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A fixed point theorem for a special class of probabilistic contraction
Fixed Point Theory and Applications volume 2011, Article number: 74 (2011)
Abstract
In this paper, a special class of probabilistic contraction will be considered. Using the theory of countable extension of t-norms, we proved a fixed point theorem for such a class of mappings f : S → S, where is a Menger space.
Mathematics Subject Classification (2000)
54H25, 47H10
1 Introduction
The notion of a probabilistic metric spaces is introduced in 1942 by K. Menger. The first idea of K. Menger was to use distribution function instead of nonnegative real numbers as values of the metric. Since then, the theory of probabilistic metric spaces has been developed in many directions [1]. The Banach fixed point theorem for contraction mappings has been generalized in different ways, see for example [2–7]. For example, Mihet [4] has worked on the existence and the uniqueness of fixed points of Sehgal contraction, Lj. Ćirić at all in [2] worked on a concept of monotone generalized contraction in partially ordered probabilistic metric spaces. Many interesting fixed point results for contraction mappings for singlevalued and multivalued mappings in probabilistic metric spaces can be found in [8].
First, we shall give some definitions and notations.
Definition 1[9]The ordered pair is said to be a probabilistic metric space if S is a nonempty set and (Δ+is the set of all distribution functions F such that F(0) = 0) so that the following conditions are satisfied (whereis written by F p,q for every (p, q) ∈ S × S):
-
1.
F p,q (x) = 1 for every x > 0 ⇔ p = q (p, q ∈ S).
-
2.
F p,q = F q,p for every p, q ∈ S.
-
3.
F p,q (x) = 1 and F q,r (y) = 1 ⇒ F p,r (x + y) = 1 for p, q, r ∈ S and x, y ∈ ℝ+.
Definition 2[9]A mapping T : [0, 1] × [0, 1] → [0, 1] is called a triangular norm (a t-norm) if the following conditions are satisfied:
T(a, 1) = a for every a ∈ [0, 1];
T(a, b) = T(b, a) for every a, b ∈ [0, 1];
a ≥ b, c ≥ d ⇒ T(a, c) ≥ T(b, d) (a, b, c, d ∈ [0, 1]);
T(a, T(b, c)) = T(T(a, b), c) (a, b, c ∈ [0, 1]).
Definition 3 If T is a t-norm, then its dual t-conorm S : [0, 1]2 → [0, 1] is given by
Example 1 Basic examples are as follows:
Definition 4[9]A Menger space is an ordered triple, where
is a probabilistic metric space, T is a t-norm and the generalized triangle inequality
holds for every p, q, r ∈ S and every x > 0, y > 0.
Definition 5 (i) A t-norm T is said to be strictly monotone if T(x, y) < T(x, z) whenever x ∈ (0, 1) and y < z.
-
(ii)
A t-norm T is called strict if it is continuous and strictly monotone.
-
(iii)
A continuous t-norm T is called Archimedean if T(x, x) < x, for all x ∈ (0, 1).
Theorem 1 A function T : [0, 1]2 → [0, 1] is a continuous Archimedean t-norm if and only if there exists a continuous, strictly decreasing function t :[0, 1] → [0, +∞] with t(1) = 0 such that for all x, y ∈ [0, 1]
The function t is then called an additive generator of T; it is uniquely determined by T up to a positive multiplicative constant.
The (ε, λ)--topology in S is introduced by the family of neighborhoods of v ∈ S, , where
If a t-norm T is such that then defines on S a metrizable topology.
Definition 6 A sequence {x n } n ∈ℕin S is a Cauchy sequence if and only if for every ε > 0 and λ ∈ (0,1) there exists n0(ε, λ) ∈ ℕ such that for every n ≥ n0(ε, λ) and every p ∈ ℕ
If a probabilistic metric spaceis such that every Cauchy sequence {x n } n ∈ℕin S converges in S, thenis a complete space.
In [10], a class of t-norms is introduced, which is useful in the fixed point theory in probabilistic metric spaces.
Let T be a t-norm and T n : [0, 1] → [0, 1] (n ∈ ℕ) be defined in the following way:
We say that t-norm T is of H-type if the family {T n (x)} n ∈ℕ is equicontinuous at x = 1.
