gradientdescentminima.py

start_n = 5 # setting the initial point 
lr = 0.001  # setting the learning rate
precision = 0.000001   #setting the initial precision
dr = lambda x: 4 * x**3 - 9 * x**2   # set the gradient of function required 
n = 1000000  #no of iterations
next_n = start_n  
iter = 0  # set initial count to be 0
for i in range(n):   
  current_n = next_n
  next_n = current_n - lr*dr(current_n)  # moving in the negative of direction of gradient calculated 
  print(next_n)                                     
  iter += 1                              # incrementing initial count
  if(abs(current_n - next_n) <= precision):    # stop when required precision reached 
    break
print(f"minimum {next_n}, total iterations: {iter}")