[英]DOcplex: the dual of the dual is not the same as primal?
I have a very simple primal problem defined like this:我有一个非常简单的原始问题,定义如下:
mdl = Model(name='tubs_primal')
aqua = mdl.continuous_var(name='aqua')
hydro = mdl.continuous_var(name='hydro')
typhoon = mdl.continuous_var(name='typhoon')
pump = mdl.add_constraint(aqua + hydro + typhoon <= 200, 'pump')
hour = mdl.add_constraint(9*aqua + 6*hydro + 8*typhoon<= 1566, 'hour')
tubing = mdl.add_constraint(12*aqua+16*hydro + 13*typhoon <= 2880, 'tubing')
profit = 350*aqua + 300*hydro + 320*typhoon
mdl.maximize(profit)
I also wrote down its dual problem like this我也这样写下它的对偶问题
mdl = Model(name='tubs_dual')
pump = mdl.continuous_var(name='pump', lb=None)
hour = mdl.continuous_var(name='hour', lb=None)
tubing = mdl.continuous_var(name='tubing', lb=None)
aqua = mdl.add_constraint(pump + 9*hour + 12*tubing >= 350, 'aqua')
hydro = mdl.add_constraint(pump + 6*hour + 16*tubing >= 300, 'hydro')
typhoon = mdl.add_constraint(pump + 8*hour + 13*tubing >= 320, 'typhoon')
cost = 200*pump + 1566*hour + 2880*tubing
mdl.minimize(cost)
The primal code works perfect.原始代码完美无缺。 However, when I ran the dual problem, CPLEX tells me that the dual objective and primal objective are different.
但是,当我运行对偶问题时,CPLEX 告诉我对偶目标和原始目标是不同的。 So the dual problem of a dual is not same as primal?
那么对偶的对偶问题与原始问题不同吗? I am quite puzzled.
我很纳闷。 Any help?
有什么帮助吗? Thanks.
谢谢。
Iteration log . . .
Iteration: 1 Dual objective = 60900.000000
objective: 66100.000
pump=200.000
hour=16.667
The reason is because of not full log display.原因是因为没有完整的日志显示。 Suppose you solve the 2 models primal and dual with primal method
假设您使用原始方法求解 2 个模型 primal 和 dual
from docplex.mp.model import Model
mdl = Model(name='tubs_primal')
aqua = mdl.continuous_var(name='aqua')
hydro = mdl.continuous_var(name='hydro')
typhoon = mdl.continuous_var(name='typhoon')
pump = mdl.add_constraint(aqua + hydro + typhoon <= 200, 'pump')
hour = mdl.add_constraint(9*aqua + 6*hydro + 8*typhoon<= 1566, 'hour')
tubing = mdl.add_constraint(12*aqua+16*hydro + 13*typhoon <= 2880, 'tubing')
profit = 350*aqua + 300*hydro + 320*typhoon
mdl.maximize(profit)
mdl.parameters.lpmethod=1
mdl.solve(log_output=True)
print("obj=",mdl.objective_value)
mdl2 = Model(name='tubs_dual')
pump = mdl2.continuous_var(name='pump', lb=None)
hour = mdl2.continuous_var(name='hour', lb=None)
tubing = mdl2.continuous_var(name='tubing', lb=None)
aqua = mdl2.add_constraint(pump + 9*hour + 12*tubing >= 350, 'aqua')
hydro = mdl2.add_constraint(pump + 6*hour + 16*tubing >= 300, 'hydro')
typhoon = mdl2.add_constraint(pump + 8*hour + 13*tubing >= 320, 'typhoon')
cost = 200*pump + 1566*hour + 2880*tubing
mdl2.minimize(cost)
mdl2.parameters.lpmethod=1
mdl2.solve(log_output=True)
print("obj=",mdl2.objective_value)
Then you will get然后你会得到
Iteration log . . .
Iteration: 1 Objective = 60900.000000
obj= 66100.0
Iteration log . . .
Iteration: 1 Scaled infeas = 0.000000
Switched to devex.
Iteration: 2 Objective = 66100.000000
obj= 66100.0
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