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Near-coincidence point results in metric interval space and hyperspace via simulation functions
Advances in Difference Equations volume 2020, Article number: 291 (2020)
Abstract
Recently, Wu (Mathematics 6(11):219, 2018; Mathematics 6(6):90, 2018) introduced the concept of a near-fixed point and established some results on near fixed points in a metric interval space and hyperspace. Motivated by these papers, we studied the near-coincidence point theorem in these spaces via a simulation function. To show the authenticity of the established results and definitions, we also provide some examples.
1 Introduction and preliminaries
In mathematics, fixed point theory plays the role of bridge between pure and applied mathematics. Therefore this field has great importance among the branches of pure mathematics and especially in nonlinear analysis. It has many applications to the existence of a solution of a nonlinear system; see, for example, the recent works [3–9] and many more. Metric fixed point theory is the celebrated area in fixed point theory based on the Banach contraction principle (BCP). This principle is given by Banach [10]. After him, this principle was generalized in different forms. This principle is studied in different structures such as dislocated quasimetric spaces [11], cone metric spaces [12], generalized metric spaces [13] and so on. On the other side, the contraction condition is modified in different forms such as the Kannan contraction condition, Chatterjee contraction condition. For details, see [14–16]. Sarwar [17] studied fixed point theorems under rational-type contractions in the setting of complex-valued metric spaces. These results generalized some important results in the present literature. De la Sen [18] used a new approach of \((s-q)\)-graphic contraction in b-metric-like spaces. These results generalize and improve several approaches in the existing literature by using this new approach for the proof that a Picard sequence is a Cauchy one.
Recently, Khojasteh [19] introduced the concept of a simulation function \(\zeta : [0,+\infty ) \times [0,+\infty ) \longrightarrow \mathbb{R}\) and the concept of Z-contraction, which modifies the contraction condition in the Banach contraction principle. Using a simulation function, he proved some fixed-point results. Then Hiero et al. [20, 21] extended the stated concept of a simulation function given by Khojasteh and investigated some coincidence point results. Argubi [22] used this concept to study the results on coincidence and common fixed point in partially ordered metric space. Alharbi [23] combined the concept of a simulation function with admissible function to generalize some existing results in the related literature. Chanda [24] surveyed many of the recent works related with simulation functions and Z-contractions in the existing literature after the publication of Khojasteh et al. Recently, Alsubaie, Alqahtani, and Karapinar [25] proved some interesting results on common fixed points in metric spaces via a simulation function. In 2019, Alqathani and Karapinar [26] introduced the concept of a bilateral contraction, which combines the ideas of Ćirić-type contraction and Caristi-type contraction with the help of simulation function in complete metric spaces. Alghamdi [27] studied common fixed point results in the setting of b-metric space via extended Z-contraction with respect to a ψ-simulation function. His work evaluated and merged as-scattered-as-possible results in fixed point theory from general framework. Karapinar and Agarwal [28] introduced the concept of an interpolative Rus–Reich–Ćirić-type Z-contraction in the setting of a complete metric space.
Recently, Wu [1, 2] raised the idea about near-fixed points in metric interval spaces and hyperspaces. He studied some results on near-fixed points in metric interval spaces and hyperspaces. Due to the nonexistence of the inverse of each element, the metric interval spaces and hyperspaces are not conventional metric spaces and normed spaces, respectively. For more detail of interval spaces, we refer the reader to [29, 30]
Inspired by the works [19, 25–28, 31, 32], we study near-coincidence points in metric interval spaces and hyperspaces via simulation functions. We also provide some examples.
2 Preliminaries
We state some basic definitions and fundamental results in this framework.
2.1 Interval space
Let I be the collection of all closed bounded intervals \([l,u]\), where \(l,u \in \mathbb{R}\) and \(l \leq u\); we consider \(l \in \mathbb{R}\) as the element \([l,l] \in I\).
The addition and the scaler multiplication are defined as
Under these two operation, the inverse of any nondegenerate closed interval does not exist in I, so I cannot form the conventional metric space. Clearly, we can see that the additive identity in I is \([0,0]\). However, for \([l,u] \in I\), the subtraction \([l,u] \ominus [l,u]=[l,u]\oplus [-u,-l]=[l-u,u-l]\) does not give a zero element. So the element \([l,u]\) has no inverse element (for more detail, see [1]).
2.2 Null set
The null set is defined as
or
In the interval spaces, the following observations are remarkable:
The distributive law in I is not true in general.
$$ (\alpha +\beta )[t,u]\neq \alpha [t,u] \oplus \beta [t ,u] \quad \text{for any }[t,u] \in I \text{ and }\alpha , \beta \in \mathbb{R}. $$The distributivity holds for scaler addition if both scalers are positive or both are negative, that is,
$$ (\alpha +\beta )[t,u]= \alpha [t,u] \oplus \beta [t,u] \quad \text{for any } [t,u] \in I, \alpha , \beta > 0, \text{ and } \alpha , \beta < 0. $$For any \([p,q],[r,s],[t,u] \in I\), we have
$$\begin{aligned}{} [t,u]\ominus \bigl([p,q]\oplus [r,s]\bigr) =&[t,u]\ominus [p,q]\ominus [r,s] \\ =&[t,u]\oplus \bigl(-[p,q]\oplus \bigl(-[r,s]\bigr)\bigr). \end{aligned}$$We write \([t,u]\stackrel{\varOmega}{=}[r,s]\) iff there exist \(\omega _{1},\omega _{2} \in \varOmega \) such that \([t,u]\oplus \omega _{1}=[r,s]\oplus \omega _{2}\).
