---

def gradient_descent(f, grad, x_start, lr=0.01, eps=1e-7, max_it=10000):
    x = x_start.copy()
    for i in range(max_it):
        g = grad(x)
        if np.linalg.norm(g) < eps:
            break
        x = x - lr * g
    return x, f(x), i

# Тестирование
res_grad_custom, f_grad_custom, it_grad = gradient_descent(f, grad_f, x_start)

# Сравнение с BFGS
res_bfgs_scipy = minimize(f, x_start, method='BFGS', jac=grad_f)

print("\n--- Градиентный метод ---")
print(f"Своя реализация: точка {res_grad_custom}, f = {f_grad_custom}, итераций: {it_grad}")
print(f"Scipy (BFGS):    точка {res_bfgs_scipy.x}, f = {res_bfgs_scipy.fun}")