带约束的最小化方程组(scipy.optimize.minimize) [英] Minimize System of Equations with constraints (scipy.optimize.minimize)

问题描述

以下代码:

import numpy as np
from scipy.optimize import minimize

def eq( p ):
    s1,s2,s3 = p 
    f1 = 1.1**3 / s1*1.1**1+s2*1.1**2+s3*1.1**3
    f2 = 0.9**1 / s1*0.9**1+s2*0.9**2+s3*0.9**3
    return (f1, f2) 

bnds = ( (0, None), (0, None), (0, None) )
cons = ( { 'type' : 'ineq', 'fun': lambda p: p[0]+[p1]+[p2] - 1} )


minimize( eq, (0.3,0.3,0.3),  bounds=bnds, constraints=cons )

引发错误

TypeError: unsupported operand type(s) for -: 'tuple' and 'tuple'

我想最小化f1和f2，以使s_t > 0和sum s_t <= 1对于t = 1、2、3.

I want to minimize f1 and f2 such that the s_t > 0 and sum s_t <= 1, for t = 1, 2, 3.

推荐答案

minimize( eq, (0.3,0.3,0.3),  bounds=bnds, constraints=cons )

第二个参数应该是一个ndarray而不是一个元组. args元组在初始猜测(x0)之后出现.

The second argument should be an ndarray not a tuple. The args tuple comes after the initial guess (x0).

http://docs. scipy.org/doc/scipy-0.17.0/reference/generation/scipy.optimize.minimize.html

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