Assume that \(\Game =(\nu (\Game ),\vartheta (\Game ))\) is a weighted graph containing all the loops. We say that a sequence \(\{k_{i}\}\in J\) with the initial value \(k_{0}\in J\) is an Ω-Picard sequence (Ω-PS) for an operator \(\Omega :J\rightarrow J\) if \(k_{i}=\Omega k_{i-1}=\Omega ^{i}k_{0}\), \(\forall i\in \mathbb{N} \).
Furthermore, \(\Game =(\nu (\Game ),\vartheta (\Game ))\) is said to justify the property (P) [27] if a ⅁-termwise connected Ω-PS \(\{k_{i}\}\) converging in J guarantees that there are a limit \(k\in J\) of \(\{k_{i}\}\) and \(k_{0}\in \mathbb{N} \) so that \((k_{i},k)\in \vartheta (\Game )\) or \((k,k_{i})\in \vartheta (\Game )\), for all \(i>i_{0}\).
Now, we demonstrate our first main results by defining a graphical \(\Game _{\sigma \xi }\)-contraction as follows:
Definition 4.1
Let \((J,d_{\Game _{\sigma \xi }})\) be a GDCML-space endowed with a graph ⅁ containing all the loops. A mapping \(\Omega :J\rightarrow J\) is called a graphical \(\Game _{\sigma \xi }\)-contraction on a GDCML-space \((J,d_{\Game _{\sigma \xi }})\) if the stipulations below hold:
- \((\Game _{ \sigma \xi }S1)\):
-
J preserves edges of ⅁, i.e., \(\forall k,l\in J\), If \((k,l)\in \vartheta (\Game )\), then \((\Omega k,\Omega l)\in \vartheta (\Game )\);
- \((\Game _{ \sigma \xi }S2)\):
-
there are \(\eta \in {}[ 0,1)\) and \(\sigma (k,l),\xi (k,l)\in {}[ 1,\infty )\) for all \(k,l\in J\) with \((k,l)\in \vartheta (\Game )\), we get
$$ d_{\Game _{\sigma \xi }}(\Omega k,\Omega l)\leq \eta d_{\Game _{ \sigma \xi }}(k,l). $$
(4.1)
Theorem 4.2
Let \((J,d_{\Game _{\sigma \xi }})\) be a ⅁-complete GDCML-space and \(\Omega :J\rightarrow J\) be a graphical \(\Game _{\sigma \xi }\)-contraction. Assume that the hypotheses below hold:
-
(1)
the graph ⅁ verifies the property (p);
-
(2)
for some \(n\in \mathbb{N} \), there is \(k_{0}\in J\) so that \(\Omega k_{0}\in {}[ k_{0}]_{\Game }^{n} \) and
$$ \lim_{i,m\rightarrow \infty } \frac{\sigma (k_{i+1},k_{i+2})}{\sigma (k_{i},k_{i+1})}\xi (k_{i+1},k_{m})< \frac{1}{\eta }, $$
(4.2)
where \(\{k_{i}\}\) is Ω-PS with initial value \(k_{0}\);
-
(3)
for every \(k\in J\), we have that \(\lim_{i\rightarrow \infty }\sigma (k,k_{i})\) and \(\lim_{i\rightarrow \infty }\xi (k_{i},k)\) exist and are finite.
Then there is \(k^{\ast }\in J\) so that the Ω-PS \(\{k_{i}\}\) is ⅁-TWC and converges to both \(k^{\ast }\) and \(\Omega k^{\ast }\).
