction on a complete Menger space (S,F, T M ), where t-norm T M is defined by T M (x,y)=min{x,y}, has a unique fixed point. In 1982, Hadžić [4] extended the result contained in [3] for a more general class of t-norms called H-type t-norms (see also [5]).

After that several types of contractions and associated fixed point theorems have been established in PM-spaces by various authors, e.g., [68] (see also [912]). We also refer to a nice book on this topic by Hadžić and Pap [13]. In this continuation, Choudhury and Das [14] extended the classical metric fixed point result of Khan et al. [15] by introducing the idea of altering distance functions in PM-spaces. In [14], it was proved that any probabilistic ϕ-contraction on a complete Menger space (S,F, T M ) has a unique fixed point.

An open problem that remains to be investigated is whether the results are valid in the cases of any arbitrary continuous t-norm. (However, in [16] Miheţ gave an affirmative answer to the question raised in [14] using the idea of probabilistic boundedness and H-type t-norms along with some additional conditions.) Very recently, Babačev in [17] extended and improved the results of Choudhury and Das [14] for a nonlinear generalized contraction wherein she used the associated t-norm as a min norm.

In this paper, we show by means of an example that Babačev’s [17] results do not hold for the class of t-norms T T p . Further, we prove a fixed point theorem for quasi-type contraction involving altering distance functions in a complete Menger space for any continuous t-norm T.

2 Preliminaries

Consistent with Choudhury and Das [14], Choudhury et al. [18] and Babačev [17], the following definitions and results will be needed in the sequel.

In the standard notation, let D + be the set of all distribution functions F:R[0,1] such that F is a nondecreasing, left-continuous mapping which satisfies F(0)=0 and sup x R F(x)=1. The space D + is partially ordered by the usual point-wise ordering of functions, i.e., FG if and only if F(t)G(t) for all tR. The maximal element for D + in this order is the distribution function given by

ϵ 0 (t)= { 0 , t 0 , 1 , t > 0 .

Definition 2.1 [2]

A binary operation T:[0,1]×[0,1][0,1] is a continuous t-norm if it satisfies the following conditions:

  1. (a)

    T is commutative and associative,

  2. (b)

    T is continuous,

  3. (c)

    T(a,1)=a for all a[0,1],

  4. (d)

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

The following are the three basic continuous t-norms:

  1. (i)

    The minimum t-norm, T M , is defined by T M (a,b)=min{a,b}.

  2. (ii)

    The product t-norm, T p , is defined by T p (a,b)=ab.

  3. (iii)

    The Lukasiewicz t-norm, T L , is defined by T L (a,b)=max{a+b1,0}.

Regarding the pointwise ordering, the following inequalities hold:

T L < T p < T M .

Definition 2.2 A Menger probabilistic metric space (briefly, Menger PM-space) is a triple (X,F,T), where X is a nonempty set, T is a continuous t-norm, and F is a mapping from X×X into D + such that if F x y denotes the value of F at the pair (x,y), the following conditions hold:

(PM1) F x y (t)= ϵ 0 (t) if and only if x=y,

(PM2) F x y (t)= F y x (t),

(PM3) F x y (t+s)T( F x z (t), F z y (s)) for all x,y,zX and s,t0.

Remark 2.1 [3]

Every metric space is a PM-space. Let (X,d) be a metric space and T(a,b)=min{a,b} be a continuous t-norm. Define F x y (t)= ϵ 0 (td(x,y)) for all x,yX and t>0. The triple (X,F,T) is a PM-space induced by the metric d.

Definition 2.3 Let (X,F,T) be a Menger PM-space.

  1. (1)

    A sequence { x n } n in X is said to be convergent to x in X if for every ϵ>0 and λ>0, there exists a positive integer N such that F x n x (ϵ)>1λ whenever nN.

  2. (2)

    A sequence { x n } n in X is called a Cauchy sequence if for every ϵ>0 and λ>0, there exists a positive integer N such that F x n x m (ϵ)>1λ whenever n,mN.

  3. (3)

    The space X is said to be complete if every Cauchy sequence in X is convergent to a point in X.

