Prove the Contraction Mapping Theorem.

In summary: You've got ##d(x_{n+1}, x_n) \le \lambda^n d(x_1, x_0)##. Use it!In summary, the Contraction Mapping Theorem states that if a complete metric space ##(X,d)## has a map ##g: X \rightarrow X## satisfying ##\forall x,y \in X, d(g(x), g(y)) \le \lambda d(x,y)## for some ##0<\lambda <1##, then ##g## has a unique fixed point ##x^* \in X## that attracts all points in the space. To prove this theorem, one must show that the sequence of iterates ##x_0, g(x
  • #1
a358ask
3
0
Prove the Contraction Mapping Theorem.

Let ##(X,d)## be a complete metric space and ##g : X \rightarrow X## be a map such that ##\forall x,y \in X, d(g(x), g(y)) \le \lambda d(x,y)## for some ##0<\lambda < 1##.Then ##g## has a unique fixed point ##x^* \in X ##, and it attracts everything, i.e. for any ##x_0 \in X## , the sequence of iterates ##x_0, g(x_0), g(g(x_0))##, ... converges to the fixed point ##x^* \in X##.

The hint I am given are for existence and convergence - prove that the sequence is Cauchy. For uniqueness, choose two fixed points of ##g## and apply the map to both.

My approach is After we got to ##d(x_{n+1}, x_n) \le \lambda^n d(x_1, x_0)##,

then assuming that ##m > n##, ##d(x_m, x_n) \le d(x_m, x_{m-1}) + d(x_{m-1}, x_{m-2}) + ... + d(x_{n+1}, x_{n})## since each term of the right hand size is 0 ...so if we add up all the 0 terms, we get 0 on the right hand size. Therefore, ##d(x_m, x_n)## is 0. If this is right, then the question I have here is how do I guarantee that ##x_{m-1} > x_n##?
 
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  • #2
a358ask said:
Prove the Contraction Mapping Theorem.

Let ##(X,d)## be a complete metric space and ##g : X \rightarrow X## be a map such that ##\forall x,y \in X, d(g(x), g(y)) \le \lambda d(x,y)## for some ##0<\lambda < 1##.Then ##g## has a unique fixed point ##x^* \in X ##, and it attracts everything, i.e. for any ##x_0 \in X## , the sequence of iterates ##x_0, g(x_0), g(g(x_0))##, ... converges to the fixed point ##x^* \in X##.

The hint I am given are for existence and convergence - prove that the sequence is Cauchy. For uniqueness, choose two fixed points of ##g## and apply the map to both.

My approach is After we got to ##d(x_{n+1}, x_n) \le \lambda^n d(x_1, x_0)##,

then assuming that ##m > n##, ##d(x_m, x_n) \le d(x_m, x_{m-1}) + d(x_{m-1}, x_{m-2}) + ... + d(x_{n+1}, x_{n})## since each term of the right hand size is 0 ...so if we add up all the 0 terms, we get 0 on the right hand size. Therefore, ##d(x_m, x_n)## is 0. If this is right, then the question I have here is how do I guarantee that ##x_{m-1} > x_n##?

Assuming you are trying to prove the sequence is Cauchy and that ##x_n=g^n(x_0)##, why do you think the terms on the right side are zero??
 
  • #3
Dick said:
Assuming you are trying to prove the sequence is Cauchy and that ##x_n=g^n(x_0)##, why do you think the terms on the right side are zero??

I realize my reasoning a little bit off since the difference between m and n could be infinity , is any difference way to argue that the sum of the right hand side are zeros?
 
  • #4
a358ask said:
I realize my reasoning a little bit off since the difference between m and n could be infinity , is any difference way to argue that the sum of the right hand side are zeros?

They aren't zeros. Try to bound it with a geometric series.
 
  • #5
Dick said:
They aren't zeros. Try to bound it with a geometric series.

Use geometric in the right hand side and could you show a step or two for that?
 
  • #6
a358ask said:
Use geometric in the right hand side and could you show a step or two for that?

You've got ##d(x_{n+1}, x_n) \le \lambda^n d(x_1, x_0)##. Use it!
 

FAQ: Prove the Contraction Mapping Theorem.

1. What is the Contraction Mapping Theorem?

The Contraction Mapping Theorem is a mathematical theorem that provides a method for proving the existence of unique solutions to certain types of equations. It states that if a function is a contraction, meaning it reduces the distance between any two points in its domain, then it has a unique fixed point where the function is equal to that point.

2. Why is the Contraction Mapping Theorem important?

The Contraction Mapping Theorem is important because it provides a powerful tool for proving the existence and uniqueness of solutions to many types of equations, including differential equations and optimization problems. This theorem has applications in many areas of science, engineering, and economics.

3. How is the Contraction Mapping Theorem proven?

The Contraction Mapping Theorem is proven using the Banach fixed-point theorem, which states that if a function is a contraction on a complete metric space, then it has a unique fixed point. The Contraction Mapping Theorem then applies this concept to prove the existence and uniqueness of solutions to equations.

4. What are the requirements for a function to be a contraction?

In order for a function to be a contraction, it must satisfy the following conditions:

  • The function must be defined on a complete metric space.
  • The function must have a Lipschitz constant less than 1, meaning the distance between any two points in the function's domain must decrease by at least a constant factor when the function is applied.

5. Can the Contraction Mapping Theorem be used for any type of equation?

The Contraction Mapping Theorem can be used for a wide range of equations, including ordinary and partial differential equations, integral equations, and optimization problems. However, it is not applicable to all types of equations and there may be other methods for proving the existence and uniqueness of solutions for specific types of equations.

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