Clearly, \([t,u]=[r,s]\) implies \([t,u]\stackrel{\varOmega}{=} [r,s]\) by taking \(\omega _{1}=\omega _{2}=[0,0]\); however, generally, the converse is not true. Under the relation \(\stackrel{\varOmega}{=}\), for any \([p,q] \in I\), the equivalence class is defined as \(\langle[p,q]\rangle = \{[x,y] \in I : [p,q]\stackrel{\varOmega}{=}[x,y] \}\) (for more detail, see [1]).
2.3 Metric interval space
A metric interval space is a pair \((I, d)\), where I is the collection of all closed bounded intervals in \(\mathbb{R}\) with the null set Ω, and d is a mapping from \(I\times I\) to nonnegative real numbers that satisfies the following axioms:
- (i)
\(d([p,q],[r,s])=0\) if and only if \([p,q]\stackrel{\varOmega}{=}[r,s]\) for all \([p,q],[r,s] \in I\);
- (ii)
\(d([p,q],[r,s])=d([r,s],[p,q])\) for all \([p,q],[r,s] \in I\);
- (iii)
\(d([p,q],[r,s])\leq d([p,q],[t,u])+d([t,u],[r,s])\) for all \([p,q],[r,s],[t,u] \in I\);
If only conditions (ii) and (iii) hold, then the space \((I,d)\) is called a pseudometric interval space.
If the following condition (iv) is satisfied for d, then d is said to satisfy the null equalities:
- (iv)
for any \(\omega _{1},\omega _{2} \in \varOmega \) and \([l,u],[x,y]\in I\), the following equalities hold:
- (a)
\(d([l,u]\oplus \omega _{1},[x,y]\oplus \omega _{2})=d([l,u],[x,y])\);
- (b)
\(d([l,u]\oplus \omega _{1},[x,y])=d([l,u],[x,y])\);
- (c)
\(d([l,u],[x,y]\oplus \omega _{2})=d([l,u],[x,y])\).
For more detail, see [1].
- (a)
Example 2.1
Let I be the collection of all closed bounded intervals in \(\mathbb{R}\), and let d be the function from \(I\times I\) to \(\mathbb{R}^{+}\) given by
Then \((I,d)\) is a metric interval space with the null equalities satisfied by d (see [1]).
Definition 2.2
Let g be a self-mapping on a metric interval space \((I,d)\), Then \([l,u] \in I\) is called a near-fixed point of g if \(g[l,u]\stackrel{\varOmega}{=}[l,u]\) (see [1]).
Definition 2.3
The sequence \(\{[l_{n},u_{n}]\}_{n=1}^{+\infty }\) in the space \((I,d)\) is convergent in I if
If there exists a point \([x,y]\) such that
or
(For more details see[1].)
Definition 2.4
([1])
If the limit of the sequence \(\{[l_{n},u_{n}]\}_{n=1}^{+\infty }\) is \([l,u]\), then the class \(\langle[l,u]\rangle \) is said to be the class limit for \(\{[l_{n},u_{n}]\}_{n=1}^{+\infty }\).
We write
Definition 2.5
([1])
Consider the sequence \(\{[l_{n},u_{n}]\}_{n=1}^{+\infty }\) in \((I,d)\) such that for any \(\epsilon > 0\), there exists a natural number N such that \(d([l_{n},u_{n}],[l_{m},u_{m}]) < \epsilon \) for all \(n,m > N\). Then the sequence is called a Cauchy sequence.
Definition 2.6
([1])
If every Cauchy sequence is convergent to a point in a subset J of the matric interval space \((I,d)\), then the subset J is said to be complete.
Definition 2.7
A function \(S : [0,+\infty )\times [0,+\infty )\rightarrow \mathbb{R}\) is said to be a simulation function if the following conditions holds for S:
- \(S_{1}\).:
\(S(0,0)=0\);
- \(S_{2}\).:
\(S(r,s) < s-r \) for all \(r,s > 0\);
- \(S_{3}\).:
If \(\{r_{n}\}\), \(\{s_{n}\}\) are two sequences in \((0,+\infty )\) such that \(\lim_{n \rightarrow +\infty }r_{n}=\lim_{n \rightarrow +\infty }s_{n} > 0\) and \(r_{n} < s_{n}\) for all \(n \in \mathbb{N}\), then
$$ \lim_{n \rightarrow +\infty }\sup S(r_{n},s_{n}) < 0. $$
By \(S_{2}\) we clearly can say that a simulation function must satisfy
Some examples of simulation functions are:
- i.
\(S (x,y)=\phi (y)-\psi (x)\) for all \(x, y \in [0,+\infty )\), where ϕ and ψ are continuous function on \([0,+\infty )\) such that \(\psi (x)=\phi (x)\) iff \(x=0\) and \(\psi (x) < x\leq \phi (x)\) for all \(x > 0\).
Particularly, if we take \(\psi (y)=\lambda y\) and \(\phi (x) = x\), then \(S (x,y)=\lambda y- x\).