The (ϵ,λ)-topology [2] in a Menger space (X,F,T) is introduced by the family of neighborhoods N x of a point xX given by

N x = { N x ( ϵ , λ ) : ϵ > 0 , λ ( 0 , 1 ) } ,

where

N x (ϵ,λ)= { y X : F x y ( ϵ ) > 1 λ } .

The (ϵ,λ)-topology is a Hausdorff topology. In this topology the function f is continuous in x 0 X if and only if for every sequence x n x 0 it holds that f( x n )f( x 0 ).

Definition 2.4 (Altering distance function [15])

The control function ψ:[0,)[0,) is called an altering distance function if it has the following properties:

  1. (i)

    ψ is monotone increasing and continuous,

  2. (ii)

    ψ(t)=0 if and only if t=0.

The following category of functions was introduced in [14].

Definition 2.5 [14]

A function ϕ:[0,)[0,) is said to be a Φ-function if it satisfies the following conditions:

  1. (i)

    ϕ(t)=0 if and only if t=0,

  2. (ii)

    ϕ(t) is strictly monotone increasing and ϕ(t) as t,

  3. (iii)

    ϕ is left continuous in (0,),

  4. (iv)

    ϕ is continuous at 0.

The class of all Φ-functions will be denoted by Φ.

Lemma 2.1 [17]

Let (X,F,T) be a Menger PM-space. Let ϕ:[0,)[0,) be a Φ-function. Then the following statement holds.

If for x,yX, 0<c<1, we have F x y (ϕ(t)) F x y (ϕ(t/c)) for all t>0, then x=y.

Theorem 2.1 [17]

Let (X,F,T) be a complete Menger PM-space with a continuous t-norm T which satisfies T(a,a)a for every a[0,1]. Let c(0,1) be fixed. If for a Φ-function ϕ and a self-mapping f on X,

F f x f y ( ϕ ( t ) ) min { F x y ( ϕ ( t c ) ) , F x f x ( ϕ ( t c ) ) , F y f y ( ϕ ( t c ) ) , F x f y ( 2 ϕ ( t c ) ) , F y f x ( 2 ϕ ( t c ) ) }
(2.1)

holds for every x,yX and all t>0, then f has a unique fixed point in X.

3 Main results

We begin with the following example.

Example 3.1 Let X=[0,) and T= T p . For each t(0,), define

F x y (t)= { min { x , y } max { x , y } , t > 0 , x y , 1 , t > 0 , x = y .

It is clear that (X,F, T p ) is a complete Menger PM-space (see [19]) (but here (X,F, T M ) is not a PM-space). Let us consider the function

f:XX,fx=x+1,xX.

Then it can be easily seen that the above example satisfies Theorem 2.1 for T p . Indeed,

  • Case I. If x=y, then inequality (2.1) is obviously true.

  • Case II. If xy and x<y, then we have x+1<y+1, x<x+1, y<y+1, x<y+1.

For any function ϕΦ and c(0,1), inequality (2.1) becomes

x + 1 y + 1 min { x y , x x + 1 , y y + 1 , x y + 1 , min { y , x + 1 } max { y , x + 1 } } .

If x+1<y, then we have

x + 1 y + 1 min { x y , x x + 1 , y y + 1 , x y + 1 , x + 1 y } .

Clearly, the inequality holds with the minimum value x/(y+1).

If x+1=y, then we have

x + 1 y + 1 min { x y , x x + 1 , y y + 1 , x y + 1 , 1 } .

Then the inequality holds with the minimum value x/(y+1).

And if x+1>y, then we have

x + 1 y + 1 min { x y , x x + 1 , y y + 1 , x y + 1 , y x + 1 } .

The inequality holds with the minimum value x/(y+1).

  • Case III. If xy and x>y, then we have x+1>y+1, x+1>y.

By inequality (2.1), we have

y + 1 x + 1 min { y x , x x + 1 , y y + 1 , min { x , y + 1 } max { x , y + 1 } , y x + 1 } .

If x>y+1, then we have

y + 1 x + 1 min { y x , x x + 1 , y y + 1 , y + 1 x , y x + 1 } .

The inequality holds with the minimum value y/(x+1).

If x=y+1, then we have

y + 1 x + 1 min { y x , x x + 1 , y y + 1 , 1 , y x + 1 } .

