Proof of Contraction Mapping for $f\left(x\right)={e}^{{-e}^{-x}}$

In summary, Ozkan12 proved that $f\left(x\right)={e}^{{-e}^{-x}}$ is contraction mapping on R by showing that $1/e = \sup\limits_{-\infty < x < \infty} |f'(x)|$. Furthermore, $g(x) = x+e^{-x}$ has only one critical point at $x = 0$, and has a global minimum at $x = 0$.
  • #1
ozkan12
149
0
Please can you prove that $f\left(x\right)={e}^{{-e}^{-x}}$ is contraction mapping on R...Thank you for your attention...
 
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  • #2
Try proving that $|f'(x)|<1$.
 
  • #3
I tried this but I didnt get something...Dear professor
 
  • #4
What did you get for $f'(x)$?
 
  • #5
I get this ${e}^{-(x+{e}^{-x})}$...But how ${e}^{-(x+{e}^{-x})}$ $\le$ 1 ?
 
  • #6
Well, based on our knowledge of the exponential function $e^x$, we know that $e^x \le 1$ precisely when $x\le 0$. Can you prove that $-(x+e^{-x})\le 0?$

Also, with contraction mappings, it's always a good idea to mention which metric is being used. Is it just $d(x,y)=|x-y|?$

Actually, a more fruitful method of showing this might be to let $g(x)=f'(x)$, and maximize $g(x)$ using the usual calculus methods.
 
  • #7
I know $-\left(x+{e}^{-x}\right)$ $\le$0...But for which "x" we get ${e}^{-(x+{e}^{-x})}$ $\le$ 1? $x\in[0,\infty)$ or $x\in[-\infty,0)$...I didnt know this
 
  • #8
Hi ozkan12,

You want to prove $x + e^{-x} > 0$ for all $x\in \Bbb R$, for then $e^{-(x + e^{-x})} < 1$ for all $x\in \Bbb R$ as desired. Actually, we can prove that $1/e$ is a suitable contraction constant for $f$ by showing that $1/e = \sup\limits_{-\infty < x < \infty} |f'(x)|$. Now the function $g(x) = x + e^{-x}$ has only one critical point at $x = 0$. Furthermore, $g'(x) = 1 - e^{-x}$ is positive for all $x > 0$ and negative for all $x < 0$. By the first derivative test, $g$ has a global minimum at $x = 0$. The minimum value of $g$ is $g(0) = 1$, so

$$\sup_{-\infty < x < \infty} |f'(x)| = e^{-g(0)} = e^{-1},$$

proving that $1/e$ is a contraction constant for $f$.
 
  • #9
Dear professor,

Why we use supremum ?
 
  • #10
We use the mean value theorem to relate derivative and contraction. Namely, if $f$ is differentiable on an interval $I$, then for every $x_1,x_2\in I$ there exists an $x_0\in (x_1,x_2)\subseteq I$ such that $f(x_2)-f(x_1)=(x_2-x_1)f'(x_0)$. Thus, if $S=\sup_{x\in I}|f'(x)|$, then $|f(x_0)|\le S$ and
\[
|f(x_2)-f(x_1)|=|x_2-x_1|\cdot|f(x_0)|\le |x_2-x_1|\cdot S.
\]
Therefore, if $S<1$. then $f$ is a contraction on $I$.
 
  • #11
Dear professor,

Why we use minimum value of "g" ?
 
  • #12
ozkan12 said:
Dear professor,

Why we use minimum value of "g" ?

So, from Euge's post, we have $g(x)=x+e^{-x}$. Then the original function's derivative is $f'(x)=e^{-g(x)}$. Now $e^{-x}$ is a monotonically decreasing function. It follows that a maximum of $f'(x)$, which is what we are trying to find, would happen precisely where $g(x)$ has a minimum.
 
  • #13
Dear Ackbach

I opened new thread on forum...Can you see that ? Thank you for your attention :)
 

Related to Proof of Contraction Mapping for $f\left(x\right)={e}^{{-e}^{-x}}$

What is a contraction mapping?

A contraction mapping is a function that maps elements from one metric space to another such that the distance between the images of any two elements is always less than the distance between the original two elements. In simpler terms, a contraction mapping is a function that "contracts" the space it is mapping from, making it smaller.

Why is the proof of contraction mapping important?

The proof of contraction mapping is important because it guarantees the existence and uniqueness of a fixed point for the function. This means that no matter what initial value is chosen, the function will eventually converge to the same value. This property is useful in various fields, including mathematics, engineering, and computer science.

What is the proof of contraction mapping for the given function?

The proof of contraction mapping for the function $f\left(x\right)={e}^{{-e}^{-x}}$ involves showing that the function satisfies the definition of a contraction mapping. This includes proving that the function is Lipschitz continuous with a Lipschitz constant less than 1. Once this is established, it can be shown that the function has a unique fixed point, and hence, the proof of contraction mapping is complete.

How is the proof of contraction mapping used in real-world applications?

The proof of contraction mapping is used in various real-world applications, including numerical methods for solving equations and optimization problems, control theory, and dynamical systems. It is also used in machine learning algorithms, such as the K-means clustering algorithm.

Are there any limitations to the proof of contraction mapping?

Yes, there are some limitations to the proof of contraction mapping. The function must satisfy the definition of a contraction mapping, and not all functions do. Additionally, the proof only guarantees the existence and uniqueness of a fixed point, but it does not provide a method for finding the fixed point. Finally, the proof assumes that the function is defined on a complete metric space, which may not always be the case in real-world applications.

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