Dependence on Initial Point
8. Using the function shown in the figure, and, for each initial estimate x_0, determine graphically what happens to the sequence of Newton’s method approximations

c. x_0=2

Verified step by step guidance

1. Newton's method is an iterative process used to find successively better approximations to the roots (or zeroes) of a real-valued function.
2. The formula for Newton's method is: x_{n+1} = x_n - \frac{f(x_n)}{f'(x_n)}. This formula uses the function value and its derivative at the current approximation to find the next approximation.
3. For the initial estimate x_0 = 2, locate this point on the graph. At x = 2, observe the function value f(2) and the slope of the tangent line, which is given by the derivative f'(2).
4. Graphically, draw the tangent line at x = 2. The point where this tangent line intersects the x-axis is the next approximation, x_1.
5. Repeat the process using x_1 as the new initial estimate. Continue iterating until the sequence converges to a root or until the approximations stabilize.

Key Concepts

Newton's Method

Newton's Method is an iterative numerical technique used to find approximate solutions to equations. It starts with an initial guess and refines it using the function's derivative. The formula x_{n+1} = x_n - f(x_n)/f'(x_n) updates the guess based on the function's slope, allowing convergence to a root, provided the initial guess is sufficiently close.

Convergence and Divergence

In the context of iterative methods like Newton's, convergence refers to the process where successive approximations get closer to the actual root of the function. Divergence occurs when the approximations move away from the root, often influenced by the choice of the initial estimate. Understanding these behaviors is crucial for predicting the success of the method.

Graphical Interpretation

Graphical interpretation involves analyzing the function's graph to understand the behavior of Newton's Method visually. By plotting the function and its tangent lines at various initial estimates, one can observe how the approximations evolve. This visual approach helps in identifying regions of convergence and divergence, enhancing comprehension of the method's effectiveness.