- ii.
\(S (x,y)=y-\phi (y)-x\) for all \(x, y \in [0, +\infty )\), where ϕ is a continuous function on \([0, +\infty )\) such that \(\phi (x)=0\) iff \(x=0\) (see [19], Example 2.2).
- iii.
\(S (x,y)=y \psi (y) -x\) for all \(x, y \in [0, +\infty )\), where ψ is a mapping such that \(\lim_{x \rightarrow r^{+}} \psi (t) < 1\) for all \(r > 0\) [20].
- iv.
\(S (x,y)=\eta (y)-x\) for all \(x,y \in [0, +\infty )\), where η is a function which is upper semicontinuous, and \(\eta (x) < x\) for all \(x > 0\), and \(\eta (0)=0\) [20].
2.4 Hyperspace
Let \(S(V)\) be the collection of all nonempty convex subsets of V, where V is a topological vector space. The two binary operations of addition and scalar multiplication are defined as follows:
The subtraction is defined by
Clearly, if \(0_{V}\) is the zero element in V, then \(\{0_{V}\}\) is the zero element in \(S(V)\) because \(\{0_{V}\} \oplus U = U\).
Here in \(S(V)\) the inverse does not exist for a nonempty and nonsingleton set, that is, if \(\varPhi \neq U \neq \{0_{V}\}\), then \(U \ominus U\) is not the zero element of \(S(V)\), and so it cannot be a conventional vector space (for more detail, see [2]).
2.5 Null set
The null set is defined as \(\varOmega = \{ U \ominus U ; U \in S(V) \}\). This set can be regarded as the zero element of \(S(V)\) (see [2]).
Remark 2.8
([2])
-
1
\((U \oplus V)\oplus W=U \oplus (V \oplus W) \).
-
2
\(\lambda (U \oplus V) = \lambda U \oplus \lambda V \) for \(\lambda \in \mathbb{R.}\)
-
3
\(\lambda _{1} (\lambda _{2} U) =(\lambda _{1} \lambda _{2})U\) for \(\lambda _{1}\lambda _{2} \in \mathbb{R.}\)
-
4
If U is a convex subset of V and \(\lambda _{1}\) and \(\lambda _{2}\) have the same sign, then \((\lambda _{1} + \lambda _{2}) U = \lambda _{1} U \oplus \lambda _{2} U\).
Proposition 2.9
([2])
For the null setΩ, we have:
- i
\(\{0_{V}\} = 0_{S(v)}\)belongs to the null set.
- ii
If\(\omega \in \varOmega \), then\(-\omega = \omega \).
- iii
\(\mu \varOmega = \varOmega \)for\(\mu \in \mathbb{R}\)where\(\mu \neq 0\).
- iv
If\(\omega _{1}, \omega _{1} \in \mathbb{R}\), then\(\omega _{1} \oplus \omega _{2} \in \varOmega \).
Definition 2.10
([2])
For any \(U, U^{*} \in S(V)\), we write \(U \stackrel{\varOmega}{=} U^{*} \) if there exist \(\omega _{1}, \omega _{2} \in \varOmega \) such that \(U \oplus \omega _{1} = U^{*} \oplus \omega _{2}\). The sets U and \(U^{*}\) are said to be almost identical. Clearly, \(U = U^{*}\) implies \(U \stackrel{\varOmega}{=} U^{*}\)
If U, \(U^{*}\), and W are not a singleton set, then \(U \ominus U^{*} = W\) implies \(U \stackrel{\varOmega}{=} U^{*} \oplus W\).
2.6 Normed hyperspace
Let V be a vector space, and let \(S(V)\) be the collection of all nonempty convex subsets of V. Then \(S(V)\) is called a normed hyperspace if there exists a length function \(\|\cdot \|: S(V) \rightarrow \mathbb{R}\) satisfying the following axioms:
- (i)
\(\| a U\|=|a| \|U\|\) for all \(U \in S(V)\) and \(a \in \mathbb{F}\).
- (ii)
\(\|U \oplus U^{*}\| \leq \|U\| + \|U^{*}\|\) for all \(U, U^{*} \in S(V)\).
- (iii)
\(\|U\|=0\) implies \(U \in \varOmega \).
If condition (iii) is replaced by \(\|U\|=0\) iff \(U \in \varOmega \), then we say that \(\|\cdot \|\) satisfies the null condition (for more detail, see [2]).
Example 2.11
([2])
Consider a conventional norm space V with conventional norm \(\|\cdot \|_{V}\). Let \(S(V)\) be the collection of all nonempty convex subsets of V. Then the norm defined on \(S(V)\) is given as
Proposition 2.12
([2])
Let\((S(V), \|\cdot \|)\)be a pseudoseminormed hyperspace such that the null superinequality holds for\(\|\cdot \|\). For any\(A, C, B_{1},B_{2}, \ldots,B_{m} \in S(V)\), we have
Proposition 2.13
([2])
We have:
- (i)
Let\((S(V), \|\cdot \|)\)be a pseudoseminormed hyperspace such that the null equality holds for\(\|\cdot \|\). If\(U, W \in S(V)\)are such that\(U\stackrel{\varOmega}{=} W\), then\(\|U\| = \|W\|\).
