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 np
from scipy.optimize import minimize
from sympy.utilities.lambdify import lambdify
import sympy as sy
sy.init_printing() # LaTeX like pretty printing for IPython
x1, 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+103
f_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)
602392714
清零编程群