How To Write Newton Raphson in Matlab

Sure, here's an example of how you could implement the Newton-Raphson method in MATLAB to find the root of a function:

```matlab function [root, iterations] = newtonRaphson(func, initialGuess, tolerance, maxIterations) syms x; % Define symbolic variable x f = matlabFunction(func); % Convert input function to a MATLAB function

% Initialize variables iterations = 0; root = initialGuess;

% Iterate until tolerance or maximum iterations reached while abs(f(root)) > tolerance && iterations < maxIterations % Calculate the derivative of the function df = diff(func, x); df = matlabFunction(df);

% Newton-Raphson formula root = root - f(root) / df(root);

iterations = iterations + 1; end

% Check if the method converged if abs(f(root)) > tolerance disp('The method did not converge'); else disp('Root found'); end end ```

To use this function, you'd call it with the following parameters:

• func: The function for which you want to find the root. For example, @(x) x^2 - 4.
• initialGuess: An initial guess for the root.
• tolerance: The desired tolerance (how close to zero you want the function value to be).
• maxIterations: Maximum number of iterations before the method stops.

For instance:

```matlab func = @(x) x^2 - 4; % Define the function initialGuess = 3; % Initial guess tolerance = 1e-6; % Tolerance maxIterations = 100; % Maximum iterations

[root, iterations] = newtonRaphson(func, initialGuess, tolerance, maxIterations); disp(['Root: ', num2str(root)]); disp(['Number of iterations: ', num2str(iterations)]); ```

This code would find the root of the function (f(x) = x^2 - 4) starting from an initial guess of 3, with a tolerance of (10^{-6}) and a maximum of 100 iterations. Adjust the function, initial guess, tolerance, and maximum iterations according to your requirements.

Professional Academic Writing Service 👈

Check our previous article: How To Write News Stories for Radio