First step to proof of convergance using Newton's root finding method

In summary: That's the gist of it.In summary, the conversation discusses how to find the root of a function of the form f(x)=x^{m}-c using Newton's method. It is suggested that the initial guess should be x_{0}=c, and subsequent guesses can be found using the formula x_{n+1}=x_{n} - \frac{f(x_{n})}{f'(x_{n})}. It is also mentioned that x_{0}>x_{1}>x_{n}>x_{n+1}>c^{1/m},
  • #1
trelek2
88
0
Suppose I am looking for the root of a function of the form:
[tex]f(x)=x ^{m}-c[/tex], where c,m>1.

Suppose I take my first guess to be [tex]x _{0}=c[/tex].

Then using Newtons method my next guess will be given by:
[tex]x _{n+1}=x _{n} - \frac {f(x _{n})}{f'(x _{n})}[/tex].

From this, or thinking about this graphically it is obvoius that
[tex]x _{0}>x _{1}>x _{n}>x _{n+1}>c^{1/m}[/tex].

However I don't know how should I go about formally proving this.
 
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  • #2
To save writing, let x be current value and x* the next.

x*=x - (xm-c)/mxm-1={(m-1)xm+c}/mxm-1
< mxm/mxm-1=x, since c<xm.
 
  • #3
Hmm, I knew this, but is this a proof?

Don't we need to prove that [tex]x^{m}>c [/tex] ?

I get the impression that if I want to prove that
[tex]
x _{0}>x _{1}>x _{n}>x _{n+1}
[/tex]
I need to use [tex]x^{m}>c [/tex]

And it is easy to prove that [tex]x^{m}>c [/tex] but only if we are sure that:
[tex]
x _{0}>x _{1}>x _{n}>x _{n+1}
[/tex]

I'm a physics student so I'm not used to proving things formally. Are you sure what you have written is enough?

I also thought of the following proof:
We know that:
[tex]
x _{0}>x _{1}>x _{n}>x _{n+1}
[/tex]
Due to the fact that the function as well as its derivative are >0.
This is true only for [tex]x_{n}>c^{1/m} [/tex]
But we can show that using Newtons method the function x* (what you had written) has a minimum at [tex]x_{n}=c^{1/m} [/tex] (I do this by taking (x*)' and equating it to 0).

Hence we know that [tex]x_{n}>=c^{1/m} [/tex] Therefore the argument stated in the beginning is definitely true: using this fact we know that x* is strictly decreasing and from this it follows that x*>[tex]c^{1/m} [/tex]
 
  • #4
Initially you have c > 1, so the initial guess (c) needs cm > c, which is certainly true for c > 1.
 
  • #5
Hi trelek, you could also use the "mean value theorem" to prove it fairly easily. An outline would go something like this :

Let "a" be the root and "b" (with b>a) be the inital guess. By the mean value theorem there exists some k : a < k < b, such that

f(b) - f(a) = f'(k)(b-a).

Since f' is monotonic increasing it follows that

f(b) - f(a) < f'(b)(b-a)

and since f(a)=0 it follows that f(b) / f'(b) < (b-a)

so

b - f(b)/f'(b) > a
 
Last edited:

What is the Newton's root finding method?

The Newton's root finding method, also known as Newton's method or Newton-Raphson method, is a numerical method used to find the roots of a given function. It is based on the idea of using iterative approximation to improve the accuracy of the root estimate.

How does the Newton's method work?

The Newton's method works by starting with an initial guess for the root of a function. Then, a tangent line is drawn at that point and the x-intercept of that line is taken as the next approximation of the root. This process is repeated until the desired level of accuracy is achieved.

What is the first step to prove convergence using Newton's method?

The first step to prove convergence using Newton's method is to show that the function is continuous and differentiable in the interval where the root is located. This ensures that the tangent line can be drawn at each iteration and that the process will not encounter any discontinuities.

How do you determine the convergence of Newton's method?

The convergence of Newton's method can be determined by calculating the derivative of the function at the root. If the derivative is less than 1, the method will converge. Additionally, the closer the initial guess is to the root, the faster the convergence will be.

What are the advantages of using Newton's method for root finding?

Newton's method has several advantages, including its fast convergence rate, its ability to find multiple roots of a function, and its applicability to a wide range of functions. Additionally, it can provide an accurate estimate of the root even if the initial guess is not very close to the actual root.

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