1. Introduction
Recall a mapping is said a probabilstic contraction if it satisfies for all and where .
And a mapping is called a probabilstic -contraction if it satisfies for all and where is a gauge function satisfying certain conditions.
In 1972, Sehgal and Bharucha-Ried proved the following theorem which is the probabilstic version of the classical Banach Contraction Principle.
Theorem 1. ([
9]) Let
be a complete Menger space with t-norm
defined by
. If
is a probabilstic
contraction, then
T is a Picard mapping.
Since then, different authors studied probabilstic
-contractions and got some fixed point theorems. But, they assumpted that
satisfied
for all
to get corresponding results. As [
2] has pointed out, the condition is very strong and difficult for testing in practice. Therefore, a natural question arises whether this condition can be omitted or improved.
iri
[
2] have made a positive attempt. The following theorem is a revised result of [
2].
Theorem 2. ([
2,
7]) Let
be a complete Menger space with
of
H-type and
be a function satisfying the condition:
and for all .
If T is a probabilistic -contraction, then T is a Picard mapping.
Later, Jachymski in [
7] obtained the following result as a generalization of Theorem 1.2.
Theorem 3. ([
7]) Let
be a complete Menger space with
of
H-type and
be a function such that
and
for all
. If
T is a probabilistic
-contraction, then
T is a Picard mapping.
Fang [
4] relaxed restrictions on the gauge functions and gave a more general class of functions denoted by
, where
is the class of gauge functions
satisfying the condition: for each
there exists
such that
. And he obtained the following theorem as a generalization of Theorem 1.3.
Theorem 4. ([
4]) Let
be a complete Menger space with
of
H-type and
. If
T is a probabilistic
-contraction, then
T is a Picard mapping.
Mihet and Zaharia [
8] investigated the existence of fixed points for several classes of probabilistic
-contractions under Fang-type conditions. They also raised several open questions, one of which is whether Theorem 1.3 and Theorem 1.4 are equivalent?
Alegre and Romaguera [
1], Gregori et al. [
5] proved the equivalence of Theorem 1.3 and Theorem 1.4 using different methods. In their work, they used the set
or
, respectively, where
is the complement of set
A. It can be seen from their work that set
A plays an important role.
Taking inspiration from set A, we propose a new concept in this paper, called -dense set, which plays a crucial role in the definition of weak probabilistic -contraction.
We notice that a probabilstic -contraction involves the following three aspects:
(i) Two sets: X and ;
(ii) Two mappings: and ;
(iii) The bridge to contact the above two sets and two mappings:
for all and .
In this paper, we introduces a “small” set which we call -dense set. The reason why we say it “small" set is because sometimes the elements contained in the set is relatively little, and sometimes its Lebesgue measure may be 0.
we use -dense set—the “small" set P to replace and then define a weak probabilstic -contraction which involves the following three aspects:
(i)′ Two sets:X and P;
(ii)′ Two mappings: and ;
(iii)′ The bridge to contact the above two sets and two mappings:
for all and .
Because P is relatively small, the mapping is relatively simple. Thus, the relational formula which is difficult to be verified and satisfied in the whole scope is relatively easy to be verified and satisfied. In fact, in this paper, the condition “ and for all " in Theorem 1.3 can be weakened to the condition “ for , where is a -dense set."
2. Preliminaries
Throughout this paper, is the space of all mappings , such that F is left-continuous, non-decreasing on and .
A triangular norm (for short t-norm) is a binary operation which is commutative, associative, monotone and has 1 as the unit element. Basic examples are the ukasiewicz t-norm and the t-norms , where . The minimum t-norm is the strongest t-norm.
If is a t-norm, then is defined for every and as 1 if and if . A t-norm is said to be of H-type if the family is equicontinuous at . Obviously, the minimum is a t-norm of H-type, in addition, there are a large number of t-norms of H-type.
Definition 2.1. ([
6]) A Menger space is a triple
where
X is a nonempty set,
is a t-norm and
F is a mapping from
to
,
is denoted by
, satisfying the following conditions:
(PM1) for all iff ,
(PM2) for all ,
(PM3) for .
Let be a Menger space. The topology in X is introduced by the family of neighborhoods , where . If the t-norm is such that , then in the topology X is a metrizable topological space.
A sequence
converges to some point
in the
topology if and only if for every
and
there exists
such that
A sequence in X is a Cauchy sequence if and only if for every and there exists such that
for all .
A Menger probabilistic metric space is said to be complete if every Cauchy sequence in X is convergent.
Definition 2.2. ([
3]) A function
is said to be a
-function if it satisfies the following conditions:
(i) if and only if .
(ii) is strictly increasing and as .
(iii) is left-continuous in .
(iv) is continuous at 0.
The class of all functions satisfying (i)-(iv) will be denoted by .
