1. Introduction
Let be a non empty set and be a self mapping. A point is called a fixed point of f if . If d is a metric on , then f is said to be contraction if there is such that , for each
The novelty of fixed point theory in distance spaces appeared in 1922 by Banach [
1] and known later by Banach contraction principle which asserts that a contraction on a complete metric space has a unique fixed point. Subsequently, several generalizations for this result are investigated, either by modifying the contraction conditions or by changing the setting of the distance spaces, for example see [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14].
One well known generalization of metric spaces is
b-metric spaces which were introduced by Bakhtin [
15] and improved and named by Czerwik [
16]. Then, it is used to investigate many fixed point results in the literature. This generalization enriched the fixed point theory in various ways: theorems, applications and many results. On the other hand, some authors generalized the notion of
b-metric spaces to some spaces such as extended
b-metric spaces, extended quasi
b-metric spaces and
-distance mappings which were introduced by Kamran et al. [
17], Nurwahyu [
18] and Hussain et al. [
19], respectively. For a deeper knowledge concerning distance spaces and fixed point theory and functional analysis, we refer the reader to [
20,
21,
22]
Henceforth, we consider the following notations: the set of reals, the set of naturals, the set of complex numbers, the set of all matrices with complex entries and for a self mapping , the set of all fixed points of f in .
2. Preliminary
The definition of b-metric spaces is given as following:
Definition 1. [15] A function is said to be b-metric if there is such that b satisfies: - (b1)
iff ,
- (b2)
for all ,
- (b3)
for all .
The pair is called a b-metric space.
The notion of
-distance mapping was introduced by Hussain et al. [
19] in 2014 and given as following:
Definition 2. [19] A function is said to be an -distance over a b-metric space if ω satisfies: - (ω1)
for all ,
- (ω2)
is s-lower semi-continuous for all ,
- (ω3)
for any , there is such that
From now on, is referred to a b-metric space, and is referred to an -distance mapping over .
It is obviously that, every b-metric is an -distance mapping.
Lemma 1. [19] On , suppose we have two sequences and in . Let and ( be sequences in such that and . Then: - 1.
If and for all , then .
- 2.
If and for all , then .
- 3.
If for all with , then is a Cauchy sequence.
- 4.
If for all , then is a Cauchy sequence.
Definition 3. [23] Let Φ denote the set of all functions that satisfy: ϕ is non decreasing and continuous on ,
for all , .
Remark 1. [23] If , then and for all . Definition 4. [24] Let denotes the set of all functions that satisfies: θ is non decreasing and continuous on ,
for each sequence in , if and only if ,
there exist and such that .
In this manuscript, we consider the class to be defined as following:
Definition 5. Let Θ denote the set of all continuous functions that satisfy:
θ is non decreasing on ,
for each sequence in , if and only if .
Remark 2. If , then
In 2015, Khojasteh et al. [
25] introduced the concept of simulation functions in which they used it to unify several fixed point results in the literature. Then, significant results in fixed point theory using simulation functions were obtained, for example see [
26,
27,
28,
29,
30,
31,
32]
Definition 6. [25] A function is called a simulation function if it satisfies the following:
for all ,
If and are sequences in such that , then 0.
Seong-Hoon Cho [
33] introduced the following class of functions, namely
-simulation functions and some new type of contractions by using
-simulation functions:
Definition 7. A function is called -simulation function if it satisfies the following:
() ,
() ,
() For each sequences in , with for all implies .
3. Main Results
We begin our work with the definition of -simulation functions and some examples on this notion. Then, we introduce the notion of -contractions with respect to to derive some results.
Definition 8. A function is called -simulation if for all
We denote by the set of all -simulation functions.
Remark 3. Let . If are sequences in with , then
Now, we provide some examples on -simulation functions.
Example 1. The following functions belong to :
- 1.
,
- 2.
,
- 3.
,
- 4.
,
- 5.
,
- 6.
Let be continuous functions such that and , for each . Define
Note: Every -simulation function is -simulation while the converse isn’t true in general as we can see in the following example.
Example 2. Consider the function which is defined as Then and .
Clearly for all and so,
To show that , consider the sequences in such that , and . Then for all and while | |
| |
| |
| |
Note: and described in Example 1 are not members of .
Definition 9. Suppose there is ω over with . A self mapping
is said to be -contraction with respect to μ if there exist and such that Lemma 2. If f is -contraction, then for all we have the following:
- 1.
implies that
- 2.
implies that
Proof. (1) Suppose
. Then Condition
1 implies that
1 | |
| |
| |
| |
| |
So Since is non-decreasing, we have and so, we get the result.
(2) Suppose
. By Condition
1, we have
Hence the result. □
Lemma 3. Suppose there is ω over with . Let be an -contraction with respect to . Then contains at most one element.
Proof. Assume that there are
. First, we claim that
. If
, then Lemma 2 implies that
a contradiction and so
. Similarly, we can get that
.
and hence
. □
On , let and be a self mapping. Then we call the sequence , where the Picard sequence generated by f at
Lemma 4. Suppose there is ω over with . Let be an -contraction with respect to . Then for any initial point , where is the Picard sequence generated by f at .
Proof. Let be the Picard sequence generated by f at . If there is such that 0, then by Lemma 2, we get that for all .