Proof
Assume that \(k_{0}\in J\) so that for some \(n\in \mathbb{N} \), \(\Omega k_{0}\in {}[ k_{0}]_{\Game }^{n}\). Because \(\{k_{i}\}\) is a Ω-PS with initial value \(k_{0}\), there is a path \(\{l_{j}\}_{j=0}^{n}\) with \(k_{0}=l_{0}\), \(\Omega k_{0}=l_{n}\) and \(( l_{j-1},l_{j} ) \in \vartheta (\Game )\) for \(j=1,2,\ldots,n\). Based on \((\Game _{\sigma \xi }S1)\), for \(j=1,2,\ldots,n\) we have \(( \Omega l_{j-1},\Omega l_{j} ) \in \vartheta (\Game )\). This implies that \(\{\Omega l_{j}\}_{j=0}^{n}\) is a path from \(\Omega l_{0}=\Omega k_{0}=k_{1}\) to \(\Omega l_{n}=\Omega ^{2}k_{0}=k_{2}\) having length n, and thus \(k_{2}\in {}[ k_{1}]_{\Game }^{n}\). By repeating the same approach, we find that \(\{\Omega ^{i}l_{j}\}_{j=0}^{n}\) is a path from \(\Omega ^{i}l_{0}=\Omega ^{i}k_{0}=k_{i}\) to \(\Omega ^{i}l_{n}=\Omega ^{i}\Omega k_{0}=k_{i+1}\) of length n, and thus \(k_{i+1}\in {}[ k_{i}]_{\Game }^{n}\), for all \(i\in \mathbb{N} \). This proves that \(\{k_{i}\}\) is a ⅁-TWC sequence.
Now, \(( \Omega ^{i}l_{j-1},\Omega ^{i}l_{j} ) \in \vartheta ( \Game ) \) for \(j=1,2,\ldots,n\) and \(i\in \mathbb{N} \). By \((\Game _{\sigma \xi }S2)\), we get
$$ d_{\Game _{\sigma \xi }}\bigl(\Omega ^{i}l_{j-1},\Omega ^{i}l_{j}\bigr)\leq \eta d_{\Game _{\sigma \xi }}\bigl(\Omega ^{i-1}l_{j-1},\Omega ^{i-1}l_{j}\bigr). $$
By continuing with the same scenario, we have
$$\begin{aligned} d_{\Game _{\sigma \xi }}\bigl(\Omega ^{i}l_{j-1},\Omega ^{i}l_{j}\bigr) \leq & \eta d_{\Game _{\sigma \xi }}\bigl(\Omega ^{i-1}l_{j-1},\Omega ^{i-1}l_{j}\bigr) \\ \leq &\eta ^{2}d_{\Game _{\sigma \xi }}\bigl(\Omega ^{i-2}l_{j-1}, \Omega ^{i-2}l_{j}\bigr) \\ &{}\vdots \\ \leq &\eta ^{i}d_{\Game _{\sigma \xi }}(l_{j-1},l_{j}). \end{aligned}$$
(4.3)
Since \(\{k_{i}\}\) is a ⅁-TWC sequence, by (4.3), we can write
Since r is finite, letting
a finite quantity, we have
$$ d_{\Game _{\sigma \xi }}(k_{i},k_{i+1})\leq \eta ^{i}Q_{\Game _{ \sigma \xi }}. $$
(4.4)
Again, since \(\{k_{i}\}\) is a ⅁-TWC sequence for \(i,m\in \mathbb{N} \), \(i< m\) and using (4.4), we obtain
