Accelerated Newton method

In summary: Instead, try using the fact that f(a)=f'(a)=0 and the quotient rule to simplify the expression for g'(x). Then evaluate g'(a) and see if it is equal to 0 or not. If it is not equal to 0, then the modified Newton method converges with order two.
  • #1
talolard
125
0

Homework Statement


given [tex] f \in C^2 [/tex] such that [tex] f(a)=f'(a)=0 ^f''(a)\neq 0 [/tex] prove that the modified Newton method [tex] x_{n+1}=x_n-2 \frac{f(x_n){f'(x_n)} [/tex] coverges with order two.


Homework Equations


if g(x) is an iterative function such that the first m derivatives of g at a are zero and [tex]g^{(m+1)}\neq 0 [/tex] then the order of convergence is m+2



The Attempt at a Solution



So it seems that i want to show that my iterating function [tex] g(x)=x-2 \frac{f(x){f'(x)} [/tex] satisfies [tex] g(a)=0 ^ g'(a)\neq 0 [/tex]
But using le'hospitals rule to find g(a) i have [tex] g(a)=a-2\frac{f'(a)}{f''(a)}=a \neq 0 [/tex]
Whats wrong here?
Thanks
Tal
 
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  • #2
Fixed your LaTeX. I'm assuming that you were using the symbol ^ to mean "and."
talolard said:

Homework Statement


given [tex] f \in C^2 [/tex] such that [tex] f(a)=f'(a)=0 ~\text{and}~ f''(a)\neq 0 [/tex] prove that the modified Newton method [tex] x_{n+1}=x_n-2 \frac{f(x_n)}{f'(x_n)} [/tex] coverges with order two.


Homework Equations


if g(x) is an iterative function such that the first m derivatives of g at a are zero and [tex]g^{(m+1)}\neq 0 [/tex] then the order of convergence is m+2



The Attempt at a Solution



So it seems that i want to show that my iterating function [tex] g(x)=x-2 \frac{f(x)}{f'(x)} [/tex] satisfies [tex] g(a)=0 ~\text{and}~ g'(a)\neq 0 [/tex]
But using le'hospitals rule to find g(a) i have [tex] g(a)=a-2\frac{f'(a)}{f''(a)}=a \neq 0 [/tex]
Whats wrong here?
Thanks
Tal
 
  • #3
talolard said:
So it seems that i want to show that my iterating function [tex] g(x)=x-2 \frac{f(x)}{f'(x)} [/tex] satisfies [tex] g(a)=0 ~\text{and}~ g'(a)\neq 0 [/tex]
But using le'hospitals rule to find g(a) i have [tex] g(a)=a-2\frac{f'(a)}{f''(a)}=a \neq 0 [/tex]
Whats wrong here?
Did you calculate g'(x)? You will need g'(x) so that you can evaluate g'(a). I'm not sure why you think you need L'Hopital's Rule.
 

1. How does the Accelerated Newton method differ from the traditional Newton's method?

The Accelerated Newton method is an advanced version of the traditional Newton's method, which aims to find the roots of a function. Unlike the traditional method, the Accelerated Newton method incorporates an additional step that uses the previous two iterations to better approximate the root. This leads to faster convergence and more accurate solutions.

2. What are the advantages of using the Accelerated Newton method?

One of the main advantages of the Accelerated Newton method is its faster convergence rate compared to the traditional Newton's method. This means that the method requires fewer iterations to find the root, making it more efficient and saving computational time. Additionally, the Accelerated Newton method can handle multiple roots and is less likely to get stuck in local minima.

3. Are there any limitations to the Accelerated Newton method?

Like any numerical method, the Accelerated Newton method also has its limitations. It requires the function to be differentiable and continuous, which may not always be the case for real-world problems. Additionally, the method may not converge or may converge to incorrect roots if the initial guess is not close enough to the actual root.

4. Can the Accelerated Newton method be used for higher-dimensional problems?

Yes, the Accelerated Newton method can be extended to handle higher-dimensional problems. However, the computational complexity increases with the number of dimensions, making it more time-consuming. In these cases, it is often more efficient to use other optimization methods specifically designed for higher-dimensional problems.

5. How do I know if the Accelerated Newton method has converged to the root?

The convergence of the Accelerated Newton method can be determined by checking the magnitude of the function's value at the current iteration. If it is close to zero, the method has likely converged to the root. Additionally, the difference between the current and previous iterations can also be monitored, and if it falls below a predefined tolerance, the method can be considered to have converged.

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