Maximum likelihood estimation
Gradient descent method

(Note: here it is a maximization problem, so the sign before gradient is flipped)

Gradient descent method requires to calculate the gradient at the rth iteration, but no need to calculate the inverse of second-order derivative, i.e., the Hessian matrix. Therefore, it is computationally faster than Newton-Raphson method.
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