$$\begin{aligned} d_{\Game _{\sigma \xi }}(k_{i},k_{m}) \leq &\sigma (k_{i},k_{i+1})d_{ \Game _{\sigma \xi }}(k_{i},k_{i+1})+ \xi (k_{i+1},k_{m})d_{\Game _{ \sigma \xi }}(k_{i+1},k_{m}) \\ \leq &\sigma (k_{i},k_{i+1})d_{\Game _{\sigma \xi }}(k_{i},k_{i+1})+ \xi (k_{i+1},k_{m})\sigma (k_{i+1},k_{i+2})d_{\Game _{\sigma \xi }}(k_{i+1},k_{i+2}) \\ &{}+\xi (k_{i+1},k_{m})\xi (k_{i+2},k_{m})d_{\Game _{\sigma \xi }}(k_{i+2},k_{m}) \\ \leq &\sigma (k_{i},k_{i+1})d_{\Game _{\sigma \xi }}(k_{i},k_{i+1})+ \xi (k_{i+1},k_{m})\sigma (k_{i+1},k_{i+2})d_{\Game _{\sigma \xi }}(k_{i+1},k_{i+2}) \\ &{}+\xi (k_{i+1},k_{m})\xi (k_{i+2},k_{m}) \sigma (k_{i+2},k_{i+3})d_{ \Game _{\sigma \xi }}(k_{i+2},k_{i+3}) \\ &{}+\xi (k_{i+1},k_{m})\xi (k_{i+2},k_{m}) \xi (k_{i+3},k_{m})d_{\Game _{ \sigma \xi }}(k_{i+3},k_{m}) \\ \leq &\cdots \\ \leq &\sigma (k_{i},k_{i+1})d_{\Game _{\sigma \xi }}(k_{i},k_{i+1})+ \sum_{q=i+1}^{m-2} \Biggl( \prod _{s=1}^{q}\xi (k_{s},k_{m}) \Biggr) \sigma (k_{q},k_{q+1})d_{\Game _{\sigma \xi }}(k_{q},k_{q+1}) \\ &{}+\prod_{p=i+1}^{m-1}\xi (k_{p},k_{m})d_{\Game _{\sigma \xi }}(k_{m-1},k_{m}) \\ \leq &\sigma (k_{i},k_{i+1})\eta ^{i}Q_{\Game _{\sigma \xi }}+ \sum_{q=i+1}^{m-2} \Biggl( \prod _{s=1}^{q}\xi (k_{s},k_{m}) \Biggr) \sigma (k_{q},k_{q+1})\eta ^{q}Q_{\Game _{\sigma \xi }} \\ &{}+\prod_{p=m+1}^{i-1}\xi (k_{p},k_{i})\eta ^{i-1}Q_{\Game _{ \sigma \xi }} \\ \leq &\sigma (k_{m},k_{m+1})\eta ^{m}Q_{\Game _{\sigma \xi }}+ \sum_{q=m+1}^{i-2} \Biggl( \prod _{s=1}^{q}\xi (k_{s},k_{i}) \Biggr) \sigma (k_{q},k_{q+1})\eta ^{q}Q_{\Game _{\sigma \xi }} \\ &{}+ \Biggl( \prod_{p=i+1}^{m-1}\xi (k_{p},k_{m}) \Biggr) \eta ^{m-1}\sigma (k_{m-1},k_{m})Q_{\Game _{\sigma \xi }} \\ =&\sigma (k_{i},k_{i+1})\eta ^{i}Q_{\Game _{\sigma \xi }}+ \sum_{q=i+1}^{m-1} \Biggl( \prod _{s=1}^{q}\xi (k_{s},k_{m}) \Biggr) \sigma (k_{q},k_{q+1})\eta ^{q}Q_{\Game _{\sigma \xi }} \\ \leq &\sigma (k_{i},k_{i+1})\eta ^{i}Q_{\Game _{\sigma \xi }}+ \sum_{q=i+1}^{m-1} \Biggl( \prod _{s=0}^{q}\xi (k_{s},k_{m}) \Biggr) \sigma (k_{q},k_{q+1})\eta ^{q}Q_{\Game _{\sigma \xi }}. \end{aligned}$$
Note that, we used the fact \(\sigma (k,l),\xi (k,l)\geq 1\). Assume that
$$ \Psi _{h}=\sum_{q=0}^{h} \Biggl( \prod_{s=0}^{q}\xi (k_{s},k_{m}) \Biggr) \sigma (k_{q},k_{q+1}) \eta ^{q}. $$
Then, we get
$$ d_{\Game _{\sigma \xi }}(k_{i},k_{m})\leq Q_{\Game _{\sigma \xi }} \bigl( \eta ^{i}\sigma (k_{i},k_{i+1})+ ( \Psi _{m-1}-\Psi _{i} ) \bigr) . $$
(4.5)
From condition (4.2) and using the ratio test, we find that \(\lim_{i\rightarrow \infty }\Psi _{i}\) exists, and hence and the real sequence \(\{\Psi _{i}\}\) is a ⅁-Cauchy.
