Meir keeler contractive mappings and cauchy sequence

In summary: In the proof, we are assuming that $\lim_{{m,n}\to{\infty}}d\left({x}_{n},{x}_{m}\right)=0$, which means that the distance between ${x}_{n}$ and ${x}_{m}$ approaches 0 as $n$ and $m$ approach infinity. However, we are also assuming that there exists a subsequence $\left\{{x}_{n\left
  • #1
ozkan12
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Denote with ${\varPsi}_{st}$ the family of strictly nondecreasing functions ${\Psi}_{st}:[0,\infty)\to [0,\infty)$ continuous in $t=0$ such that

${\Psi}_{st}=0$ if and only if $t=0$
${\Psi}_{st}(t+s)\le {\Psi}_{st}(t)+{\Psi}_{st}(s)$.

Definition: Let $\left(X,d\right)$ be a metric space and ${\Psi}_{st} \in {\varPsi}_{st}$. Suppose that $f: X\to X$ is a $\alpha-$ admissible mapping satisfying the following condition: for each $\varepsilon >0$ there exists $\delta >0$ such that

$\epsilon\le{\Psi}_{st}\left(M\left(x,y\right)\right)<\varepsilon+\delta$ implies $\alpha\left(x,y\right){\Psi}_{st}\left(d\left(fx,fy\right)\right)<\varepsilon$

for all $x,y\in X$ where $M\left(x,y\right)=max\left\{d\left(x,y\right),d\left(x,fx\right),d\left(y,fy\right),\frac{1}{2}\left[d\left(fx,y\right)+d\left(x,fy\right)\right]\right\}$. Then f is called generalized an $\alpha-{\Psi}_{st}-$ meir-keeler contractive mapping.

Theorem: Let $\left(X,d\right)$ be a complete metric space and $f:X\to X$ a orbitally continuous generalized $\alpha-{\Psi}_{st}-$ meir-keeler contractive mapping, if there exists ${x}_{0}\in X$ such that $\alpha\left({x}_{0},f{x}_{0}\right)\ge 1$. Then, f has a fixed point. ( İn part of proof of this theorem, it is not important definition of $\alpha-$ admissible mapping.)

Proof: Define ${x}_{n+1}={f}^{n+1}{x}_{0}$ for all $n\ge0.$ We want to prove that $\lim_{{m,n}\to{\infty}}d\left({x}_{n},{x}_{m}\right)=0$. İf this not, then there exists $\varepsilon>0$ and a subsequence $\left\{{x}_{n\left(i\right)}\right\}$ of $\left\{{x}_{n}\right\}$ such that $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)}\right)>2\varepsilon$ (2.12). For this $\varepsilon>0$, there exists $\delta>0$ such that $\varepsilon\le{\Psi}_{st}\left(M\left(x,y\right)\right)<\varepsilon+\delta$ implies that $\alpha\left(x,y\right){\Psi}_{st}\left(d\left(fx,fy\right)\right)<\varepsilon$. Put $r=min\left\{\varepsilon,\delta\right\}$ and ${s}_{n}=d\left({x}_{n},{x}_{n+1}\right)$ for all $n\ge1$. From proposition, there exists ${n}_{0}$ such that ${s}_{n}=d\left({x}_{n},{x}_{n+1}\right)<\frac{r}{4}$ for all $n\ge{n}_{0}$ (2.13). ( I didnt write this preposition, İt is enough to know that this is true.) We get $n\left(i\right)\le n\left(i+1\right)-1$. İf $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)-1}\right)\le\varepsilon+\frac{r}{2}$, then
$d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)}\right)\le d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)-1}\right)+d\left({x}_{n\left(i+1\right)-1},{x}_{n\left(i+1\right)}\right)< \varepsilon +\frac{r}{2}+{s}_{n\left(i+1\right)-1}<\varepsilon+\frac{3r}{4}<2\varepsilon$ which contradicts the assumption (2.12). Therefore, there are values of k such that $n\left(i\right)\le k \le n\left(i+1\right)$ and $d\left({x}_{n\left(i\right)},{x}_{k}\right)>\varepsilon+\frac{r}{2}$. Now if $d\left({x}_{n\left(i\right)},{x}_{n\left(i\right)+1}\right)\ge\varepsilon+\frac{r}{2}$, then ${s}_{n\left(i\right)}=d\left({x}_{n\left(i\right)},{x}_{n\left(i\right)+1}\right)\ge\varepsilon+\frac{r}{2}>r+\frac{r}{2}>\frac{r}{4}$ which is a contradiction with (2.13). Hence, there are values of k with $n\left(i\right)\le k\le n\left(i+1\right)$ such that $d\left({x}_{n\left(i\right)},{x}_{k}\right)<\varepsilon+\frac{r}{2}.$

