我需要使用pythonscipy最大化网络的吞吐量

class optimization():
    def __init__(self,nodes=nodes):
        self.nodes = nodes
        self.estimated_throughput_list=[] # list of throughputs
        for i in self.nodes:
            self.estimated_throughput_list.append(i.estimated_throughput) # adding for each node its throughput to the list
        self.d= np.ones_like(self.nodes, dtype=bool) # creating a boolean matrix having the same dimensions of the estimated_throughput_list

    def objective_function(self):
        """
        Objective function to maximize the sum of Di * xi,
        where Di is a binary variable that can take values of 0 or 1.
        x is an array of values for the variables x1, x2, ..., xn,
        and D is an array of values for the binary variables D1, D2, ..., Dn.
        """  
        return  np.dot(self.d, self.estimated_throughput_list)

    def constraint1(self):
        summation=8* 0.5 # erlang
        summation= summation+ np.dot(self.d, self.estimated_throughput_list)
        return summation
    def solution(self):
        return linprog(c=self.nodes,A_ub=self.estimated_throughput_list,b_ub=[8* 0.5],method="revised simplex")

我有一个节点的全局列表，每个节点包含它的估计吞吐量，由estimated_throughput表示
我试图解决的优化问题是由上传的figure:optimzation problem提出的
为简单起见m *h^(-1)[PDR] is replaced by (8*0.5)
在main.py文件中，我只是调用函数：

opt=optimization()
    
 print("obj:",opt.solution())

这是我第一次在python中应用优化，我不知道这是否是解决这类问题的方法。如果有人能在这个问题上进一步指导我，我将不胜感激。
结果是：c = np.array(c, dtype=np.float64, copy=True).squeeze() TypeError: float() argument must be a string or a real number, not 'myNode'