- (ii)
Let\((S(V), \|\cdot \|)\)be a pseudonormed hyperspace. If\(U, W \in S(V)\)are such that\(\|U \ominus W\|=0\), then\(U \stackrel{\varOmega}{=} W\).
- (iii)
Let\((S(V), \|\cdot \|)\)be a pseudonormed hyperspace such that the null superinequality and null condition holds for\(\|\cdot \|\). If\(U, W \in S(V)\)are such that\(U \stackrel{\varOmega}{=} W \), then\(\|U \ominus W\|= 0\).
2.7 Convergent sequence
Let \((S(V),\|\cdot \|)\) be a pseudoseminormed hyperspace (see [2]). A sequence \(\{U_{n}\}_{n=1}^{+\infty }\) in \(S(V)\) is said to converge to \(U \in S(V)\) if
Proposition 2.14
([2])
Let\((S(V),\|\cdot \|)\)be a pseudoseminormed hyperspace with the null setΩ.
- (i)
If the sequence\(\{U_{n}\}_{n=1}^{+\infty }\)converges to bothUand\(U^{*}\), then\(U \stackrel{\varOmega}{=} U^{*} \).
- (ii)
Suppose\(\|\cdot \|\)satisfies the null equality. If the sequence\(\{U_{n}\}_{n=1}^{+\infty }\)in\(S(V)\)converges to\(U \in S(V)\), then for any\(U^{*} \in [U]\), the sequence\(\{U_{n}\}_{n=1}^{+\infty }\)converges to\(U^{*}\).
Definition 2.15
([2])
Let \((S(V),\|\cdot \|)\) be a pseudoseminormed hyperspace. If the sequence \(\{U_{n}\}_{n=1}^{+\infty }\) in \(S(V)\) converges to some \(U \in S(V)\), then the equivalence class \([U]\) is called the class limit of \(\{U_{n}\}_{n=1}^{+\infty }\). We write this mathematically as
Definition 2.16
([2])
Let \((S(V),\|\cdot \|)\) be a pseudoseminormed hyperspace. A sequence \(\{U_{n}\}_{n=1}^{+\infty }\) in \(S(V)\) is called a Cauchy sequence if for any \(\epsilon > 0\), there exists \(N \in \mathbb{N}\) such that
\(S(V)\) is said to be complete if every Cauchy sequence in \(S(V)\) is convergent.
Definition 2.17
([2])
A normed hyperspace \((S(V),\|\cdot \|)\) is called a Banach hyperspace if it is complete.
Definition 2.18
([2])
Let Γ be a self-mapping on \((S(V),\|\cdot \|)\). Then a point \(U \in S(V)\) is called a near-fixed point of Γ if \(\varGamma [U] \stackrel{\varOmega}{=}[U]\).
3 Results and discussion
Definition 3.1
If \(G[l,u]\stackrel{\varOmega}{=}g[l,u]\) for a point \([l,u] \in I\), then \([l,u]\) is called a near-coincidence point for G and g.
Example 3.2
Let us consider two mappings G and g from I to itself defined by
Then every point in Ω, that is, \([-k,k]\), where \(k \in \mathbb{R}^{+}\), is a near-coincidence point for G and g. Particularly, if we take \([-1,1]\), then \(G[-1,1]=[-4,4]\) and \(g[-1,1]=[-3,3]\), so that \([-4,4]\stackrel{\varOmega}{=}[-3,3]\). Hence by definition \([-1,1]\) is a near-coincidence point for G and g.
Definition 3.3
Let G and g be mappings from \((I,d)\) to itself. Then G and g are called compatible mappings if
By taking the functions G and g defined before and any sequence, we can easily verify that these mappings are compatible.
Definition 3.4
Mappings G and g are said to be commuting if \(G g[l,u]\stackrel{\varOmega}{=} g G[l,u]\) for all \([l,u] \in (I,d)\).
Example 3.5
Let us consider previously defined two mappings G and g: \(G[x,y]=[2x-2,2x+2]\) and \(g[x,y]=[x-1,y+1]\) for \(x, y \in \mathbb{R}\) and \(x \leq y\). Then for any \([x,y] \in I\), we can easily show that \(G g[x,y]=[2x-4,2y+4]\stackrel{\varOmega}{=}[2x-3,2y+3]=g G[x,y]\).
To show that \([2x-4,2y+4]\stackrel{\varOmega}{=}[2x-3,2y+3]\), we can take \(\omega _{1}=[0,0]\) and \(\omega _{2} =[-1,1]\), and hence
Definition 3.6
A mapping G is called a \((Z_{d},g)\)-contraction in \((I,d)\) if there is a simulation function \(S \in Z\) such that \(S(d(G[l,u],G[x,y]), d(g[l,u], g[x,y])) \geq 0\) for all \([l,u], [x,y] \in I\), such that \(g[l,u] \stackrel{\varOmega}{\neq} g[x,y]\).
Example 3.7
Let us consider the mappings G and g defined on the metric interval space I by \(G[x,y]=[2x-2,2x+2]\) and \(g[x,y]=[x-1,y+1]\). Take the simulation function as \(S(t,s)=\lambda s-t\) where \(\lambda \geq 2\). Then G is a \((Z_{d},g)\)-contraction in \((I,d)\), because \(S(d(G[l,u],G[x,y]), d(g[l,u], g[x,y])) \geq 0\) for all \([l,u], [x,y] \in I\).