Based on
-functions, a type of altering distance functions in probabilistic meric spaces, Choudhury and Das introduced a new type of contraction mapping and obtained the following theorem [
3].
Theorem 5. ([
3]) Let
be a complete Menger probabilistic metric space with
and
satisfies the following conditions:
for and ,
where is a -function and , then T has a unique fixed point.
3. Main Results
Definition 3.1. A set is said a -dense set if for each there exists such that and .
Example 3.2. Let , then P is a -dense set.
Example 3.3. Let , , , then are -dense sets. It is obvious that are countable and for , where u is the Lebesgue measure .
Definition 3.4. A function is said a weak gauge function if is a -dense set and for each .
Example 3.5. Let
and
defined by
then
is a weak gauge function.
Example 3.6. Let
and
defined by
then
is a weak gauge function.
Denote the class of weak gauge functions for a -dense set P.
Definition 3.7. Let
be a Menger space. A mapping
is called a weak probabilistic
-contraction if there exsits a
-dense set
P and
such that
for
and
.
The following lemma plays a crucial role in the proof of our main results.
Lemma 3.8. Let be a Menger space and is a weak probabilistic -contraction for , where P is a -dense set. Then for each and , there exsits such that
(i) ;
(ii) as .
Proof. Firstly, we show that for and ,.
Conversely, suppose that there exists such that ,then
, which is a contradiction.
Secondly, since , then for each , there exsits such that when ,
.
We claim there exists such that .
Suppose that for , . Then we have , which is a contradiction. Thus, for each and , there exsits such that
(i) ;
(ii) as . □
Theorem 6. Let be a complete Menger space with of H-type and is a weak probabilistic -contraction for some -dense set P with . Then T is a Picard mapping.
Proof. Let be an arbitrary point, we define the sequence in X by for all . If for some , then T has a fixed point. Therefore, in the following proof, we can suppose for each .
We complete the proof by the following five steps.
Step 1. We show that for,
Fix and let . Since there exsits such that
For and , by Lemma 3.8, there exsits such that when ,
. Then
,
thus, we have
.
for
, where
.
It is obvious for , since
Assume that (3.3) holds for some k, then
Step 3. is a Cauchy sequence in X.
For , as proved in Lemma 3.8, we can find and such that,
. Then
Let
, since
is equicontinuous at
and
, so there exists
such that
By step 1, we have that .
Thus there exists such that for all .
It follows from (3.4) that
for all and .
Thus, is a Cauchy sequence in X.
Step 4. T has a fixed point.
Since X is complete and is a Cauchy sequence in X, there exists a such that for For , as proved in Lemma 3.8, we can find and such that,
.
Then,
,
where .
Since and is continuous, passing , we get
for all .
Thus,
Step 5. T has at most one fixed point.
Suppose, on the contrary, that there exists another fixed point of T such that
.
Then, there exsits such that
Since , there exsits and such that
By Lemma 3.8, there exsits such that .
Then we have
which is a contradiction. Therefore, the fixed point of T is unique. □
Next, we will demonstrate the generality and validity of our results. Firstly, we are going to show that Theorem 1.4 can be obtained as an easy consequence of Theorem 3.9.
Proof of Theorem 1.4. Let be a complete Menger space with of H-type and and let T be a probabilistic -contraction.
Let , then P is a -dense set by the definition of .
Define by for , then is well-defined and . In fact, for , by the definition of P, , then
.
Thus, .
.
We can get for in a similar way.
So, . Thus, .
Since, for all . In particular, for all .
That is, for all . So, T is a weak probabilistic -contraction. Therefore, we can apply Theorem 3.9 and thus T is a Picard mapping.
Secondly, we use Theorem 3.9 to prove a more general theorem than Theorem 2.3. As a consequence, we answer an open question raised by Choudhury and Das [
3].
Theorem 7. Suppose
is a complete Menger space,
is a t-norm of
H-type. Suppose that
satisfies:
for
,
, where
,
, then
T is a Picard mapping.
Proof. Let , then P is a -dense set by the definition of .
Define by for , then is well-defined and . In fact, for , by the definition of P, .
.
We can get for in a similar way.
By the definition of , . So, . Thus, .
Since, for all is equivalent to
for .
That is, for all . So, T is a weak probabilistic -contraction. Therefore, we can apply Theorem 3.9 and thus T is a Picard mapping. □
Finally, we provide an example to demonstrate the validity of our results.
Example 3.9. Let with the metric , then is a complete metric space.
Let , then is a complete Menger space, where is a t-norm of H-type.
Now, define by and by ,
where P is the set of all rational numbers of X.
We notice that ,that is .
In the following, we show that T is a weak probabilistic -contraction while T is not a probabilistic -contraction. In fact, and . So if , then .
But, T is not a probabilistic -contraction. To see it, let and , where , then , while .