1. Introduction
Fixed-point theory is a very important branch of modern mathematics. Since the publication of Banach fixed-point theorem, research on fixed-point theory has been extremely rich both in theory and in application. Banach first obtained fixed points on k-contraction in complete metric spaces. Since then, scholars have obtained abundant fixed-point theorems in different spaces with different contraction conditions.
Menger probabilistic metric spaces (abbreviated as MPM spaces), a core concept in the theory of probabilistic metric spaces, was proposed by Karl Menger in 1942 [
1]. It aims to generalize the concept of traditional metric spaces to a probabilistic framework, enabling a more flexible description of distance relationships in uncertain environments.
Sehgal provided the Banach-type fixed point theorem in MPM spaces for the first time [
2]. Since then, various researchers have delved into probabilistic
-contractions and derived several fixed-point theorems in MPM spaces. However, due to the complexity of MPM space, strong conditions are often attached in order to obtain fixed points, one of which is that
fulfilled
for all
. Weakening the additional conditions in probabilistic
-contraction, or more precisely reducing the requirements for the
function, has become a direction of researchers’ efforts.
iri
[
3] positively attempted to modify the assumptions regarding the function
.
iri
obtained a fixed-point theorem under the condition:
and
for all
.
Later, Jachymski [
4] obtained the same result under the condition:
and
for all
, where
denotes the nth iteration of
.
Fang [
5] relaxed restrictions on the function
as well. Fang [
5] obtained the fixed-point result under the condition that
satisfied the following: for
there exists
, such that
holds.
In [
6], Choudhury and Das discussed a new kind of contraction, and the functions they used were as follows
, which is a strictly increasing function satisfying the following conditions: it is left-continuous at every point in its domain, in particular, it is continuous at 0; its minimum and maximum function values can be taken to 0 and infinity, respectively. The concept of control functions has paved the way for establishing novel fixed point outcomes within MPM spaces. Literatures that utilize the
-function include [
7,
8,
9,
10]. In the aftermath, a number of alternative control functions have been employed in issues concerning fixed points within PM spaces, with examples such as [
3,
5,
11].
Mihet and Zaharia [
12] 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 the result of Jachymski is equivalent to that of Fang?
Alegre and Romaguera [
13], and Gregori et al. [
14] proved the above equivalence using different methods. In their work, they used sets
or
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 ZH-set, which plays a vital role in the definition of weak probabilistic -contraction.
We notice that a probabilistic -contraction involves the following three aspects:
- (i)
Two sets: X and ;
- (ii)
Two mappings: and ;
- (iii)
The bridge to connect the above two sets and two mappings:
In this paper, we introduce a “small” set, which we call ZH-set. The reason we say it is a “small” set is because sometimes the elements contained in the set are relatively little, and sometimes its Lebesgue measure may be 0.
We use the ZH-set—the 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 connect the above two sets and two mappings:
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 ” can be weakened to the condition “ for , where is a ZH-set.”
2. Preliminaries
Throughout this paper, let . We define as the space of all mappings , such that F is left-continuous, non-decreasing on R, satisfies , and .
A triangular norm (for a short t-norm) is a binary operation , which is commutative, associative, and monotone and has 1 as the unit element. Basic examples are as follows:
The Łukasiewicz t-norm:
The product t-norm:
The minimum t-norm:
The minimum t-norm is the strongest t-norm, that is, for each and each 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 (see [
15] for details).
Definition 1 ([
15]).
A Menger probabalistic metric space (MPM space) is a triple , where M is a nonempty set, ⋄ is a t-norm, and F is a mapping from to , where is denoted by , satisfying the following conditions:(PM1) for all if ,
(PM2) for all ,
(PM3) for .
Definition 2 ([
15]).
Let be an MPM space.- (1)
A sequence converges to some point , if and only if for all .
- (2)
A sequence is called Cauchy if and only if for all .
- (3)
An MPM space is said to be complete if every Cauchy sequence in M is convergent.
Definition 3 ([
15]).
Let be an MPM space. is called a probabilistic φ-contraction if it satisfiesfor all and where is a function satisfying certain conditions. Definition 4 ([
16]).