My questions:

1) Firstly, we get $n\left(i\right)\le n\left(i+1\right)-1$. İf $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)-1}\right)\le\varepsilon+\frac{r}{2}$ by using triangle inequality, we get a contradiction...And then we say that there are values of k such that $n\left(i\right)\le k \le n\left(i+1\right)$ and $d\left({x}_{n\left(i\right)},{x}_{k}\right)>\varepsilon+\frac{r}{2}$ and if $d\left({x}_{n\left(i\right)},{x}_{n\left(i\right)+1}\right)\ge\varepsilon+\frac{r}{2}$, then ${s}_{n\left(i\right)}=d\left({x}_{n\left(i\right)},{x}_{n\left(i\right)+1}\right)\ge\varepsilon+\frac{r}{2}>r+\frac{r}{2}>\frac{r}{4}$, again, we get a contradiction...By taking into consideration these cases, how we say that there are values of k with $n\left(i\right)\le k\le n\left(i+1\right)$ such that $d\left({x}_{n\left(i\right)},{x}_{k}\right)<\varepsilon+\frac{r}{2}.$ in last part, if we take $k=n\left(i+1\right)-1$ we can get a contradiction as in first part...

Please can you explain this ? thank you for your attention...best wishes...
 
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  • #2


2) Additionally, in the proof, we are assuming that $\lim_{{m,n}\to{\infty}}d\left({x}_{n},{x}_{m}\right)=0$, but then we are also assuming that there exists a subsequence $\left\{{x}_{n\left(i\right)}\right\}$ such that $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)}\right)>2\varepsilon$. How can we assume both of these things at the same time? It seems contradictory. Can you please explain this part of the proof as well? Thank you.

3) Lastly, I am not familiar with the proposition that is referenced in the proof. Can you please provide more information on this proposition and why it is important in the proof? Thank you.

1) The contradiction arises when we assume that $\lim_{{m,n}\to{\infty}}d\left({x}_{n},{x}_{m}\right)=0$ and that there exists a subsequence $\left\{{x}_{n\left(i\right)}\right\}$ such that $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)}\right)>2\varepsilon$. This contradiction shows that our assumption must be wrong, and thus, we can conclude that $\lim_{{m,n}\to{\infty}}d\left({x}_{n},{x}_{m}\right)=0$ is true.

The proof then goes on to show that there are values of k such that $n\left(i\right)\le k\le n\left(i+1\right)$ and $d\left({x}_{n\left(i\right)},{x}_{k}\right)<\varepsilon+\frac{r}{2}.$ This is because we have shown that the contradiction cannot occur in the case where $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)-1}\right)\le\varepsilon+\frac{r}{2}$ and $d\left({x}_{n\left(i\right)},{x}_{n\left(i+1\right)}\right)\ge\varepsilon+\frac{r}{2}$. Therefore, there must be values of
 

FAQ: Meir keeler contractive mappings and cauchy sequence

What is the significance of Meir-Keeler contractive mappings?

Meir-Keeler contractive mappings are a type of function that has been proven to be useful in many areas of mathematics, including the study of Cauchy sequences. These mappings have the property of shrinking the distance between any two points in a given space, making them useful in the study of convergence and continuity.

What is a Cauchy sequence?

A Cauchy sequence is a sequence of numbers in which the terms get closer and closer to each other as the sequence progresses. This means that the difference between any two terms in the sequence can be made arbitrarily small by choosing a large enough index. Cauchy sequences are important in the study of limits and convergence in mathematics.

How are Meir-Keeler contractive mappings related to Cauchy sequences?

Meir-Keeler contractive mappings are often used in the study of Cauchy sequences because these mappings can help determine whether a sequence is convergent or not. In particular, if a sequence converges to a limit, then it can be shown that the sequence must also be a Cauchy sequence. This relationship is essential in many areas of mathematics, including real analysis and functional analysis.

What is the Meir-Keeler fixed point theorem?

The Meir-Keeler fixed point theorem is a fundamental result in the study of contractive mappings. It states that if a contractive mapping is applied repeatedly to a starting point, the resulting sequence will converge to a unique fixed point. This theorem has many applications in various branches of mathematics, including the study of Cauchy sequences and the Banach fixed point theorem.

How are Meir-Keeler contractive mappings used in practical applications?

Meir-Keeler contractive mappings have a wide range of practical applications, including in computer science, physics, and engineering. In computer science, they are used in algorithms for optimization and data compression. In physics, they are used to model physical systems and predict their behavior. In engineering, they are used in the design and analysis of structures and mechanical systems.

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