Assume that
for all
. By Lemma 2, we have
Thus
is a non increasing sequence in
. There is
such that
. Suppose to the contrary; that is,
. Let
and
Then
. By (
1) and Remark 3, we have
a contradiction.
. By the same way we can show that
0. □
Lemma 5. Suppose there is ω over with . Let be an -contraction with respect to . If there is with then . In addition, if there is with , then .
Proof. The proof follows from part (a) and part (c) of the definition of . □
Theorem 1. Suppose is complete with base . Suppose that there are and such that is an -contraction with respect to such that: Then consists of only one element. Moreover, the sequence , where converges for any and .
Proof. Let and consider the Picard sequence in generated by f at . According to Lemma 5, if there exists such that or , then or , respectively. Therefore, we may assume that for each , and . By Lemma 4, we have and Now, we want to show that , i.e. is a Cauchy sequence.
Assume the contrary; that is,
Thus there are
and two sub-sequences
and
of
such that
is chosen as the smallest index for which
Set
. By Lemma 2, Equations (
4) and (
5) and (
) of the definition of
, we get
| |
| |
| |
By taking the limit inferior as
and taking into account Equation (
2), we get
In addition,
| |
| |
| |
| |
By taking the limit superior as
and taking into account (
2), we get
By Equations (
6) and (
7), we get
Now, set
. By Lemma 2, we get
By taking the limit superior to both sides, we get
On the other hand, we have
| . |
| |
By taking the limit inferior to both sides, we get
By Equations (
9) and (
10), we get
By the properties of
and
, we get
Now, by letting
and
then
Remark 3 and Condition (
1) yield that
which is a contradiction. Therefore
Thus Lemma 1 implies that
is Cauchy. There is
such that
.
Since
, then for any
there is
such that
The lower semi-continuity of
implies that
Suppose that
. Then we have
0 | |
| |
| , |
for every
which is a contradiction. Therefore
The uniqueness of
follows from Lemma 3. □
Corollary 1. Suppose is complete with base , and there is ω over . Suppose that there are real numbers and such that satisfies the following condition:
In addition, assume that if and , then Then consists of only one element.
Proof. Define
,
and
by
,
and
, respectively. Then
and
. We now show that
f is an
-contraction with respect to
. From Condition (
12), we have
iff
iff
iff
iff
iff
Hence the result follows from Theorem 1. □
Corollary 2. Suppose is complete with base , and there is ω over . Suppose that there is a real number such that satisfies the following condition:
In addition, suppose that if if , then Then consists of only one element.
Proof. Define
,
and
by
,
and
, respectively. Then
and
. We now show that
f is an
-contraction with respect to
. From Condition (
14), we have
iff
iff
iff
iff
iff
iff
Hence the result follows from Theorem 1. □
4. Examples
Next, we illustrate our result by some examples.
Example 3. Suppose . Let f be a self mapping on via with α is real number in . To show that consist of only one element. Define via and via . In addition, define by .
Moreover, define via and by . Then is complete b-metric space with and ω is -distance mapping over , , and .
Now, we show that f is an -contraction with respect to μ; i.e., Now, for all , we have Utilizing Theorem 1, we get consists of only one element.
Example 4. Let . Define by . Additionally, define by and . Moreover, define via and by . Then the function which is defined by has a unique fixed point in
Proof. It is clearly that
is a complete b-metric space with , and also, is -distance mapping over .
(see Example 2).
and .
To show that
consists of only one element, it suffices to show that
i.e., we want to show that
Hence, Theorem 1 ensures that consists of only one element. Using MATLAB, we can find that the fixed point of g is □
5. Applications
In this section, we highlight the novelty of our work by introducing some applications by utilizing Theorem 1.
Next, we show that for any real number
, the equation
has a unique solution in [0,1].
Theorem 2. Let . Define by . Additionally, define by and . Moreover, define via and by . Then for the function which defined by , the set consists of only one element.
Proof. It is obviously that:
is a complete b-metric space with . In addition, is an -distance mapping over .
(see Example 2).
and .
To show that
consists of only one element, it suffices to prove that
which is equivalent to prove that
Hence, Theorem 1 ensures that
consists of only one element. There is
such that
; i.e.,
. Hence Equation (
17) has a unique solution. □
Now, we use Theorem 1 to confirm that for all
, for
, the matrix equation
where
has a unique solution.
Let and consider the spectral norm which known as where are the singular-values of A. Clearly is a Banach space since is a finite dimensional norm space.
Theorem 3. Let for be such that . Then the matrix in Equation (20) has a unique solution in . Moreover, for any matrix , the sequence converges to the solution of Equation (20). Proof. Let be defined as and Then, clearly b is a b-metric on with base and is an -distance mapping. Let and be defined as following: , and . Define by .
Now, we prove that
f is
-contraction with respect to
. To see this, let
. Then,
| |
| |
| |
| |
| |
| |
| |
| |
. Hence,
. Consequently,
consists of only one element. The matrix in Equation (
20) has a unique solution. □
To illumine our application, consider the following example
Example 5. Let be given as following:
One can find that . Theorem 3 implies that the matrix equation has a unique solution, and the sequence , for converges to the unique solution for any initial matrix .
For instance, if we start at initial matrix we find the solution using MATLAB at the 10th iteration which is