Gradient Descent Optimization for Java and Python

What is gradient descent optimization?

How does gradient descent work in minimizing a function?

Answer:

Gradient descent is an iterative optimization algorithm commonly used to minimize a function by iteratively adjusting the parameters. It calculates the gradient of the function to be minimized and updates the current guess by subtracting the product of the learning rate and gradient.

Gradient descent optimization is a fundamental algorithm for minimizing functions, especially in machine learning and optimization tasks. The algorithm works by starting with an initial guess and iteratively updating the guess based on the gradient of the function to be minimized.

The process involves calculating the gradient at the current guess using a function named `compute_gradient()`. This gradient represents the direction of steepest decrease in the function. The learning rate is then used to determine the step size for updating the guess. By subtracting the product of the learning rate and gradient from the current guess, the algorithm moves towards the optimal solution.

Iterating this process for a specified number of iterations allows the algorithm to converge towards the minimum of the function. The final guess obtained after the iterations represents the optimized solution found by gradient descent.