Newton's Method for Finding Roots

Read this section. Work through practice problems 1-6.

The Algorithm for Newton's Method

Rather than deal with each particular function and starting point, let's find a pattern for a general function \mathrm{f}. For the starting point \mathrm{x}_{0}, the slope of the tangent line at the point \left(\mathrm{x}_{0}, \mathrm{f}\left(\mathrm{x}_{0}\right)\right) is \mathrm{f}^{\prime}\left(\mathrm{x}_{0}\right) so the equation of the tangent line is y-f\left(x_{0}\right)=f^{\prime}\left(x_{0}\right)\left(x-x_{0}\right). This line intersects the x-axis when y=0, so

is y-f\left(x_{0}\right)=f^{\prime}\left(x_{0}\right)\left(x-x_{0}\right). This line intersects the x-axis when y=0, so 0-\mathrm{f}\left(\mathrm{x}_{0}\right)=\mathrm{f}^{\prime}\left(\mathrm{x}_{0}\right)\left(\mathrm{x}-\mathrm{x}_{0}\right) and \mathrm{x}_{1}=\mathrm{x}=\mathrm{x}_{0}-\frac{\mathrm{f}\left(\mathrm{x}_{0}\right)}{\mathrm{f}^{\prime}\left(\mathrm{x}_{0}\right)}. Starting with \mathrm{x}_{1} and repeating this process we have \mathrm{x}_{2} =x_{1}-\frac{f\left(x_{1}\right)}{f^{\prime}\left(x_{1}\right)}; starting with x_{2}, we get x_{3}=x_{2}-\frac{f\left(x_{2}\right)}{f^{\prime}\left(x_{2}\right)} ; and so on.

In general, if we start with x_{n}, the line tangent to the graph of f at the point \left(x_{n}, f\left(x_{n}\right)\right) intersects the x-axis at the point x_{n+1}=x_{n}-\frac{f\left(x_{n}\right)}{f^{\prime}\left(x_{n}\right)}, our new estimate for the root of f.

Algorithm for Newton's Method:

(1) Pick a starting value \mathrm{x}_{0} (preferably close to a root of \mathrm{f}).

(2) For each estimate \mathrm{x}_{\mathrm{n}}, calculate a new estimate \mathrm{x}_{\mathrm{n}+1}=\mathrm{x}_{\mathrm{n}}-\frac{\mathrm{f}\left(\mathrm{x}_{\mathrm{n}}\right)}{\mathrm{f}^{\prime}\left(\mathrm{x}_{\mathrm{n}}\right)}.

(3) Repeat step (2) until the estimates are "close enough" to a root or until the method "fails".

When the algorithm for Newton's method is used with \mathrm{f}(\mathrm{x})=\mathrm{x}^{2}-5, the function at the beginning of this section, we have \mathrm{f}^{\prime}(\mathrm{x})=2 \mathrm{x} so

\begin{aligned} x_{n+1} &=x_{n}-\frac{f\left(x_{n}\right)}{f^{\prime}\left(x_{n}\right)}=x_{n}-\frac{x_{n}^{2}-5}{2 x_{n}}=\frac{2 x_{n}^{2}-\left(x_{n}^{2}-5\right)}{2 x_{n}} \\ &=\frac{x_{n}^{2}+5}{2 x_{n}}=\frac{1}{2}\left\{x_{n}+\frac{5}{x_{n}}\right\} \end{aligned}

The new approximation, x_{n+1}, is the average of the previous approximation, x_{n}, and \mathrm{5} divided by the previous approximation, 5 / \mathrm{x}_{\mathrm{n}}. Problem 16 asks you to show that this pattern, called Heron's method, approximates the square root of any positive number. Just replace the "\mathrm{5}" with the number whose square root you want.


Example 1: Use Newton's method to approximate the root(s) of \mathrm{f}(\mathrm{x})=2 \mathrm{x}+\mathrm{x} \sin (\mathrm{x}+3)-5. Solution: \mathrm{f}^{\prime}(\mathrm{x})=2+\mathrm{x} \cos (\mathrm{x}+3)+\sin (\mathrm{x}+3) so

x_{n+1}=x_{n}-\frac{f\left(x_{n}\right)}{f^{\prime}\left(x_{n}\right)}=x_{n}-\frac{2 x_{n}+x_{n} \sin \left(x_{n}+3\right)-5}{2+x_{n} \cos \left(x_{n}+3\right)+\sin \left(x_{n}+3\right)}

The graph of \mathrm{f}(\mathrm{x}) for -4 \leq \mathrm{x} \leq 6 (Fig. 8) indicates only one root of \mathrm{f}, and that root is near \mathrm{x}=3 so pick \mathrm{x}_{0}=3. Then Newton's method yields the values \mathrm{x}_{0}=3, \mathrm{x}_{1}=\underline{2.96484457} \mathrm{x}_{2}=\underline{2.96446277}, \mathrm{x}_{3}=\underline{2.96446273} \quad (the underlined digits agree with the exact root).


If we had picked \mathrm{x}_{0}=4, Newton's method would have required \mathrm{4} iterations to get \mathrm{9} digits of accuracy. If \mathrm{x}_{0}=5, then \mathrm{7} iterations are needed to get \mathrm{9} digits of accuracy. If we pick x_{0}=5.1, then the values of x_{n} are not close to the actual root after even \mathrm{100} iterations, \mathrm{x}_{100} \approx \, -49.183. Picking a good value for \mathrm{x}_{0} can result in values of \mathrm{x}_{\mathrm{n}} which get close to the root quickly. Picking a poor value for \mathrm{x}_{0} can result in \mathrm{x}_{\mathrm{n}} values which take longer to get close to the root or which don't approach the root at all.

Note: An examination of the graph of the function can help you pick a "good" \mathbf{x}_{0}.


Practice 3: Put \mathrm{x}_{0}=3 and use Newton's method to find the first two iterates, x_{1} and x_{2}, for the function f(x)=x^{3}-3 x^{2}+x-1


Example 2: The function in Fig. 9 has roots at \mathrm{x}=3 and x=7. If we pick x_{0}=1 and apply Newton's method, which root do the iterates, the \mathrm{x}_{\mathrm{n}}, approach?


Solution: The iterates of \mathrm{x}_{0}=1 are labeled in Fig. 10. They are approaching the root at \mathrm{7}.


Practice 4: For the function in Fig. 11, which root do the iterates of Newton's method approach if (a) x_{0}=2?


(b) \mathrm{x}_{0}=3?

(c) \mathrm{x}_{0}=5?