At the last, letting \(m,i\rightarrow \infty \) in (4.5), we have
$$ \lim_{i,m\rightarrow \infty }d_{\Game _{\sigma \xi }}(k_{i},k_{m})=0. $$
This proves that the sequence \(\{k_{i}\}\) is a ⅁-Cauchy in \((J,d_{\Game _{\sigma \xi }})\). The completeness of \((J,d_{\Game _{\sigma \xi }})\) implies that there is a sequence \(\{k_{i}\}\) converges in J and from stipulation (1), there is \(k^{\ast }\in J\), \(i_{0}\in \mathbb{N} \) so that \((k_{i},k^{\ast })\in \vartheta (\Game )\) or \((k^{\ast },k_{i})\in \vartheta (\Game )\), \(\forall i>i_{0}\) and
$$ \lim_{i\rightarrow \infty }d_{\Game _{\sigma \xi }}\bigl(k_{i},k^{\ast } \bigr)=d_{ \Game _{\sigma \xi }}\bigl(k,k^{\ast }\bigr)=\lim_{m,i\rightarrow \infty }d_{ \Game _{\sigma \xi }}(k_{i},k_{m})=0. $$
This assures that \(\{k_{i}\}\) converges to \(k^{\ast }\).
If \((k_{i},k^{\ast })\in \vartheta (\Game )\), then by stipulation \((\Game _{\sigma \xi }S2)\), we get
$$ d_{\Game _{\sigma \xi }}\bigl(k_{i+1},\Omega k^{\ast } \bigr)=d_{\Game _{\sigma \xi }}\bigl(\Omega k_{i},\Omega k^{\ast }\bigr) \leq Ld_{\Game _{\sigma \xi }}\bigl(k_{i},k^{ \ast }\bigr),\text{ for }i>i_{0}. $$
This implies that
$$ \lim_{i\rightarrow \infty }d_{\Game _{\sigma \xi }}\bigl(k_{i+1},\Omega k^{ \ast }\bigr)=0. $$
Also, if \((k^{\ast },k_{i})\in \vartheta (\Game )\), then with the same arguments as above, we find that
$$ \lim_{i\rightarrow \infty }d_{\Game _{\sigma \xi }}\bigl(\Omega k^{\ast },k_{i+1} \bigr)=0. $$
Therefore, \(\{k_{i}\}\) converges to both \(k^{\ast }\) and \(\Omega k^{\ast }\), and this finishes the proof. □
In order to achieve the existence of the fixed point, we introduce the following results:
Definition 4.3
Let \((J,d_{\Game _{\sigma \xi }})\) be a GDCML-space and \(\Omega :J\rightarrow J\) be self-mapping. A trio \((J,d_{\Game _{\sigma \xi }},\Omega )\) is said to satisfy the property (H) if: corresponding to two limits \(k^{\ast }\in J\) and \(l^{\ast }\in \Omega (J)\) of a ⅁-TWC Ω-PS \(\{k_{i}\}\), we get \(k^{\ast }=l^{\ast }\).
Theorem 4.4
Assume that all hypotheses of Theorem 4.2hold and suppose that a trio \((J,d_{\Game _{\sigma \xi }},\Omega )\) verifies the property (H). Then, Ω possesses a fixed point.
Proof
In Theorem 4.2, we were able to prove that the Ω-PS \(\{k_{i}\}\) with initial value \(k_{0}\) converges to both \(k^{\ast }\) and \(\Omega k^{\ast }\). As \(k^{\ast }\in J\) and \(\Omega k^{\ast }\in \Omega (J)\), therefore by our assumption, we have \(k^{\ast }=\Omega k^{\ast }\). Hence, Ω has a fixed point. □
The below example supports Theorem 4.2.
