Newton's method and Mean Value theorem

In summary, the conversation discusses using the Mean Value Theorem to show that the sequence of approximations of pi from Newton's Method can be expressed as |pi-xj+1|=|tan2cj||pi-xj| for some cj between xj and pi. The conversation explores two possible approaches to solving the problem and concludes with a hint to use the sequence that defines Newton's method.
  • #1
Lily@pie
109
0

Homework Statement



let x0, x1,... be the approximations of pi from the Newton's Method. Use Mean Value theorem to show that
|pi-xj+1|=|tan2cj||pi-xj|
for some cj between xj and pi

Homework Equations



pi is defined as smallest positive number r when sin r =0

The Attempt at a Solution


I have tried to let f(x) = sin x and the bound to be [x,pi].
By using the mean value theorem, there exist some c such that
f'(c) = (sin pi - sin x) / (pi - x)
cos c = - sin x / (pi-x)
But I couldn't get the form, especially the |tan2cj| part.

I have also tried to let f(x) = tan x - x in [x,pi]
so, f'(c)=sec2c + 1 = (tan pi + pi - tan x - x)/(pi-x)
tan2 c = (tan pi - tan x + pi - x)/(pi-x)
But tan pi is undefined...

Am I on the correct path?
 
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  • #2
Lily@pie said:
I have also tried to let f(x) = tan x - x in [x,pi]
so, f'(c)=sec2c + 1 = (tan pi + pi - tan x - x)/(pi-x)
tan2 c = (tan pi - tan x + pi - x)/(pi-x)
But tan pi is undefined...

Am I on the correct path?

This second route is a good path. Note tan(pi) = sin(pi)/cos(pi) = 0/(-1) = 0. Then you're done, assuming you know what Newton's method is.

This was actually a decent problem.
 
  • #3
I've used f(x) = tan x - x

and manage to find
tan2cj= (-pi + xj - tan xj)/(pi - xj)

From intermediate theorem,
xj+1 = xj - (tan xj - xj)/tan2xj

Hence, I get
tan2cj
= (-pi + xj+1 - tan xj + (tan xj - xj)/tan2xj))/ (pi-xj)

I have tried all sorts of method to evaluate this but still couldn't get the form (pi-xj+1)/(pi-xj)...

I have tried opening the tan x, but still stuck...

Did I do it correctly? Do you mind providing any hints?
 
  • #4
You shouldn't go back to the intermediate value theorem. The problem statement tells you that we're approximating pi with Newton's method. The fact that pi is defined as smallest positive number r when sin r = 0 makes it clear how the sequence that defines Newton's method is generated here. We are approximating a root of sin(x) basically. Now write out the sequence that defines Newton's method.
 
  • #5
Oh! Thank you so much!
 

Related to Newton's method and Mean Value theorem

1. What is Newton's method and how does it work?

Newton's method is an iterative algorithm used to approximate the roots of a function. It involves starting with an initial guess for the root, then using the slope of the function at that point to find a better guess. This process is repeated until the desired level of accuracy is achieved.

2. How is the Mean Value theorem related to Newton's method?

The Mean Value theorem states that if a function is continuous on a closed interval and differentiable on the open interval, there exists at least one point where the slope of the tangent line is equal to the slope of the secant line between the endpoints of the interval. This is the basis for the derivative used in Newton's method.

3. What are the advantages and limitations of using Newton's method?

One advantage of Newton's method is that it can converge quickly to the root of a function, especially when the initial guess is close to the actual root. However, it may fail to converge if the initial guess is far from the root or if the function has multiple roots. Additionally, it requires the function to be differentiable and its derivative to be known.

4. Can Newton's method be used for finding roots of any function?

No, Newton's method can only be used for finding roots of differentiable functions. It also requires the derivative of the function to be known, which may not always be the case. In these situations, other methods such as bisection or secant method may be more suitable.

5. How is Newton's method used in real-world applications?

Newton's method is commonly used in optimization problems, such as finding the maximum or minimum of a function. It is also used in physics and engineering to solve complex equations and calculate trajectories. In addition, it is used in machine learning algorithms for training models and finding optimal parameters.

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