GradientDescent

x = 2           # Initial value (starting point)
lr = 0.01       # Learning rate
precision = 0.000001  # Stop when the change in x is smaller than this
previous_step_size = 1  # Initialize to a large value
max_iter = 10000  # Maximum number of iterations to prevent infinite loop
iter = 0         # Iteration counter
gf = lambda x: (x + 3)**2  # Define the gradient of the function

import matplotlib.pyplot as plt

gd = []  # List to store the values of x during each iteration

while precision < previous_step_size and iter < max_iter:
    prev = x
    x = x - lr * gf(prev)  # Update x using the gradient descent formula
    previous_step_size = abs(x - prev)
    iter += 1
    print('Iteration', iter, 'Value:', x)
    gd.append(x)  # Append the current value of x to the list

print('Local Minima:', x)

plt.plot(gd)