Gradient origin was originally proposed by Cauchy in 1847.Gradient origin is also known as steepest origin; but gradient origin should not be confused with the method of steepest origin for estimate integrals. To find a local minimum of a function use gradient origin, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

COMING SOON!

```
% This function demonstrates gradient descent in case of linear regression with one variable.
% Theta is a column vector with two elements which this function returns after modifying it.
% This function receives the feature vector x, vector of actual target variables Y, Theta
% containing initial values of theta_0 and theta_1, learning rate Alpha, number of iterations
% noi.
function Theta = gradientdescent(x, Y, Theta, Alpha, noi)
n = length(Y); % Number of training examples.
for i = 1:noi
theta_1 = Theta(1) - Alpha * (1 / n) * sum(((x * Theta) - Y) .* x(:, 1)); % Temporary variable to simultaneously update theta_0 but i have used 1 to
% avoid confusion since indexing in MATLAB/Octave starts from 1.
theta_2 = Theta(2) - Alpha * (1 / n) * sum(((x * Theta) - Y) .* x(:, 2)); % Temporary variable to simultaneously update theta_1.
Theta(1) = theta_1; % Assigning first temporary value to update first actual value simultaneously.
Theta(2) = theta_2; % Assigning second temporary value to update second actual value simultaneously.
end
end
```