Newton's method of estimation - using derivatives

In summary, Newton's method for approximating a real root of the equation f(x) = 0 involves guessing an initial approximation and then using the tangent line at that point to find a better approximation. This process can be repeated until the desired level of accuracy is achieved. This method is similar to the method used by Heron to approximate square roots. The formula for Newton's method is xn+1 = xn – f(xn)/f’(xn) and it can be applied to various functions, such as f(x) = x^2 - k, to approximate roots.
  • #1
Bruce3
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Homework Statement



Newton devised the following method for approximating a real root of the equation f(x) = 0. i.e. a real number for which f(r) = 0. We begin by guessing an approximation, say x1, to the real root r.

(i) Find the equation of the line tangent to the graph of y = f(x) at the point (x1,f(x1)). Assume f is differentiable and f’(x1) ≠ 0.

(ii) Newton felt that the x-intercept of the tangent line in part(i) would be a better approximation to r than x1. Label the point of intersection of the tangent line in (i) with the x-axis (x2,0). Find x2 in terms of x1, f(x1), and f’(x1).

(iii) The above procedure may now be repeated using the tangent line at (x2,f(x2)). If f’(x2) ≠ 0 this leads to a third approximation, x3, where (x3,0) is the point of intersection of the x-axis with the tangent line at (x2,f(x2)). Find x3 in terms of x2, f(x2), and f’(x2).

If done correctly, you have just derived Newton’s method, which can be written as:
To approximate the real root of r of f(x)=0, begin by guessing an initial approximation to r, say x1, For n = 1, 2, 3, … let xn+1 = xn – f(xn)/f’(xn). Under “good” conditions the sequence xn will converge to r.

(iv) Approximate k^.5 for k > 0 by applying Newton’s method to f(x) = x^2 – k with the initial guess x1, You have just derived the method Heron used around 100 A.D. to approximate k^.5.

Homework Equations


To approximate the real root of r of f(x)=0, begin by guessing an initial approximation to r, say x1, For n = 1, 2, 3, … let xn+1 = xn – f(xn)/f’(xn). Under “good” conditions the sequence xn will converge to r.

My work
(i) for (x1, f(x1))
y – f(x1) = f’(x1)(x-x1)
y = f’(x1)(x-x1)+f(x1)
(ii) for (x2,0)
0=f’(x1)(x2-x1) + f(x1)
-f(x1) = f’(x1)(x2-x1)
x2 – x1 = -f(x1)/f’(x1)
x2 = x1 – f(x1)/f’(x1)
(iii) for (x2, f(x2))
y – f(x2) = f’(x2)(x-x2)
y = f’(x2)(x-x2)+f(x2)
(ii) for (x3,0)
0=f’(x2)(x3-x2) + f(x2)
-f(x2) = f’(x2)(x3-x2)
x3 – x2 = -f(x2)/f’(x2)
x3 = x2 – f(x2)/f’(x2)
(iv)
f(x) = x^2 –k
f’(x) = 2x
xn+1 = xn – (n^2-k)/(2n)
xn+1 = xn – n/2 – k/(2x)
xn+1 – xn + n/2 = – k/(2x)
(xn+1 – xn + n/2)*(-2n) = k

Any help on the work is greatly appreciated.
 
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  • #2
Any help on the work is greatly appreciated.
Cool! Um... what seems to be the problem?
...
f(x) = x^2 –k
f’(x) = 2x
xn+1 = xn – (n^2-k)/(2n)
... this is where you lose me: what is your reasoning here?
 

1. What is Newton's method of estimation?

Newton's method of estimation is a numerical method used to find the roots of a function. It is a type of iterative process that uses derivatives to approximate the roots of a function.

2. How does Newton's method work?

Newton's method works by using the derivative of a function to find the slope of the tangent line at a given point. This slope is then used to find a more accurate approximation of the root of the function. The process is repeated until a desired level of accuracy is reached.

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

One advantage of using Newton's method is that it can converge to the root of a function faster than other methods, such as the bisection method. It also allows for finding multiple roots of a function simultaneously.

4. What are the limitations of Newton's method?

One limitation of Newton's method is that it requires the function to have a continuous derivative. If the function has multiple roots or is not well-behaved, the method may fail to converge or produce inaccurate results.

5. What are some real-world applications of Newton's method?

Newton's method has various applications in fields such as physics, engineering, and economics. It can be used to solve optimization problems, determine the stability of systems, and model complex systems. It is also commonly used in machine learning algorithms and financial forecasting.

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