One of the most important results for the fixed point theory in metric space (M, d) is the Banach contraction principle.
A mapping f : M → M is said to be a q-contraction if there exists q ∈ [0, 1) such that
for every x, y ∈ M.
Every q-contraction f : M → M on a complete metric space (M, d) has one and only one fixed point.
Sehgal and Bharucha-Reid introduced in [11] the notion of a probabilistic q-contraction (q ∈ (0, 1)) in probabilistic metric space.
Definition 7 Letbe a probabilistic metric space. A mapping f : S → S is a probabilistic q-contraction if
for every p1, p2 ∈ S and every x ∈ ℝ.
The first fixed point theorem in probabilistic metric space was proved by Sehgal and Bharucha-Reid in [11].
Theorem 2 Let be a complete Menger space and f: S → S a probabilistic q-contraction. Then, there exists a unique fixed point x of the mapping f and for every p∈ S.
In [12], Mihet introduced the following definition.
Definition 8 Letbe a probabilistic metric space. A mapping f: S → S is said to be a q-contraction of (ε, λ)-type, if the following implication holds for every p1, p2 ∈ S:
It is obvious that (1) implies that f is a probabilistic q-contraction.
In [12], the following theorem was proved.
Theorem 3 Letbe a complete Menger space and f: S → S a q-contraction of (ε, λ)-type. Then, there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
In [8], Hadžić and Pap proved that Mihet's theorem holds for a more general class of Menger space. In order to formulate the theorem, the notion of geometrical convergence of a t-norm is defined.
Definition 9 We say that a t-norm T is geometrically convergent if
for every q ∈ (0, 1).
Theorem 4 Let be a complete Menger space such that and f: S → S a q-contraction of (ε, λ)-type. If T is geometrically convergent, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
In this paper, the definition of a strong (b n )-contraction is introduced. Using the theory of countable extension of a t-norm given in [8], we prove a fixed point theorem where the mapping f : S → S is a strong (b n )-contraction, and is a complete Menger space and T satisfies an additional condition. In corollaries, we prove that this condition is satisfied if T belongs to the class of t-norms of Dombi, Aczé l-Alsina and Sugeno-Weber.
2 Countable extension of t-norms
Each t-norm T can be extended (by associativity see [13]) in a unique way to an n ary operation taking for (x1,..., x n ) ∈ 0[1]n the values
A t-norm T can be extended to a countable infinitary operation taking for any sequence (x n ) n ∈ℕ from [0, 1] the value
The sequence is nonincreasing and bounded from below, and hence, the limit exists.
In the fixed point theory (see [8]), it is of interest to investigate the classes of t-norms T and sequences (x n ) from the interval [0, 1] such that and
It is obvious that
for T = T L and T = T p .
For T ≥ T L , we have the following implication
Important classes of t-norms are given in the following example.
Example 2 (i) The Dombi family of t-norms is defined by
(ii) The Aczél-Alsina family of t-norms is defined by
(iii) The family of Sugeno-Weber t-norms is given by
(iv) The Schweizer-Sklar family of t-normsis defined by
The condition T ≥ T L is fulfilled by the families , .
On the other side, there exists a member of the family which is incomparable with T L , and there exists a member of the family which is incomparable with T L .
In [8], the following results are obtained:
-
(a)
If is the Dombi family of t-norms and (x n ) n ∈ℕ a sequence of elements from (0, 1] such that , then we have the following equivalence
(3) -
(b)
If is the Sugeno-Weber family of t-norms and (x n ) n ∈ℕ a sequence of elements from (0, 1] such that , then we have the following equivalence
(4) -
(c)
The equivalence (3) holds also for the family , i.e.,
(5)
In [8], the following Proposition is obtained.
Proposition 1 Let (x n ) n ∈ℕbe a sequence of numbers from [0, 1] such thatand t-norm T is of H-type. Then,
In the fixed point theory in probabilistic spaces, it is of interest to investigate condition (2) for a special sequence (1 - qn ) n ∈ℕ for q ∈ (0, 1).
In [14], the following Proposition is proved.
Proposition 2 If for a t-norm T there exists q0 ∈ (0, 1) such that
then
for every q ∈ (0, 1).