Example 4.5
Let \(J=\{0\}\cup \{\frac{1}{3^{i}}:i\in \mathbb{N} \}\) be endowed with a GDCML \(d_{\Game _{\sigma \xi }}\) described as
$$ d_{\Game _{\sigma \xi }}(k,l)=\textstyle\begin{cases} 0, & \text{if }k=l, \\ ( k+l ) ^{2}, & k\neq l,\end{cases} $$
including the graph ⅁ so that \(J=\nu (\Game )\) and \(\vartheta (\Game )=\Lambda \cup \{(k,l)\in J^{2}:(k\Re l)_{\Game }, k-l\geq 0\}\). It is obvious that \((J,d_{\Game _{\sigma \xi }})\) is a GDCML-space with \(\sigma (k,l)=2+k+l\) and \(\xi (k,l)=3+k+l\). Define a self mapping \(\Omega :J\rightarrow J\) by \(\Omega (k)=\frac{k}{3}\), for all \(k\in J\). It is obvious to see that there is \(k_{0}=\frac{1}{3}\) so that \(\Omega (\frac{1}{3})=\frac{1}{9}\in {}[ \frac{1}{3}]_{\Game }^{1}\), i.e., \((\frac{1}{3}\Re \frac{1}{9})_{\Game }\) and the contraction (4.1) is fulfilled with \(\eta =\frac{1}{9}\). Hence, Ω is a graphical \(\Game _{\sigma \xi }\)-contraction on a GDCML-space \((J,d_{\Game _{\sigma \xi }})\) with \(\sigma (k,l)=2+k+l\), \(\xi (k,l)=1+k+l\) and \(\eta =\frac{1}{9}\).
Since \(\{k_{i}\}\) is a Ω-PS, for each \(k\in J\), \(\Omega ^{i}(k)=\frac{k}{3^{i}}\), such that, we get
$$\begin{aligned} \lim_{i,m\rightarrow \infty } \frac{\sigma (k_{i+1},k_{i+2})}{\sigma (k_{i},k_{i+1})}\xi (k_{i+1},k_{m}) =&\lim_{i,m\rightarrow \infty } \frac{\sigma (\Omega ^{i+1}k_{0},\Omega ^{i+2}k_{0})}{\sigma (\Omega ^{i}k_{0},\Omega ^{i+1}k_{0})} \xi \bigl(\Omega ^{i+1}k_{0},\Omega ^{m}k_{0}\bigr) \\ =&\lim_{i,m\rightarrow \infty } \frac{\sigma (\frac{k_{0}}{3^{i+1}},\frac{k_{0}}{3^{i+2}})}{\sigma (\frac{k_{0}}{3^{i}},\frac{k_{0}}{3^{i+1}})} \xi \biggl(\frac{k_{0}}{3^{i+1}},\frac{k_{0}}{3^{m}}\biggr) \\ =&\frac{\sigma (0,0)}{\sigma (0,0)}\xi (0,0)=3< 9=\frac{1}{\eta }. \end{aligned}$$
Moreover, since \(k_{0}=\frac{1}{3}\) and \(\Omega ^{i}(k)=\frac{k}{3^{i}}\), we see that \(\lim_{i\rightarrow \infty }\sigma (k,k_{i})\) and \(\lim_{i\rightarrow \infty }\xi (k_{i},k)\) exist and are finite. Therefore, all hypotheses of Theorem 4.2 are fulfilled, and 0 is the unique fixed point of Ω on J. Figure 6 presents the weighted graph for \(\nu ^{\ast }(\Game )=\{0,\frac{1}{3},\frac{1}{3^{2}},\frac{1}{3^{3}}, \frac{1}{3^{4}},\frac{1}{3^{5}},\frac{1}{3^{6}}\}\subseteq \nu ^{\ast }(\Game )\), where the wight of edge \((k,l)\) is equal to the value of \(d_{\Game _{\sigma \xi }}(k,l)\).