python-如何显示纸浆中定义的(原始)线性程序的对偶

关于堆栈溢出的第一个问题…喜欢这个网站…

我在Python上使用PuLP.基于输入变量,目标函数和约束条件,我试图查看与初始LP问题(原始)的最优解相关的对偶变量/值.我在搜索文档时遇到麻烦,我只是想清楚一点.有没有办法用纸浆做到这一点？下面有示例代码显示原始输入.

注意：我编辑了我的问题,从提出双重要求到询问双重结果(基于反馈).

注2：根据反馈进行了进一步编辑以澄清.

import pulp
lpProb = pulp.LpProblem("example", pulp.LpMinimize)
x1 = pulp.LpVariable("x1", 0, None, pulp.LpContinuous)
x2 = pulp.LpVariable("x2", 0, None, pulp.LpContinuous)
x3 = pulp.LpVariable("x3", 0, None, pulp.LpContinuous)
lpProb += 3*x1 + 4*x2 + 2*x3
lpProb += x1 + 2*x2 + x3 >= 42
lpProb += 5*x1 + 7*x2 + 4*x3 >= 68
lpProb.solve()
print(pulp.LpStatus[lpProb.status])
for i in lpProb.variables():
    print("Variable {0} = {1}".format(i.name, i.varValue))
print("Objective function z = {0}".format(pulp.value(lpProb.objective)))

解决方法:

@StuartMitchell在对该问题的评论中提出了以下建议.它打印每个约束的名称,对数值和松弛度.

for name, c in list(lpProb.constraints.items()):
    print(name, ":", c, "\t", c.pi, "\t\t", c.slack)

标签：python-3-x,linear-programming,pulp,python
来源： https://codeday.me/bug/20191109/2013160.html