Definition 3.8
A sequence \(\{[l_{n},u_{n}]\}\) is said to be a Picard \((G,g)\) sequence at point \([l_{o},u_{o}]\) for the mapping G and g in the metric interval space \((I,d)\) if \(g([l_{n+1},u_{n+1}])\stackrel{\varOmega}{=}G([l_{n},u_{n}])\) for all \(n \geq 0\). The space \((I,d)\) is said to satisfy the \(\mathrm{CLR}_{(G,g)}\) property.
Example 3.9
Consider the previously defined mappings G and g: \(G[x,y]=[2x-2,2x+2]\) and \(g[x,y]=[x-1,y+1]\) for \(x, y \in \mathbb{R}\) and \(x \leq y\). Then the sequence \(\{[x_{n},y_{n}] \} = \{[2^{n}(x_{o}-1)+1,2^{n}(y_{o}+1)-1]\}\) is a Picard sequence at any point \([x_{o},y_{o}]\), that is, \(g([l_{n+1},u_{n+1}])\stackrel{\varOmega}{=}G([l_{n},u_{n}])\) for all \(n \geq 0\).
Definition 3.10
If g is a mapping over a metric interval space \((I,d)\) with a null set Ω, then g is injective if \(g[l,u] \stackrel{\varOmega}{=} g[l^{\prime },u^{\prime }] \Rightarrow [l,u]\stackrel{\varOmega}{=}[l^{\prime },u^{\prime }]\) for all \([l,u],[l^{\prime },u^{\prime }] \in I\).
Definition 3.11
A point \(U \in S(V)\) is said to be a near-coincidence point for F and g if \(F[U]\stackrel{\varOmega}{=}g[U]\).
Definition 3.12
Let F and g be self-mappings from \(S(V)\) to itself. Then F and g are called compatible mappings if
Definition 3.13
Mappings F and g are said to be commuting if \(F g[U]\stackrel{\varOmega}{=} g F[U]\) for all \(U \in S(V)\).
Definition 3.14
A mapping F is called a \((Z_{\|\cdot \|},g)\)-contraction in \(S(V)\) if there is a simulation function \(\xi \in Z\) such that \(\xi (\|G[U]-G[U^{*}]\|, \|g[U]-g[U^{*}]\|) \geq 0\) for all \(U, U^{*} \in S(V)\) such that \(g[U] \stackrel{\varOmega }{\neq} g[U^{*}]\).
Definition 3.15
A sequence \(\{A_{n},n \geq 0\} \) is called a Picard \((F,g)\) sequence in \(S(V)\) on the point \([A_{o}]\) for the mappings F and g on the hyperspace \(S(V)\) if \(g(A_{n+1})\stackrel{\varOmega}{=}F(A_{n})\) for all \(n \geq 0\). Then the space \(S(V)\) is said to satisfy the \(\mathrm{CLR}_{(F,g)}\) property.
Theorem 3.16
LetGbe a self-mapping that is a\((Z_{d},g)\)-contraction over the complete metric interval space\((I,d)\). Suppose that there exists a Picard sequence for the mappingsGandgat a point\([l,u] \in I\), that is,
Also, assume that the mappingsGandgare continuous and compatible. Then there exists a near-coincidence point forGandg.
Proof
Either the near-coincidence point is contained in the sequence \(\{g[l_{n},u_{n}]\}\), or the sequence \(\{g[l_{n},u_{n}]\}\) approaches to the required near-coincidence point.
Suppose that \(\{g[l_{n},u_{n}]\}\) contains no near coincidence point of G and g, that is,
In such a case,
We will prove the result in three steps.
Step 1
We will show that
Using condition (2) of a simulation function and condition of a \((Z_{d},g)\)-contraction, we have
which implies that
Note that the sequence \({d(g[l_{n},u_{n}],g[l_{n+1},u_{n+1}])}\) is a nonnegative decreasing sequence in \(\mathbb{R}\), so it converges to a point l, that is,
We will show that \(l=0\). Assume that \(l \neq 0\), so \(l > 0 \).
Using \(S_{3} \) by taking the sequences
we clearly have
So by \(S_{3}\) we have \(\lim_{n\rightarrow +\infty } \sup (S(r_{n},s_{n})) < 0\), that is,
which is a contradiction because
So our supposition that \(l \neq 0\) was wrong, and thus \(l=0\). Therefore we have
Step 2
We will show that \(\{g[l_{n},u_{n}]\}\) is a Cauchy sequence in \((I,d)\). On the contrary, suppose that \({g[l_{n},u_{n}]}\) is not Cauchy. So there exists \(\epsilon _{o} > 0\) such that for all \(N \in \mathbb{N}\), there exist positive integers m, n such that
We can construct two subsequences by giving successive values \(g[l_{n_{k}},u_{n_{k}}]\) and \(g[l_{m_{k}},u_{m_{k}}]\) to N such that
Let \(m(k)\) be the smallest positive integer \(m \in \{n(k),n(k)+1,n(k)+2,\ldots \}\) such that (3.2) holds. Now it is clear that
because \(m_{k-1} < m(k)\) and \(m(k)\) is the least number for which (3.2) holds.