The inequality holds with the minimum value y/(x+1).

Finally, if x<y+1, then we have

y + 1 x + 1 min { y x , x x + 1 , y y + 1 , x y + 1 , y x + 1 } .

Again inequality holds with the minimum value y/(x+1).

Thus the above example satisfies all the conditions of Theorem 2.1 with the t-norm T p , but here the mapping f has no fixed point. Therefore, Theorem 2.1 cannot be generalized for T T p .

Now, we are motivated to introduce our result.

Theorem 3.1 Let (X,F,T) be a complete Menger PM-space with a continuous t-norm, and let c(0,1) be fixed. If for a Φ-function ϕ and a self-mapping f on X,

F f x f y ( ϕ ( t ) ) min { F x y ( ϕ ( t c ) ) , F x f x ( ϕ ( t c ) ) , F y f y ( ϕ ( t c ) ) , F y f x ( ϕ ( t c ) ) }
(3.1)

holds for every x,yX and all t>0, then f has a unique fixed point in X.

Proof Let x 0 X. Now, construct a sequence { x n } in X as follows:

x n =f x n 1 ,n=1,2,.

Applying (3.1) for x= x n 1 and y= x n , we have

F x n x n + 1 ( ϕ ( t ) ) = F f x n 1 f x n ( ϕ ( t ) ) min { F x n 1 x n ( ϕ ( t / c ) ) , F x n 1 x n ( ϕ ( t / c ) ) , F x n x n + 1 ( ϕ ( t / c ) ) , F x n x n ( ϕ ( t / c ) ) } = min { F x n 1 x n ( ϕ ( t / c ) ) , F x n x n + 1 ( ϕ ( t / c ) ) }

for all t>0.

We should prove that

min { F x n 1 x n ( ϕ ( t / c ) ) , F x n x n + 1 ( ϕ ( t / c ) ) } = F x n 1 x n ( ϕ ( t / c ) )
(3.2)

for all t>0.

If it is not, there exists p>0 such that

F x n 1 x n ( ϕ ( p / c ) ) > F x n x n + 1 ( ϕ ( p / c ) ) .
(3.3)

Then, using (3.1), we have

F x n + 1 x n ( ϕ ( p ) ) F x n x n + 1 ( ϕ ( p / c ) ) min { F x n 1 x n ( ϕ ( p / c 2 ) ) , F x n x n + 1 ( ϕ ( p / c 2 ) ) } .

If

min { F x n 1 x n ( ϕ ( p / c 2 ) ) , F x n x n + 1 ( ϕ ( p / c 2 ) ) } = F x n 1 x n ( ϕ ( p / c 2 ) ) ,
(3.4)

then by (3.3) and (3.4)

F x n 1 x n ( ϕ ( p / c ) ) > F x n x n + 1 ( ϕ ( p / c ) ) F x n 1 x n ( ϕ ( p / c 2 ) ) .

So, we get a contradiction.

Accordingly, min{ F x n 1 x n (ϕ(p/ c 2 )), F x n x n + 1 (ϕ(p/ c 2 ))}= F x n x n + 1 (ϕ(p/ c 2 )).

Now, we have

F x n x n + 1 ( ϕ ( p ) ) F x n x n + 1 ( ϕ ( p / c ) ) F x n x n + 1 ( ϕ ( p / c 2 ) ) min { F x n 1 x n ( ϕ ( p / c 3 ) ) , F x n x n + 1 ( ϕ ( p / c 3 ) ) } .

Repeating the same procedure, we conclude that

F x n 1 x n ( ϕ ( p / c ) ) > F x n x n + 1 ( ϕ ( p / c ) ) F x n x n + 1 ( ϕ ( p / c 2 ) ) F x n x n + 1 ( ϕ ( p / c k ) ) .

Since F x n x n + 1 (ϕ(p/ c k ))1, k, we get a contradiction. Accordingly, (3.2) is true. Therefore,

F x n x n + 1 ( ϕ ( t ) ) F x n 1 x n ( ϕ ( t / c ) ) F x 1 x 0 ( ϕ ( t / c n ) ) 1

as n.

