scipy-stubs

import numpy as np from scipy.optimize import minimize, OptimizeResult from typing import Tuple def rosen(x: np.ndarray) -> np.float64: """The Rosenbrock function""" return np.sum(100.0 * (x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0) def rosen_der(x: np.ndarray) -> np.ndarray: """The Rosenbrock function gradient""" xm = x[1:-1] xm_m1 = x[:-2] xm_p1 = x[2:] grad = np.zeros_like(x) grad[1:-1] = 200.0 * (xm - xm_m1**2) - 400.0 * (xm_p1 - xm**2) * xm - 2.0 * (1 - xm) grad[0] = -400.0 * x[0] * (x[1] - x[0]**2) - 2.0 * (1 - x[0]) grad[-1] = 200.0 * (x[-1] - x[-2]**2) return grad x0: np.ndarray = np.array([0.5, 0.5]) res: OptimizeResult = minimize(rosen, x0, method='BFGS', jac=rosen_der, options={'disp': True}) print(f"Optimization successful: {res.success}") print(f"Function value at minimum: {res.fun}") print(f"Optimal parameters: {res.x}")