Also, \(m(k) > n(k)\) from (3.2), so \(m(k) \geq n(k)+1\) for all \(k \in \mathbb{N}\). Now if \(m(k)=n(k)+1\), then from (3.1) and (3.2) we have
since from (3.1) we have
Hence \(m(k)=n(k)+1\) is not possible by taking into account (3.1) and (3.2), and therefore we conclude that \(m(k) \geq n(k)+2\) for any \(k \in \mathbb{N}\).
It follows that \(n_{k+1} < m_{k} < m_{k+1}\) for all \(k \in \mathbb{N}\). From (3.2) and (3.3) we have
Therefore
Also,
As G is a \((Z_{d},g)\)-contraction associated with S, we get
Thus
Let
and
Clearly, \(r_{n},s_{n} > 0\), \(\lim_{n\rightarrow +\infty } r_{n}=\lim_{n\rightarrow +\infty }s_{n}= \epsilon _{o}\), and \(r_{n}< s_{n}\).
So by \(S_{3}\)
which is a contradiction, and hence \(\{g[l_{n},u_{n}]\}\) is a Cauchy sequence in \((I,d)\).
Step 3 Now as we have proved that the sequence \(\{g[l_{n},u_{n}]\}\) is Cauchy and \((I,d)\) is complete, this sequence is convergent, so there exists \([l,u] \in I\) such that \(g[l_{n},u_{n}] \rightarrow [l,u]\). The continuity of G and g implies that \(gg[l_{n},u_{n}] \rightarrow g[l_{n},u_{n}]\) and \(Gg[l_{n},u_{n}] \rightarrow G[l_{n},u_{n}]\).
Moreover, since G and g are compatible mappings and the limits of the sequences \(G[l_{n},u_{n}] \stackrel{\varOmega}{=} g[l_{n+1},u_{n+1}]\) and \(g[l_{n},u_{n}]\) coincide, we have
Consider
This implies that
Hence \([l,u]\) is a near-coincidence point of G and g. □
To illustrate the theorem, we consider the following example.
Example 3.17
Let \((I,d)\) be a complete metric interval space, and let \(G[x,y]=[2x-2,2y+2]\) and \(g[x,y]=[x-1,y+1]\) be two self-mappings. Take the sequence \(\{[-\frac{1}{n},\frac{1}{n}]\}_{n=1}^{n=+\infty }\) in I and the simulation function \(S(t,s)=\lambda s-t\) with \(\lambda \geq 2\). As we have proved before, the mappings G and g are compatible, and also G is a \((Z_{d},g)\)-contraction in \((I,d)\). We will just prove that the sequence \(\{[-\frac{1}{n},\frac{1}{n}]\}_{n=1}^{n=+\infty }\) is a Picard sequence. For this, we have to show that
that is, we have to prove that \([-\frac{1}{n+1}-1,\frac{1}{n+1}+1]\stackrel{\varOmega}{=}[-2 \frac{1}{n}-2,2\frac{1}{n}+2]\). Take \(\omega _{1}=[-\frac{n^{2}+2n+2}{n(n+1)}, \frac{n^{2}+2n+2}{n(n+1)}] \) and \(\omega _{2}=[0,0]\). Then clearly
Consequently, we have \(g[x_{n+1},y_{n+1}]\stackrel{\varOmega}{=}G[x_{n},y_{n}]\). So the sequence \(\{[-\frac{1}{n},\frac{1}{n}]\}_{n=1}^{n=+\infty }\) is a Picard sequence for G and g. Hence by the theorem the limit of \(g[x_{n},y_{n}]=[-\frac{1}{n}-1,\frac{1}{n}+1]\), which is \([-1,1]\), is a near-coincidence point for G and g.
If G and g are commuting, then we have
which implies that G and g are compatible, that is,
So we can state the following corollary.
Corollary 3.18
LetGbe aZ-contraction in the complete metric interval space\((I,d)\)and suppose there exists a Picard sequence for the mappingsGandgat the point\([l,u] \in I\), that is,
Also, assume that the mappingsGandgare continuous and commuting. Then there exists a near-coincidence point forGandg.
Corollary 3.19
LetGandgbe the two self-mappings on the complete metric interval space\((I,d)\)satisfying all the conditions stated in Theorem 3.16. If\([l,u]\)and\([w,x]\)are two near-coincidence points forGandg, then\(G[l,u]\stackrel{\varOmega}{=}g[l,u]\stackrel{\varOmega}{=}g[w,x]\stackrel{\varOmega}{=}G[w,x]\). Moreover, if one of the mappingsGandgis injective, then there exists a unique class of near-coincidence points.
Proof
Let G and g have two near-coincidence points \([l,u]\) and \([w,x]\). Then \(G[l,u]\stackrel{\varOmega}{=}g[l,u]\) and \(G[w,x]\stackrel{\varOmega}{=}g[w,x]\). We have to show that \(g[l,u]\stackrel{\varOmega}{=}g[w,x]\). On the contrary, suppose \(g[l,u]\stackrel{\varOmega }{\neq}g[w,x]\). So \(d(g[l,u],g[w,x]) > 0\). Now as G is a \((Z_{d},g)\)-contraction, we have
it implies that
which is contradiction to (2.1). So we have \(g[l,u]\stackrel{\varOmega}{=}g[w,x]\) and hence \(G[l,u]\stackrel{\varOmega}{=}g[l,u]\stackrel{\varOmega}{=}g[w,x]\stackrel{\varOmega}{=}G[w,x]\).