By the property of ϕ, given s>0, there exists t>0 such that s>ϕ(t). Thus,

F x n x n + 1 (s)1as n
(3.5)

for all s>0.

Now, we claim that { x n } is a Cauchy sequence. If not, then ϵ>0 and λ>0, and subsequences { x m ( k ) } and { x n ( k ) } such that m(k)<n(k) and

F x m ( k ) x n ( k ) (ϵ)<1λ,
(3.6)
F x m ( k ) x n ( k ) 1 (ϵ)1λ.
(3.7)

Since F is non-decreasing, we have

{ x : F x p ( ϵ ) 1 λ } { x : F x p ( ϵ ) 1 λ }

for all pX, λ>0, and 0< ϵ <ϵ. It follows that whenever the above construction is possible for ϵ>0, λ>0, it is also possible to construct { x m ( k ) } and { x n ( k ) } satisfying (3.6) and (3.7) corresponding to ϵ >0, λ>0 whenever ϵ <ϵ.

Since ϕ is continuous at 0 and strictly monotonic increasing with ϕ(0)=0, it is possible to obtain ϵ 1 >0 such that ϕ( ϵ 1 )<ϵ. Then, by the above argument, it is possible to obtain increasing sequences of integers m(k) and n(k) with m(k)<n(k) such that

F x m ( k ) x n ( k ) ( ϕ ( ϵ 1 ) ) <1λ,
(3.8)
F x m ( k ) x n ( k ) 1 ( ϕ ( ϵ 1 ) ) 1λ.
(3.9)

Since 0<c<1 and ϕΦ, we can choose η>0 such that 0<η<ϕ( ϵ 1 /c)ϕ( ϵ 1 ). Since ϕ is strictly increasing, therefore

ϕ( ϵ 1 /c)η>ϕ( ϵ 1 ).

From (3.9), we get

F x m ( k ) x n ( k ) 1 ( ϕ ( ϵ 1 / c ) η ) > F x m ( k ) x n ( k ) 1 ϕ( ϵ 1 )1λ.
(3.10)

By (3.5), for λ 1 <λ<1, it is possible to find a positive integer N 1 such that for all k> N 1 ,

F x m ( k ) x m ( k ) 1 ϕ ( η ) 1 λ 1 , F x n ( k ) x n ( k ) 1 ϕ ( η ) 1 λ 1 . }
(3.11)

By (PM3), we have

F x m ( k ) 1 x n ( k ) 1 ( ϕ ( ϵ 1 / c ) ) T ( F x m ( k ) 1 x m ( k ) ( η ) , F x m ( k ) x n ( k ) 1 ( ϕ ( ϵ 1 / c ) η ) ) .
(3.12)

Let 0< λ 2 < λ 1 <λ<1 be arbitrary. Then by (3.5) there exists a positive integer N 2 such that for all k> N 2 ,

F x m ( k ) 1 x m ( k ) (η)1 λ 2 .
(3.13)

Now, using (3.10), (3.12), and (3.13), we have, for all k>max{ N 1 , N 2 },

F x m ( k ) 1 x n ( k ) 1 ( ϕ ( ϵ 1 / c ) ) T(1 λ 2 ,1λ).

As λ 2 is arbitrary and T is continuous, we have

F x m ( k ) 1 x n ( k ) 1 ( ϕ ( ϵ 1 / c ) ) T(1,1λ)=1λ.
(3.14)

Now, using (3.1), (3.9), (3.11), and (3.14), we have

1 λ > F x m ( k ) x n ( k ) ( ϕ ( ϵ 1 ) ) = F f x m ( k ) 1 f x n ( k ) 1 ( ϕ ( ϵ 1 ) ) min { F x m ( k ) 1 x n ( k ) 1 ( ϕ ( ϵ 1 / c ) ) , F x m ( k ) 1 x m ( k ) ( ϕ ( ϵ 1 / c ) ) , F x n ( k ) 1 x n ( k ) ( ϕ ( ϵ 1 / c ) ) , F x n ( k ) 1 x m ( k ) ( ϕ ( ϵ 1 / c ) ) } min { 1 λ , 1 λ , 1 λ , 1 λ } = 1 λ ,

which is a contradiction. Therefore { x n } is a Cauchy sequence in a complete Menger PM-space X, thus there exists zX such that z= lim n x n .