The distance of a point from a non-empty set V for is defined asand the distance between two non-empty sets W and V for is defined as Definition 5. Let be a pair of non-empty subsets of an MPM space . Let P be a ZH-set. Then, the pair is said to have the S-property if and only if for and , satisfyingimplies that for all . 3. Main Results
Definition 6. A set is said to be a ZH-set if for each there exists , such that and . Equivalently, P contains a pair of sequences, say and , such that and .
Example 1. Let , then P is a ZH-set.
Example 2. Let , , , then are ZH-sets. It is obvious that are countable and for , where u is the Lebesgue measure.
Definition 7. A function is said to be a weak gauge function if is a ZH-set and for each .
Example 3. Let and be defined bythen φ is a weak gauge function. Example 4. Let and defined bythen φ is a weak gauge function. Denote as the class of weak gauge functions for a ZH-set P.
Definition 8. Let be an MPM space. A mapping is called a weak probabilistic φ-contraction if there exists a ZH-set P and , such thatfor and . The following lemma plays a crucial role in the proof of our main results.
Lemma 1. Let be an MPM space and be a weak probabilistic φ-contraction for , where P is a ZH-set. Then, for each and , there exists , 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 exists , such that when , .
We claim there exists , such that .
Suppose that for
,
. Then, we have
which is a contradiction. Thus, for each
and
, there exists
, such that
- (i)
;
- (ii)
as .
□
Theorem 1. Let be a complete MPM-space with ⋄ of H-type and is a weak probabilistic φ-contraction for some ZH-set P with . Then, T is a Picard mapping.
Proof. Let be an arbitrary point, we define the sequence in M 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 exists , such that
For
and
, by Lemma 1, there exists
, such that when
,
. Then
thus we have
.
Step 2.
for
, where
.
It is obvious for
, since
Assume that (
3) holds for some
k, then
Step 3. is a Cauchy sequence in M.
For
, as proved in Lemma 1, 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 (
4) that
for all
and
.
Thus, is a Cauchy sequence in M.
Step 4. T has a fixed point.
Since
M is complete and
is a Cauchy sequence in
M, there exists a
, such that
for
For
, as proved in Lemma 1, we can find
and
, such that
Then
where
.
Since and ⋄ is continuous, passing , we obtained 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 exists
such that
Since
, there exists
and
, such that
By Lemma 1, there exists
, 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. First of all, we prove that many existing results can be obtained by our theorem.
Theorem 2 ([
4]).
Let be a complete MPM space with ⋄ of H-type and be a function such that and for all . If T is a probabilistic φ-contraction, then T is a Picard mapping. Proof. Let P = , It clearly satisfies all the conditions in Theorem 1. □
Theorem 3 ([
5]).
Let be a complete MPM space with ⋄ of H-type and satisfying: for there exists , such thatholds. If T is a probabilistic ϕ-contraction, then T is a Picard mapping. Proof. Let
, then
P is a ZH-set by the property (
5) of
.
Define
by
for
, then
is well-defined and
. In fact, for
, by the definition of
P,
, then
Thus,
,
We can obtain
for
in a similar way.
So, . Thus, .
Since
for all
, in particular,
That is
for all
. So,
T is a weak probabilistic
-contraction. Therefore, we can apply Theorem 1 and thus
T is a Picard mapping. □
In [
6], Choudhury and Das defined a function
as follows:
Definition 9 ([
6]).
A function is said to be a type of special function if it satisfies the following conditions:- (i)
if and only if .
- (ii)
ϕ is strictly increasing and as .
- (iii)
ϕ is left-continuous in .
- (iv)
ϕ is continuous at 0.
The main result in [
6] is described as follows: Let
be a complete MPM space with
and
satisfies the following conditions:
for
and
,
, where
is a special function satisfying Definition 9. Then,
T has a unique fixed point.
At the end of the paper [
6], Choudhury and Das put forward an open question: Is the above result true for other t-norms? We provide a more general theorem than the above result by Theorem 1, thus providing at least a partial answer to this question.
Theorem 4. Suppose is a complete MPM-space, ⋄ is a t-norm of H-type. Suppose that satisfies:for , , where ϕ is as described in Definition 9, , then, T is a Picard mapping. Proof. Let , then P is a ZH-set by the properties of in Definition 9.
