Gradient Descent

Problem

Minimize f(x) = x^2 using gradient descent.

Start at x = start. For n_iters iterations: grad = 2 * x x = x - learning_rate * grad

Return the final value of x.

Hint

df/dx = 2x. Update rule: x = x - lr * 2x.

case 1

in: [10,0.1,100]

expected: 0

case 2

in: [5,0.05,50]

expected: 0.02576887603660058

case 3

in: [1,0.5,1]

expected: 0

case 4

in: [0,0.1,100]

expected: 0

Starter codePython

def gradient_descent(start, learning_rate, n_iters):
    # Minimize f(x) = x ** 2
    # Your code here
    pass

4 test cases · run your code against them in the editor