Now, we will show that z is a fixed point of f. Since ϕΦ, we have that for every x,yX and all s>0, there exists r>0 such that s>ϕ(r) and n 0 N such that for all n n 0 ,

F f z z (s)T ( F f z x n ( ϕ ( r ) ) , F x n z ( s ϕ ( r ) ) ) .
(3.15)

Since s>ϕ(r), thus (sϕ(r))>0. Also, since z= lim n x n , for arbitrary δ(0,1), we have

F x n z ( s ϕ ( r ) ) >1δ.
(3.16)

Hence, from (3.15) and (3.16), we get

F f z z (s)T ( F f z x n ( ϕ ( r ) ) , 1 δ ) .

Since δ>0 is arbitrary and the t-norm T is continuous, we get

F f z z ( s ) F f z x n ( ϕ ( r ) ) F f z f x n 1 ( ϕ ( r ) ) min { F z x n 1 ( ϕ ( r / c ) ) , F z f z ( ϕ ( r / c ) ) , F x n 1 f x n 1 ( ϕ ( r / c ) ) , F x n 1 f z ( ϕ ( r / c ) ) } .

Letting n in the above inequality and using the fact that the t-norm T is continuous, we obtain

F f z z ( ϕ ( r ) ) F z f z ( ϕ ( r / c ) )

and applying Lemma 2.1, we get z=fz.

Next, we prove the uniqueness of a fixed point. Let wX be another fixed point of f, i.e., fw=w. Since ϕΦ, for all s>0, there exists r>0 such that s>ϕ(r). Then we have

F z w ( s ) F z w ( ϕ ( r ) ) = F f z f w ( ϕ ( r ) ) min { F z w ( ϕ ( r / c ) ) , F z f z ( ϕ ( r / c ) ) , F w f w ( ϕ ( r / c ) ) , F w f z ( ϕ ( r / c ) ) } = min { F z w ( ϕ ( r / c ) ) , F z z ( ϕ ( r / c ) ) , F w w ( ϕ ( r / c ) ) , F w z ( ϕ ( r / c ) ) } = F z w ( ϕ ( r / c ) ) .

From Lemma 2.1, it follows that z=w, i.e., z is the unique fixed point of f. □

4 Connection with metric spaces

It is well known that every metric space (X,d) is also a Menger space (X,F, T M ) if F is defined in the following way:

F x y (t)= { 1 , d ( x , y ) < t , 0 , d ( x , y ) t .

If ψ is an altering distance function defined in Definition 2.4 with additional property ψ(t) as t, then the function

ϕ(t)= { inf { α : ψ ( α ) t } , t > 0 , 0 , t = 0

is a Φ-function (see [18]).

We will present that (3.1) in this case implies

ψ ( d ( f x , f y ) ) cmax { ψ ( d ( f x , x ) ) , ψ ( d ( f y , y ) ) , ψ ( d ( x , y ) ) , ψ ( d ( f x , y ) ) } ,

c(0,1) and x,yX in a metric space.

Suppose the contrary, i.e., there exists t>0 such that F f x f y (ϕ(t))=0 and all of

F x f x ( ϕ ( t / c ) ) =1, F y f y ( ϕ ( t / c ) ) =1, F x , y ( ϕ ( t / c ) ) =1,and F f x y ( ϕ ( t / c ) ) =1.

So, F f x f y (ϕ(t))=0 implies that d(fx,fy)ϕ(t), and since ψ is continuous, we have

ψ ( d ( f x , f y ) ) t.

Similarly, since F x f x (ϕ(t/c))=1, we have that d(fx,x)<ϕ(t/c), which implies

ψ ( d ( f x , x ) ) < t c .

Also, we have the following:

ψ ( d ( f y , y ) ) < t c ,ψ ( d ( x , y ) ) < t c ,andψ ( d ( f x , y ) ) < t c .

Thus, we have

ψ ( d ( f x , f y ) ) >cmax { ψ ( d ( f x , x ) ) , ψ ( d ( f y , y ) ) , ψ ( d ( x , y ) ) , ψ ( d ( f x , y ) ) } ,

and we get a contradiction.