Python 想用sympy实现多变量最小化吗

我想用符号化字符对scipy.optimize进行最小化

from scipy.optimize import minimize
from sympy.utilities.lambdify import lambdify
import sympy as sp


x1, x2, x3, x4 = sp.symbols('x1 x2 x3 x4')

FormulaMain = sp.symbols('-2*x1**2*x3+6*x1**2*x4+13*x1**2-3*x1*x2**2+x1*x2+3*x1*x3**2-3*x4+103')
HandleMain  = lambdify((x1,x2,x3,x4),FormulaMain,'numpy')
bnds = ((-1, 1), (-1, 1), (-1, 1), (-1, 1))

PrintParams  = minimize(HandleMain,[1,1,1,1],method='SLSQP',bounds=bnds)

print PrintParams

当我运行代码时，我得到

<lambda>() takes exactly 4 arguments (1 given)

（）正好接受4个参数（给定1个）

我想我已经用[1,1,1,1]输入了4个参数

---

代码有什么需要更改的吗？

首先：欢迎使用SO

据我所知，

lambdify（）

无法处理向量。此外，当使用辛矩阵时，确定雅可比矩阵很容易。你可以试试：

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)

---

谢谢你提供的信息。它工作得非常好。但我未能添加约束，出现了类似的错误。是否可以添加类似于f的公式？您可以使用*arr将数组解包为多个参数