Numerical Analysis: Fixed Point Iteration

In summary, Numerical Analysis: Fixed Point Iteration is a method used to approximate solutions to equations by repeatedly applying a function to an initial guess. It is based on the concept of a fixed point, where the output of the function is equal to the input. This method is useful for solving nonlinear equations and can be applied to a wide range of mathematical problems. It is an iterative process that continues until a desired level of accuracy is achieved. While it can be computationally efficient, it may not always converge to the correct solution and may require careful selection of the initial guess.
  • #1
Goomba
11
0
Consider the fixed point iteration formula:
*x_(n+1) = (2/3)[(x_n)^3 - 1] - 3(x_n)^2 + 4x_n = g(x)

*Note: "_" precedes a subscript and "^" precedes a superscript

(a) Find an interval in which every starting point x_0 will definitely converge to alpha = 1.

(b) Show that the order of the above fixed point iteration formula is 2 (quadratic convergence).

=======================================

For (a), I took the derivative of g(x) and set it equal to zero. I found that when g'(x) = 2x^2 - 6x +4 = (2x - 2)(x - 2)= 0, x = 1, 2.

But g'(alpha) = g'(1) = 2 - 6 + 4 = 0...?

I want to say that the interval is (1,2]...

For (b), I tried |alpha - x_(n + 1)| <= c|1 - x_n|^p, where p is the order and c is some constant >= 0. And Newton's method usually converges quadratically... I ended up with:

|-(2/3)(x_n)^3 + 3(x_n)^2 - 4x_n + (5/3)| <= c|1 - x_n|^p

I don't know how to conclude that p must be 2... or if this is even right...
 
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  • #2
For anyone reading this thread in 2022 or after, the * on the second line isn't part of the equation, it appears to just be a "footnote," not mathematical notation. If anyone wants to use a similar footnote, in this example it would have been better to have it on the first line like this:

Consider the fixed point iteration formula*:
x_(n+1) = (2/3)[(x_n)^3 - 1] - 3(x_n)^2 + 4x_n = g(x)

*Note: "_" precedes a subscript and "^" precedes a superscript

As for the OP's question, here are some sources to read and work through if you find yourself with a similar problem:

https://math.stackexchange.com/ques...the-interval-of-convergence-in-Newtons-method

https://en.wikipedia.org/wiki/Newton's_method

This next source is a video which goes into the weeds a bit farther than the OP's question, but the first eleven minutes or so are an excellent lecture on the "Calculus 1" type of Newton's method problems. Also, the weeds the video gets into after that are very pretty, so one might want to stick around for them anyway:
 
  • #3
Goomba said:
Consider the fixed point iteration formula:
*x_(n+1) = (2/3)[(x_n)^3 - 1] - 3(x_n)^2 + 4x_n = g(x)

*Note: "_" precedes a subscript and "^" precedes a superscript

(a) Find an interval in which every starting point x_0 will definitely converge to alpha = 1.

(b) Show that the order of the above fixed point iteration formula is 2 (quadratic convergence).

=======================================

For (a), I took the derivative of g(x) and set it equal to zero. I found that when g'(x) = 2x^2 - 6x +4 = (2x - 2)(x - 2)= 0, x = 1, 2.

But g'(alpha) = g'(1) = 2 - 6 + 4 = 0...?

I want to say that the interval is (1,2]...

For (b), I tried |alpha - x_(n + 1)| <= c|1 - x_n|^p, where p is the order and c is some constant >= 0. And Newton's method usually converges quadratically... I ended up with:

|-(2/3)(x_n)^3 + 3(x_n)^2 - 4x_n + (5/3)| <= c|1 - x_n|^p

I don't know how to conclude that p must be 2... or if this is even right...

Setting [itex]x_n = 1 + \epsilon_n[/itex] should be the first step, and leads to [tex]
\epsilon_{n+1} = 3\epsilon_n^2 + \tfrac23 \epsilon_n^3.[/tex] For [itex]|\epsilon_n| < \frac32[/itex] this leads to the bound [tex]
|\epsilon_{n+1}| \leq 3|\epsilon_n|^2 + \tfrac23|\epsilon_n|^3 < 4|\epsilon_n|^2.[/tex]

We also have [itex]|3\epsilon^2 + \frac23\epsilon^3| < |\epsilon|[/itex] when [tex]
\left|\left(\frac94 + \epsilon\right)^2 - \frac{81}{16}\right| < \frac32[/tex] which yields [tex]
-0.363 \approx \frac{\sqrt{57} - 9}4 < \epsilon < \frac{\sqrt{105} - 9}{4} \approx 0.312.[/tex] Provided we start with [itex]\epsilon = x - 1[/itex] in this interval, the iteration will converge to [itex]x = 1[/itex].
 
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1. What is fixed point iteration in numerical analysis?

Fixed point iteration is a numerical method used to find the root of a function. It involves repeatedly applying a function to an initial guess until a fixed point is reached, meaning that the function output is equal to the input. This method is commonly used to solve non-linear equations.

2. How does fixed point iteration work?

Fixed point iteration works by starting with an initial guess for the root of a function. The function is then applied to this guess, creating a new guess. This process is repeated until the function output is equal to the input, indicating that a fixed point has been reached. The final guess is then considered to be the root of the function.

3. What is the formula for fixed point iteration?

The formula for fixed point iteration is xn+1 = g(xn), where xn is the current guess and g(x) is the function being iterated. This formula is applied repeatedly until a fixed point is reached.

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

Choosing an appropriate initial guess for fixed point iteration is important for the method to converge to the correct root. A good initial guess is one that is close to the actual root and also ensures that the function remains continuous and differentiable. This can be determined by plotting the function and visually estimating the root, or by using other methods such as the bisection method.

5. What are the advantages and disadvantages of fixed point iteration?

The advantages of fixed point iteration include its simplicity and ease of implementation, making it a popular method for solving non-linear equations. However, it may converge slowly or not at all for certain functions, and it can be difficult to determine an appropriate initial guess for more complex functions. Therefore, it is important to also consider other numerical methods for solving equations.

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