In [8] the following definition is given.
Since and for every s > 0 it follows that all t-norms from the family
are geometrically convergent, where is the class of all t-norms of H-type.
3 A fixed point theorem
Definition 10 Let be a probabilistic metric space and(b n ) n ∈ℕa sequence from (0, 1) such that. The mapping f: S → S is a strong (b n )-contraction if the following implication holds
Each q-contraction (ε, λ)-type is a strong b n -contraction, where q ∈ (0, 1), if a sequence (b n ) n ∈ℕ is defined in the following way:
Theorem 5 Let be a complete Menger space and(b n ) n ∈ℕa sequence from (0, 1) such that. If t-norm T satisfies the following condition
and f : S → S is a strong (b n )-contraction, then there exists a unique fixed point x ∈ S of the mapping f andfor every p ∈ S.
Proof: We will show that mapping f : S → S is uniformly continuous since f is a strong (b n )-contraction. Let δ > 0 and λ ∈ (0, 1) be given. Since , there exists m ∈ ℕ such that b m +1 > 1 - λ. Let , q ∈ (0, 1).
Then, the following implication holds
Let N(ε, λ) = {(u, v): u, v ∈ S, F u,v (ε) > 1 - λ}.
It follows that which means that a mapping f is uniformly continuous.
We shall prove that a sequence (fnp) n ∈ℕ is Cauchy sequence for every p ∈ S, i.e., that for every ε > 0 and λ ∈ (0, 1), there exists n0(ε, λ) ∈ ℕ such that for every n > n0(ε, λ) and r ∈ ℕ the following condition is satisfied
Since t-norm T satisfies the condition , it follows that there exists m0 ∈ ℕ such that
Let p ∈ S. Since F p,fp ∈ Δ+, there exists η such that
The mapping f is a strong (b n )-contraction and (8) implies
Continuing in this way, we got that for every k ∈ ℕ
Let k0 ∈ ℕ be such that and k0 > m0. Then for every l ∈ ℕ and every r ≥ 2, it follows
This means that the sequence (fnp) n ∈ℕ is a Cauchy, and since the space is complete, there exists x ∈ S such that . From the continuity of the mapping f, it follows that x = fx. Let y = fy, for y ∈ S. It remains to be proved that x = y, i.e., we have to prove that F x,y (ε) > 1 - λ for every ε > 0 and every λ ∈ (0, 1). Let ε > 0 and λ ∈ (0, 1) be given. From the condition , it follows that there exists m ∈ ℕ such that b m > 1 - λ. Since F x,y ∈ Δ+, there exists δ > 0 such that F x , y (δ) > b m . Because q ∈ (0, 1), there exists k0 such that ε > qkδ, for every k ≥ k0 and there exists k1 > k0 such that . From the Definition 10, it follows .
The following Corollary is in fact Theorem 4.
Corollary 1 Let be a complete Menger space and f: S → S is a q-contraction (ε, λ)-type. If t-norm T is geometrically convergent, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
Proof: Let (b n ) n ∈ℕ be a sequence defined in the following way
m ∈ ℕ, for some λ ∈ (0, 1). Since λ ∈ (0, 1), it follows that qiλ < qi and so
From that it follows that , and the whole conditions of previous theorem are satisfied.
Remark In [12], Mihet introduced the following definition.
Definition 11 Let be a probabilistic space and(b n ) n ∈ℕa sequence in (0, 1) such that b n ↗ 1. We say that the mapping f : S → S is a (b n )-contraction if for all n ∈ ℕ, there exists k n ∈ (0, 1) such that for every p, q ∈ S and every t > 0
In [12], Mihet proved that a b n -contraction in a complete Menger space under a t-norm T = T H has a unique fixed point.
The class of strong (b n )-contraction is strictly included in the class of (b n )-contraction (for more details see [15]). For future work, it would be interesting to combine Theorem 5 with the result given above, i.e., to use (b n )-contraction as the mapping and a more general class of t-norms (for example, the one used in Theorem 5).
Corollary 2 Let be a complete Menger space and(b n ) n ∈ℕa sequence from (0, 1) such that. If and f: S → S is a strong (b n )-contraction, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
Proof: From equivalence (3), we have
Since is satisfied, all the conditions of previous theorem are fulfilled.
