求解后显示约束值

Question

我有这个简单的产品组合 L.P :

from pulp import *
x = pulp.LpVariable("x", lowBound=0)
y = pulp.LpVariable("y", lowBound=0)

problem = pulp.LpProblem("A simple max problem", pulp.LpMaximize)

problem += 300*x + 250*y, "The objective function"

# Constraints
problem += 1.4*x + y <= 35000, "1st constraint"
problem += 0.51*x + y <= 17000, "2nd constraint"
problem += x <= 22500, "3rd constraint"
problem += y <= 15000, "4th constraint"

problem.solve()

print "Optimal Result:"
for variable in problem.variables():
    print variable.name, "=", variable.varValue

print "Total net max profit:"
print value(problem.objective)

Optimal Result:
x = 20224.719
y = 6685.3933
Total net max profit:
7738764.025

如何显示“已填充”约束计算出的解决方案？ :

让我们采用第一个约束，我需要进行此计算并显示它，我将 x 和 y 替换为最佳值：

1.4*20224.719 + 6685.3933 = 34999,9999

我需要的是输出这样的东西，列出每个约束“左侧计算”：

{"1st constraint solution" : 34999,9999 ,
"2nd constraint solution" : 16999,9999 ,
"3rd constraint" solution" :20224.719 ,
"4th constraint" solution" : 6685.3933 }

等...

在此示例中，值非常接近，但情况并非总是如此...

我认为它叫做“左侧” 非常感谢，我找不到任何关于此的文档，是否有任何代码准备好执行此操作？

Answer 1

欢迎来到本站。

我已经扩充了您的示例（下方）以展示如何访问 pulp 中的约束值和松弛。您的示例似乎是用 python 的早期版本（3.0 之前）编写的，打印语句中缺少括号就证明了这一点。如果可能的话，您应该转向更现代的安装。

为了在 pulp 中找到这些功能，我发现有时 dox 很好，但很多时候，我只是潜入一个交互式环境 (ipython) 并使用 auto-complete查看提供的内容并进行探索的功能。 :) 您命名了您的约束，因此下面的名称与名称配合得很好。它应该可以在没有名称的情况下正常工作，并且您可以从字典中打印出约束值，即表达式。

代码

from pulp import *
x = LpVariable("x", lowBound=0)
y = LpVariable("y", lowBound=0)

problem = pulp.LpProblem("A simple max problem", LpMaximize)

problem += 300*x + 250*y, "The objective function"

# Constraints
problem += 1.4*x + y <= 35000, "1st constraint"
problem += 0.51*x + y <= 17000, "2nd constraint"
problem += x <= 22500, "3rd constraint"
problem += y <= 15000, "4th constraint"

problem.solve()

print ("Optimal Result:")
for variable in problem.variables():
    print (variable.name, "=", variable.varValue)

print ("Total net max profit:")
print (value(problem.objective))

constraints = problem.constraints
print(f'The constraints are held in a {type(constraints)}')

for name in constraints.keys():
    value = constraints.get(name).value()
    slack = constraints.get(name).slack
    print(f'constraint {name} has value: {value:0.2e} and slack: {slack:0.2e}')

产量：

...
Optimal Result:
x = 20224.719
y = 6685.3933
Total net max profit:
7738764.025
The constraints are held in a <class 'collections.OrderedDict'>
constraint 1st_constraint has value: -1.00e-04 and slack: -0.00e+00
constraint 2nd_constraint has value: -1.00e-05 and slack: -0.00e+00
constraint 3rd_constraint has value: -2.28e+03 and slack: 2.28e+03
constraint 4th_constraint has value: -8.31e+03 and slack: 8.31e+03

求解后显示约束值

Show constraints values ​after solving

pulp

代码

产量：

Show constraints values after solving