Gradient Descent Converging to Initial Values-CodePudding

Im trying to find optimal point of a function using gradient descent, however it is converging to initial conditions only. May be my code or thinking is incorrect. Please provide intuition to this.

Thank you.

My Code: Initial value is 10000 and the solution is converging to 10000 instead of actual solution 1.

import numpy.linalg as nl
x_ini=10000

def obj(x):
    f = x**2 - 2*x   3
    return f

def grad(x):
    df = 2*x - 2
    return df

n_iter=1000
lr=0.001

x_old = x_ini


for _ in range(True):
    
    x_new = x_old - lr*(grad(x_old))
    x_old = x_new
    
    if(nl.norm(grad(x_old))<=1e-03):
        break
    x_new = x_old
    
print(x_new)

CodePudding user response：

while True:
    
    x_new = x_old - lr*(grad(x_old))
    x_old = x_new
    y = nl.norm(grad(x_old))
    if(y<=1e-03):
        break
    x_new = x_old

print(x_new)

You can change for to while