想要用sympy进行多变量最小化

想要用sympy进行多变量最小化

首先：欢迎来到SO！

据我所知，

lambdify()

不能处理向量。此外，使用Sympy时，确定雅可比很容易。您可以尝试：

import numpy as npfrom scipy.optimize import minimizefrom sympy.utilities.lambdify import lambdifyimport sympy as sysy.init_printing()  # LaTeX like pretty printing for IPythonx1, x2, x3, x4 = sy.symbols('x1 x2 x3 x4')xx = (x1, x2, x3, x4)f = -2*x1**2*x3+6*x1**2*x4+13*x1**2-3*x1*x2**2+x1*x2+3*x1*x3**2-3*x4+103f_n = lambdify(xx, f, modules='numpy')# Build Jacobian:jac_f = [f.diff(x) for x in xx]jac_fn = [lambdify(xx, jf, modules='numpy') for jf in jac_f]def f_v(zz):    """ Helper for receiving vector parameters """    return f_n(zz[0], zz[1], zz[2], zz[3])def jac_v(zz):    """ Jacobian Helper for receiving vector parameters """    return np.array([jfn(zz[0], zz[1], zz[2], zz[3]) for jfn in jac_fn])bnds = ((-1, 1), (-1, 1), (-1, 1), (-1, 1))zz0 = np.array([1, 1, 1, 1])rslts = minimize(f_v, zz0, method='SLSQP', jac=jac_v, bounds=bnds)print(rslts)