Fixed Point Iteration Requirements

In summary, the conversation revolves around the question of proving that the given function, x_{n+1}=\frac{1}{3}(x_n^2+2), converges to 1 as n approaches infinity when -2<x_0<2. The participants discuss the conditions for a function to converge to a fixed point, the behavior of the function on different intervals, and the possibility of generalizing the proof without the condition of |f'(x)|<1. The conversation also touches on the issue of proving the strict decreasing nature of the function and explores different approaches to proving it.
  • #1
k3N70n
67
0
Hi

I wrote a numerical analysis midterm earlier this week and there was one question I couldn't figure out. I was wondering if anyone had some insight.

What I've been told and what I've read in many many places is that
f(x) will converge to a fixed point on an interval I if
1. f(x) is continuous and differentiable on I
2. |f'(x)|<1 on I

Now the question I was posed was given [itex] x_{n+1}=\frac{1}{3}(x_n^2+2)[/itex] prove that [itex]x_n\rightarrow 1[/itex] as [itex]n\rightarrow\infty [/itex] if [itex] -2<x_0<2[/itex] (that is prove [itex]x_{n+1}[/itex] is between [itex]x_n[/itex] and 1 when [itex]n \geq 1[/itex])

What was most natural to me was to find the interval where |f'(x)|<1 which happens to be on [itex](-\frac{3}{2},\frac{3}{2})[/itex]. It easy to see (but not so easy to prove) that the interval (-2,2) will work as well. How would you go about showing that? Do think that it would be possible to generalize this such that the requirement |f'(x)|<1 would not be needed for fixed point iteration?

Anyway, hope this interest someone else.
 
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  • #2
Have you looked at a few of the sequences produced from different starting points? The behavior is very easy to describe... it's the sort of behavior whose existence I would expect to be very easy to prove directly (particularly due to the algebraically simple nature of the iteration).
 
  • #3
Yes I have looked at different starting points. I did show that for all starting points on (-2,2) you end up on (1,2). I also noted that the iteration function was strictly decreasing after the first iteration but I wasn't able to show that. I guess if I was more specific I would have said that was my question.
 
  • #4
k3N70n said:
I did show that for all starting points on (-2,2) you end up on (1,2).
All of them? Are you sure?

I also noted that the iteration function was strictly decreasing after the first iteration but I wasn't able to show that. I guess if I was more specific I would have said that was my question.
In other words,
If y = (x^2 + 2)/3 and x is in (1, 2), then y < x

But you can do better than that... you know that it's strictly decreasing to 1; in other words, the distance from 1 is decreasing. This suggests it's more natural to seek to prove
|y-1| < |x-1|.​
 
  • #5
Hurkyl said:
All of them? Are you sure?

Whoops. no. that should have been [itex](\frac{2}{3},2)[/itex]
but I guess it doesn't matter much because I had already showed that it converges on [itex](-\frac{3}{2},\frac{3}{2})[/itex]

So then it remains to be shown that on [itex](\frac{3}{2},2)[/itex] that

In other words,
If y = (x^2 + 2)/3 and x is in (1, 2), then y < x

But you can do better than that... you know that it's strictly decreasing to 1; in other words, the distance from 1 is decreasing. This suggests it's more natural to seek to prove
|y-1| < |x-1|.​
I'm still confused. I do know that it is strictly decreasing but I don't know how to prove it.
What you're suggesting to me is prove:

[tex]|x_{n+1}-1|<|\frac{x_n}{3}-\frac{1}{3}|[/tex] for any [tex]x_n\in(\frac{3}{2},2)[/tex]
I still don't know how to do that though. I there just something really stupid that I'm missing.
 

1. What is fixed point iteration and why is it important?

Fixed point iteration is a mathematical method used to find the roots or fixed points of a function. It involves repeatedly applying a function to an initial guess until a desired level of accuracy is achieved. This method is important because it can be used to solve a wide range of problems in various fields such as engineering, physics, and economics.

2. What are the requirements for a function to be suitable for fixed point iteration?

There are three main requirements for a function to be suitable for fixed point iteration: 1) The function must be continuous on the interval of interest. 2) The function must have a fixed point within the interval. 3) The derivative of the function must be between 0 and 1 at the fixed point. If any of these requirements are not met, the method may not converge or may converge to the wrong solution.

3. Can any function be solved using fixed point iteration?

No, not all functions can be solved using fixed point iteration. The function must have a fixed point and meet the other requirements mentioned above. Additionally, the initial guess chosen must be close enough to the fixed point for the method to converge.

4. How do you choose an appropriate initial guess for fixed point iteration?

Choosing an appropriate initial guess is crucial for the success of fixed point iteration. One method is to plot the function and visually estimate where the fixed point is located. Another approach is to use the intermediate value theorem to narrow down the interval in which the fixed point lies. Additionally, trial and error can be used to find a suitable initial guess.

5. Are there any limitations or drawbacks to using fixed point iteration?

While fixed point iteration can be a powerful method for solving certain problems, it also has some limitations. One limitation is that it may not converge or may converge to the wrong solution if the function does not meet the necessary requirements. Additionally, the convergence may be slow for some functions, requiring a large number of iterations to achieve the desired level of accuracy. Finally, it may not be suitable for functions with multiple fixed points or when the fixed point is close to a point where the derivative is close to 1.

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