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  • Describe the steps of Newton’s method.
  • Explain what an iterative process means.
  • Recognize when Newton’s method does not work.
  • Apply iterative processes to various situations.

In many areas of pure and applied mathematics, we are interested in finding solutions to an equation of the form f ( x ) = 0 . For most functions, however, it is difficult—if not impossible—to calculate their zeroes explicitly. In this section, we take a look at a technique that provides a very efficient way of approximating the zeroes of functions . This technique makes use of tangent line approximations and is behind the method used often by calculators and computers to find zeroes.

Describing newton’s method

Consider the task of finding the solutions of f ( x ) = 0 . If f is the first-degree polynomial f ( x ) = a x + b , then the solution of f ( x ) = 0 is given by the formula x = b a . If f is the second-degree polynomial f ( x ) = a x 2 + b x + c , the solutions of f ( x ) = 0 can be found by using the quadratic formula. However, for polynomials of degree 3 or more, finding roots of f becomes more complicated. Although formulas exist for third- and fourth-degree polynomials, they are quite complicated. Also, if f is a polynomial of degree 5 or greater, it is known that no such formulas exist. For example, consider the function

f ( x ) = x 5 + 8 x 4 + 4 x 3 2 x 7 .

No formula exists that allows us to find the solutions of f ( x ) = 0 . Similar difficulties exist for nonpolynomial functions. For example, consider the task of finding solutions of tan ( x ) x = 0 . No simple formula exists for the solutions of this equation. In cases such as these, we can use Newton’s method to approximate the roots.

Newton’s method    makes use of the following idea to approximate the solutions of f ( x ) = 0 . By sketching a graph of f , we can estimate a root of f ( x ) = 0 . Let’s call this estimate x 0 . We then draw the tangent line to f at x 0 . If f ( x 0 ) 0 , this tangent line intersects the x -axis at some point ( x 1 , 0 ) . Now let x 1 be the next approximation to the actual root. Typically, x 1 is closer than x 0 to an actual root. Next we draw the tangent line to f at x 1 . If f ( x 1 ) 0 , this tangent line also intersects the x -axis, producing another approximation, x 2 . We continue in this way, deriving a list of approximations: x 0 , x 1 , x 2 ,… . Typically, the numbers x 0 , x 1 , x 2 ,… quickly approach an actual root x * , as shown in the following figure.

This function f(x) is drawn with points (x0, f(x0)), (x1, f(x1)), and (x2, f(x2)) marked on the function. From (x0, f(x0)), a tangent line is drawn, and it strikes the x axis at x1. From (x0, f(x0)), a tangent line is drawn, and it strikes the x axis at x2. If a tangent line were drawn from (x2, f(x2)), it appears that it would come very close to x*, which is the actual root. Each tangent line drawn in this order appears to get closer and closer to x*.
The approximations x 0 , x 1 , x 2 ,… approach the actual root x * . The approximations are derived by looking at tangent lines to the graph of f .

Now let’s look at how to calculate the approximations x 0 , x 1 , x 2 ,… . If x 0 is our first approximation, the approximation x 1 is defined by letting ( x 1 , 0 ) be the x -intercept of the tangent line to f at x 0 . The equation of this tangent line is given by

y = f ( x 0 ) + f ( x 0 ) ( x x 0 ) .

Therefore, x 1 must satisfy

f ( x 0 ) + f ( x 0 ) ( x 1 x 0 ) = 0 .

Solving this equation for x 1 , we conclude that

x 1 = x 0 f ( x 0 ) f ( x 0 ) .

Similarly, the point ( x 2 , 0 ) is the x -intercept of the tangent line to f at x 1 . Therefore, x 2 satisfies the equation

x 2 = x 1 f ( x 1 ) f ( x 1 ) .

In general, for n > 0 , x n satisfies

x n = x n 1 f ( x n 1 ) f ( x n 1 ) .

Next we see how to make use of this technique to approximate the root of the polynomial f ( x ) = x 3 3 x + 1 .

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Source:  OpenStax, Calculus volume 1. OpenStax CNX. Feb 05, 2016 Download for free at http://cnx.org/content/col11964/1.2
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