Now for the sake of simplicity we assume that G is injective then we have to show that the near-coincidence point is unique.
Let \([l,u]\) and \([w,x]\) are the two coincidence point of G and g. Then by above we have \(G[l,u]\stackrel{\varOmega}{=}g[l,u]\stackrel{\varOmega}{=}g[w,x]\stackrel{\varOmega}{=}G[w,x] \Rightarrow G[l,u]\stackrel{\varOmega}{=}G[w,x] \Rightarrow [l,u]\stackrel{\varOmega}{=}[w,x]\) as G is injective Therefore we have \(\langle [l,u]\rangle =\langle [w,x]\rangle \). Hence the class of near-coincidence point is unique. □
Corollary 3.20
Let\((I,d)\)be a complete metric interval space and letGandgbe self mappings on\((I,d)\)such that the\(\mathrm{CLR}(G,g)\)property holds inIand\(d(G[l,u],G[w,x]) \leq \lambda d(g[l,u],g[w,x])\)for all\([l,u],[w,x] \in I\)such that\(g[l,u] \stackrel{\varOmega }{\neq} g[w,x]\)where\(\lambda \in [0,1)\), thenGandghas a near-coincidence point.
Proof
We will show that G is a \(Z_{d}\)-contraction by taking the simulation function \(S \in Z\) defined by \(S(l,u)=\lambda u-l\) for all \(l,u \in [0, +\infty )\) and \(\lambda \in [0,1)\).
Since by the given condition we have
this implies that
The last inequality shows that G is a \(Z_{d}\)-contraction, so by Theorem 3.16 it has a near-coincidence point in I. □
Corollary 3.21
Let\((I, d)\)be a complete metric interval space, and letGandgbe self-mappings onIthat satisfy the following condition:
whereΦis a lower semicontinuous function on\([0, +\infty )\), and\(\varPhi ^{-1}(0) =0\). ThenGandghas a near-coincidence point inI.
Proof
By taking the simulation function \(S \in Z\) defined by
We can show that G is a Z-contraction, and hence by Theorem 3.16G and g have a near-coincidence point. □
Lemma 3.22
IfFis a\((Z_{\|\cdot }\|,g)\)-contraction in the hyperspace\(S(V)\)andUand\(U^{*}\)are two near-coincidence points ofFandg, then
Further, ifForgis injective and if they have a near-coincidence point, then it is unique in the sense of equivalence class.
Proof
As U and \(U^{*}\) are near-coincidence points for F and g, we have
We have to show that \(g(U) \stackrel{\varOmega}{=} g(U^{*})\). On the contrary, suppose \(g(U) \stackrel{\varOmega }{\neq} g(U^{*})\). Then \(\|g(U)-g(U^{*})\| \geq 0\). As F is a \((Z_{\|\cdot }\|,g)\)-contraction, we have
This implies that \(\xi (\|g[U]-g[U^{*}]\|, \|g[U]-g[U^{*}]\|) \geq 0\), which is a contradiction to (2.1). So \(g(U) \stackrel{\varOmega}{=} g(U^{*})\), and hence
Further, if F or g is injective and if they have a near-coincidence point, then it is unique in the sense of equivalence class. Let F be injective, and let U and \(U^{*}\) be two different near-coincidence points for F and g. Then
from which we have
This implies that \(F(U) \stackrel{\varOmega}{=} F(U^{*})\),which in turn implies \(U \stackrel{\varOmega}{=} U^{*}\) because F is injective. So the near-coincidence point is unique in the sense of equivalence class. □
Theorem 3.23
Let\((S(V),\|\cdot \|)\)be a Banach hyperspace such that\(\|\cdot \|\)satisfies the null equality, and letFbe a\((Z_{\|\cdot \|},g)\)-contraction. Also, assume that the functionsFandgare compatible and continuous and there exists a Picard sequence\(\{A_{n}\}\)forFandg. ThenFandghave at least one near-coincidence point.
Proof
If the sequence \(\{g[A_{n}]\}\) contains a near-coincidence, then the is nothing to prove. Suppose \(\{g[A_{n}]\}\) contains no near-coincidence point, that is,
Hence \(\|g[A_{n}] \ominus g[A_{n+1}]\| > 0\). First, we will prove that \(\lim_{n \rightarrow +\infty } \|g[A_{n}] \ominus g[A_{n+1}]\| = 0 \).
By \(\xi _{2}\) we have
which implies that
The sequence \(\{ \|g[A_{n}] \ominus g[A_{n+1}]\| \}\) is a nonincreasing sequence of nonnegative real numbers, so it is convergent. Let its limit be p, that is,
To show that \(p=0\), let on the contrary suppose that \(p > 0\). As the sequences \(t_{n}= \{ \|g[A_{n+1}] \ominus g[A_{n+2}]\| \}\) and \(s_{n} = \{ \|g[A_{n}] \ominus g[A_{n+1}]\| \}\) have the same limit and \(t_{n} < s_{n}\) for all \(n \geq 0\), applying \(\xi _{3}\) to these sequences, we have
which is a contradiction because
and hence \(p=0\).
