python有哪些求解线性规划的包

# -*- coding: utf-8 -*-

import pulp as pulp

def solve_ilp(objective , constraints) :

    print objective

    print constraints

    prob = pulp.LpProblem('LP1' , pulp.LpMaximize)

    prob += objective

    for cons in constraints :

        prob += cons

    print prob

    status = prob.solve()

    if status != 1 :

        #print 'status'

        #print status

        return None

    else :

        #return [v.varValue.real for v in prob.variables()]

        return [v.varValue.real for v in prob.variables()]

#解如下整数线性规划

#maximize  z = c*x = 3*x1 + 4*x2 + 5*x3

#subject to :

#x1 2 3 >= 0

#x1 + 2*x2 <  20

#x2 + 3*x3 <= 40    

V_NUM = 3

#变量，直接设置下限

variables = [pulp.LpVariable('X%d'%i , lowBound = 0 , cat = pulp.LpInteger) for i in range(0 , V_NUM)]

#目标函数

c = [3 , 4 , 5]

objective = sum([c[i]*variables[i] for i in range(0 , V_NUM)])

#约束条件

constraints = []

a1 = [1 , 2 , 0]

constraints.append(sum([a1[i]*variables[i] for i in range(0 , V_NUM)]) <= 100)

a2 = [0 , 1 , 3]

constraints.append(sum([a2[i]*variables[i] for i in range(0 , V_NUM)]) <= 40)

print constraints

res = solve_ilp(objective , constraints)

print res