newton raphson method algorithm


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PDF The Newton-Raphson Method

The Newton-Raphson method or Newton Method is a powerful technique for solving equations numerically Like so much of the di erential calculus it is based on the simple idea of linear approximation The Newton Method properly used usually homes in on a root with devastating e ciency

  • How to solve a non-linear equation using Newton Raphson method?

    An algorithm for Newton Raphson method requires following steps in order to solve any non-linear equation with the help of computational tools: 1. Start 2. Define function as f (x) 3. Define first derivative of f (x) as g (x) 4. Input initial guess (x0), tolerable error (e) and maximum iteration (N) 5. Initialize iteration counter i = 1 6.

  • What is an algorithm for Newton Raphson method?

    And an algorithm for Newton Raphson method involves repetition of above process i.e. we use x1 to find x2 and so on until we find the root within desired accuracy. An algorithm for Newton Raphson method requires following steps in order to solve any non-linear equation with the help of computational tools:

  • Is Raphson a linear approximation?

    For polynomials, Raphson’s procedure is equivalent to linear approximation. Raphson, like Newton, seems unaware of the connection between his method and the derivative. The connection was made about 50 years later (Simpson, Euler), and the Newton Method nally moved beyond polynomial equations.

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Newton-Raphson Method Algorithm

The Newton-Raphson method is an iterative numerical technique used for finding successively better approximations to the roots (or zeroes) of a real-valued function. It's widely used in various fields, including engineering, physics, and mathematics.

This tutorial provides a comprehensive overview of the Newton-Raphson method algorithm, including examples, exercises, case studies, and important notes to enhance understanding and application.

Examples

1. Finding the square root of a number using the Newton-Raphson method.

2. Solving a nonlinear equation such as \(x^2 - 4x + 3 = 0\) using the Newton-Raphson method.

3. Calculating the inverse square root using the Newton-Raphson method.

4. Estimating the solution to a transcendental equation like \(e^x - x - 2 = 0\).

5. Approximating the solution to a trigonometric equation such as \(\sin(x) - x = 0\).

Exercises with Solutions

  1. Find the square root of 5 using the Newton-Raphson method.

    Solution:

    Let \(f(x) = x^2 - 5\). Applying the Newton-Raphson formula:
    \(x_{n+1} = x_n - \frac{f(x_n)}{f'(x_n)}\)

    Starting with an initial guess \(x_0 = 2\),
    \(x_1 = 2 - \frac{2^2 - 5}{2 \times 2} = \frac{7}{4} = 1.75\)
    \(x_2 = 1.75 - \frac{1.75^2 - 5}{2 \times 1.75} \approx 2.0714\)

    Iterating further, we converge to \(x \approx 2.2361\).

    Correct Answer: \(x \approx 2.2361\)

Multiple Choice Questions (MCQ) with Answers

  1. What is the key idea behind the Newton-Raphson method?
    a) Linear interpolation
    b) Iterative approximation
    c) Matrix inversion
    d) Polynomial regression
    Correct Answer: b) Iterative approximation

Case Studies

Case Study 1: Application of the Newton-Raphson method in calculating the electrical power output of a solar panel based on varying sunlight intensity.

Case Study 2: Use of the Newton-Raphson method to determine the optimal shape of a parabolic mirror for focusing light in a solar concentrator system.

Case Study 3: Implementation of the Newton-Raphson method in optimizing the trajectory of a spacecraft for interplanetary missions.

Case Study 4: Application of the Newton-Raphson method in financial modeling for estimating future stock prices based on historical data.

Case Study 5: Use of the Newton-Raphson method in medical imaging for reconstructing three-dimensional structures from two-dimensional X-ray images.

Important Notes

  1. The Newton-Raphson method requires an initial guess close to the root for convergence.
  2. Convergence is not guaranteed for all functions or initial guesses, and oscillation or divergence may occur.
  3. The method may require fewer iterations and provide faster convergence compared to other root-finding techniques.
  4. The choice of initial guess and the derivative function significantly impact the efficiency and accuracy of the method.
  5. The Newton-Raphson method is widely used in computer algorithms, scientific simulations, and engineering design.




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