Corollary 3 Let be a complete Menger space and(b n ) n ∈ℕa sequence from (0, 1) such that. If and f: S → S is a strong (b n )-contraction, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
Proof: From equivalence (5), we have
Since is satisfied all the conditions of previous theorem are fulfilled.
Corollary 4 Let be a complete Menger space and(b n ) n ∈ℕa sequence from (0, 1) such that. If and f: S → S is a strong (b n )-contraction, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
Proof: From equivalence (4), we have
Since is satisfied, all the conditions of previous theorem are fulfilled.
Corollary 5 Let be a complete Menger space and(b n ) n ∈ℕ a sequence from (0, 1) such that. If t-norm T is of H-type and f: S → S is a strong (b n )-contraction, then there exists a unique fixed point x ∈ S of the mapping f and for every p∈ S.
Proof: The proof results from the Proposition 1 directly.
4 Probabilistic metric space related to decomposable measure
Let be a σ-algebra of subsets of a given set Ω. A classical measure is a set function such that m(∅) = 0 and
for every sequence (A i ) i ∈ℕ of pairwise disjoint set from .
Definition 12 Let S be a t-conorm. A-decomposable measure m is a set function such that m(∅) = 0 and
for everyand A ∩ B = ∅.
A measure m is of (NSA)-type if and only if s ○ m is a finite additive measure, where s is an additive generator of the t-conorm S, which is continuous, nonstrict, and Archimedean and with respect to which m is decomposable (s(1) = 1).
Proposition 3 Let be a measure space, where m is a continuous decomposable measure of (NSA)-type with monotone increasing generator s . Then, is a Menger space, where and t-norm T are given in the following way :
(for every),
for every x, y ∈ [0, 1].
Proposition 4 Let be as in Proposition 3 and(M, d) be a complete separable metric space. Then, from Proposition 3 is a complete probabilistic metric space.
Example 3 Let be a probability measure space, (M, d) a separable metric space, and the family of Borel subsets of M. A mapping f: Ω × M → M is a random operator if for every and every x∈ M
i.e., if the mapping ω ↦ f(ω, x) is measurable on Ω. A random operator f : Ω × M → M is continuous if for every ω ∈ Ω the mapping x ↦ f(ω, x) is continuous on M.
If f : Ω × M → M is a continuous random operator, then for every measurable mapping X : Ω → M the mapping ω ↦ f(ω, X(ω)) is measurable on Ω.
Let S be the set of all equivalence classes of measurable mappings X : Ω → M and let f be a continuous random operator. The mapping, defined by
is the so-called Nemytskij operator of f. If f : Ω × M → M is a random operator, then a measurable mapping X : Ω → M is a random fixed point of the mapping f if
If f is a continuous random operator, then (12) holds if and only if, In this case, the problem of the existence of a random fixed point of a continuous random operator f reduces to the problem of the existence of a fixed point of the Nemytskij operatorof f.
Corollary 6 Let be a measure space, where m is a continuous decomposable measure of (NSA)-type, s is a monotone increasing additive generator of, (M, d) a complete separable metric space and f : Ω × S → M a random operator such that for some q ∈ (0, 1) and every measurable mappings X, Y : Ω → M
where (b n ) n ∈ℕis a sequence from (0, 1) such thatand t-norm T defined by
satisfies condition
then there exists a random fixed point of the operator f.
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Acknowledgements
This research was supported by MNTRRS-174009 and Serbian Ministry of Education and Science project III 44006.
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5 Authors' contributions
T. Došenović has formulated Theorem 5 and Definition 10 which are the base for this paper. Also, together with A. Takači she has the Corollary's 1-4 of this theorem. Moreover, together with D. Rakić, M. Brdar Corollary 6 was formulated. The proofs are the joint contribution of all authors.
Finally, the authors declare that they have no competing interest considering the publication of this paper.
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Došenović, T., Takači, A., Rakić, D. et al. A fixed point theorem for a special class of probabilistic contraction. Fixed Point Theory Appl 2011, 74 (2011). https://doi.org/10.1186/1687-1812-2011-74
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DOI: https://doi.org/10.1186/1687-1812-2011-74