数学建模__非线性规划Python实现

from scipy.optimize import minimize
import numpy as np

#定义目标函数
def fun(服务器托管网args):
    a,b,c,d = args
    v = lambda x: (a+x[0])/ (b+x[1]) - c*x[0] + d*x[2]
    return v

#定义约束条件
def con(args):
    # 约束条件 分为eq 和ineq
    # eq表示 函数结果等于0 ； ineq 表示 表达式大于等于0  

    x1min,x1max,x2min,x2max,x3min,x3max = args
    cons = ({'type':'ineq', 'fun': lambda x : x[0] - x1min},
            {'type':'ineq', 'fun': lambda x : -x[0] + x1max},
            {'type':'ineq', 'fun': lambda x : x[1] - x2min},
            {'type':'ineq', 'fun': lambda x : -x[1] + x2min},
            {'type':'ineq', 'fun': lambda x : x[2] - x3min},
            {'type':'ineq', 'fun': lambda x : -x[2] + x3min},

    )

    return cons

#定义常量值
args = (2,1,3,4)
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#设置变量约束条件
args2 = (0.1,0.9,0.1,0.9,0.1,0.9)
cons = con(args2)


#设置初始随机值
x0 = np.asarray((0.5,0.5,0.5))
res = minimize(fun(args), x0, method='SLSQP', constraints=cons)
res