Step 2. Next, we have to show that \(\{g[A_{n}]\}\) is a Cauchy sequence. On the contrary, suppose that \(\{g[A_{n}]\}\) is not a Cauchy sequence. So there exists \(\epsilon ^{\prime } > 0\) such that for all \(N \in \mathbb{N}\), there exist positive integers m, n such that
which leads to two subsequences \(\{g[A_{n_{k}}]\}\) and \(\{ A_{m_{k}}\}\) by taking successive values for N such that \(n_{o} \leq n_{k} < m_{k} \) and
Also, by the convergence of the sequence \(\{g[A_{n}]\}\) we have
Let \(m_{k}\) be the least among \(\{n_{k},n_{k+1},n_{k+2},\ldots\}\) such that (3.4) holds. Then if we take a smaller value than \(m_{k}\), then we will have
Also, we cannot take \(m_{k}=n_{k}\) and \(m_{k}=n_{k+1}\) because (3.4) becomes zero for \(m_{k}=n_{k}\), and for \(m_{k}=n_{k+1}\), we have
so \(m_{k} \geq n_{k+2}\) for all \(k \in N\). From Eqs. (3.5) and (3.7) we have
As \(\lim_{n \rightarrow +\infty } \|g[A_{n_{k}}] \ominus g[A_{m_{k}}]\| = 0 \), by the inequalities we have
Similarly,
Now as F is a \((Z_{\|\cdot }\|,g)\)-contraction, we have
which implies that
Now from the last inequality consider the two sequences \(t_{k}=\{ \|g[A_{m_{k+1}}] \ominus g[A_{n_{k+1}}]\|\}\) and \(s_{k} = \|g[A_{m_{k}}] \ominus g[A_{n_{k}}]\|\). As \(t_{k}\) and \(s_{k}\) have the same limit and \(t_{k} < s_{n}\), by applying \(\xi _{3}\) we have
which is a contradiction because \(\xi (t_{k},s_{k}) > 0\). Hence \(\{g[A_{n}]\}\) is a Cauchy sequence.
Step 3. In this step, we will show that the limit point of \(\{g[A_{n}]\}\) is a near-coincidence point for F and g. As the space \((S(V),\|\cdot \|)\) is complete, the sequence converges to some limit, say A. As F and g are continuous and compatible and A is the limit of \(\{g[A_{n}]\}\), we have:
Consider
Hence \(\|F[A] \ominus g[A]\| = 0\), which implies \(F[A] \stackrel{\varOmega}{=} g[A]\). So we have proved that A is a near-coincidence point of F and g. □
As commuting of F and g implies compatibility, we have the following corollary.
Corollary 3.24
Let\((S(V),\|\cdot \|)\)be a Banach hyperspace such that\(\|\cdot \|\)satisfies the null equality, and letFbe a\((Z_{\|\cdot \|},g)\)-contraction. Also, assume that the functionsFandgare commuting and continuous and there exists a Picard sequence\(\{A_{n}\}\)forFandg. ThenFandghave at least one near-coincidence point.
Corollary 3.25
Let\((S(V),\|\cdot \|)\)be a Banach hyperspace such that\(\|\cdot \|\)satisfies the null equality, and letFandgbe self-mappings on\(S(V)\)such that there exists a Picard sequence forFandgin\(S(V)\)and
such that\(g[U] \stackrel{\varOmega }{\neq} g[U^{*}]\). ThenFandghave a near-coincidence point.
Proof
We will show that F is a Z-contraction by taking the simulation function \(S \in Z\) defined by \(S(u,v)=\lambda v - u\) for all \(l,u \in [0, +\infty )\) and \(\lambda \in [0,1)\).
According to the given condition, we have
which implies that
The last inequality shows that F is a \(Z_{d}\)-contraction, so by Theorem 3.23 it has a near-coincidence point in \(S(V)\). □
Corollary 3.26
Let\((S(V),\|\cdot \|)\)be a Banach hyperspace such that\(\|\cdot \|\)satisfies the null equality, and letFandgbe self-mappings on\(S(V)\)that satisfy the following condition:
whereΦis a lower semicontinuous function on\([0, +\infty )\), and\(\varPhi ^{-1}(0) =0\). ThenGandghave a near-coincidence point in\(S(V)\).
Proof
By taking the simulation function \(S \in Z\) defined by
We can show that F is a Z-contraction, and hence by Theorem 3.16F and g have a near-coincidence point. □
4 Conclusion
Nowadays, the researchers in the subject area are working to produce more effective and generalized fixed point results. Recently, Wu [1, 2] introduced the concept of a near-fixed point and established some results on near-fixed points in metric interval spaces and hyperspaces. Motivated by these papers, we studied the near-coincidence point theorem in these spaces via a simulation function. To illustrate the established results and definitions, we included some examples.
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Ullah, M., Sarwar, M., Khan, H. et al. Near-coincidence point results in metric interval space and hyperspace via simulation functions. Adv Differ Equ 2020, 291 (2020). https://doi.org/10.1186/s13662-020-02734-6
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DOI: https://doi.org/10.1186/s13662-020-02734-6
Keywords
- Near-fixed point
- Metric interval space
- Hyperspace
- Null set
- Simulation function
- Coincidence point