Define
by
for
, then
is well-defined and
. In fact, for
, by the definition of
P,
,
We can obtain
for
in a similar way.
By the properties of
,
. So,
Thus,
.
Since
for all
is equivalent to
for
, that is
So,
T is a weak probabilistic
-contraction. Therefore, we can apply Theorem 1 and thus
T is a Picard mapping. □
Example 5. Let and define as follows:Then is a complete MPM space (see Example in [5]), where . Let be defined by . Firstly, we show that T is not the Sehgal contraction. Suppose, on the contrary, that there exists , such that for all and .
Let , then when By (6), we haveSince , we haveAfter a simple calculation, we obtainPassing limit as , we have , which is a contradiction. Thus, T is not the Sehgal contraction. Let , where Q is the set of all rational numbers. Let be defined byWe now show that T is a weak probabilistic φ-contraction, i.e., T satisfies (1). Let . If , then we have , (1) holds. Suppose that . From (7), it is easy to see thatthus . Note thatWe obtain , which implies that since the function is strictly increasing on . By (6), we have . Thus,i.e., (1) holds. This shows that all the conditions of Theorem 1 are satisfied. By Theorem 1, we conclude that T has a unique fixed point in M. Indeed, is a unique fixed point of T. However, Theorem 2 in [4] cannot be applied to this example because the φ defined by (7) does not meet the conditions for all since . Lemma 2 ([
17]).
Let W and V be two non-empty subsets of a PM-space , then is a distribution function. Then, we define a novel weak contraction.
Definition 10. Let be a MPM-space and be two disjoint non-empty subsets of M. Let P be a ZH-set and . A non-self mapping is called weak ϕ-proximal contraction ifwhere , . Definition 11. Let W and V be two non-empty subsets of PM-space . Let P be a ZH-set. We define and as follows: Theorem 5. Let be a complete MPM-space with ⋄ of H-type and be two non-empty disjoint subsets of M where W is closed. Let P be a ZH-set and . Let be a weak ϕ-proximal contraction mapping which satisfies the following conditions:
- (i)
and satisfies the S-property,
- (ii)
there exist , such that for all .
Then, there exists an element such that for all , that is, T has a best proximity point.
Proof. From the hypothesis, it can be known that the existence of
, such that
holds. Due to
, there exists
, such that
holds. So, for all
,
Due to
, there exists
, such that
holds. So, for all
,
After performing
n steps in this way, we have for all
,
and
Due to the fact that
has the
S-property, we obtain from (
9) and (
10), for all
,
Since
T is a weak
-proximal contraction, we have for all
,
We claim that
is a Cauchy sequence. Firstly, we prove that for
,
Fix and let . Since there exists , such that
For
and
, by Lemma 1, the existence of
, such that
holds, when
. Then,
thus we have
Secondly, as proof in Theorem 1,
for
, where
.
Thirdly, for
, as proved in Lemma 1, we can find
and
, such that,
. Then
Let
, since
is equicontinuous at
and
, so there exists
, such that
From the foregoing description, we have that
Thus, there exists
, such that
for all
. It follows from (
12) that
for all
and
. Thus,
is a Cauchy sequence in
M. Since
is complete and
W is a closed subset of
M, there exists
, such that
Since
, we can pick
such that
holds. In fact, let
as in Lemma 1.
According to the non-decreasing property of distribution function, we obtain,
Passing limit as
,
Let us choose
be arbitrary, such that
, then
On the above inequality, taking the limit as
and using the results of (
13), (
14) we have,
Due to the arbitrariness of
and the left continuity of
, we have
this means
□
Example 6. Let and define as follows:Then, is a complete MPM-space, where . We consider the following disjoint closed subsets:Let be defined by . Then, for all . Note that and .
For the only point and the only point , for all .
Therefore, satisfies the S-property.
Let , where Q is the set of all rational numbers. Let be defined by By the same method as in Example 5, We can show that T is a weak ϕ-proximal contraction, that is,for , . That means all the conditions of Theorem 5 are satisfied. By Theorem 5, it is concluded that T has a best proximity point. Indeed, is the best proximity point.