更改索引参数值的最佳方法

时间:2019-11-29 01:41:47

标签: pyomo

我是Pyomo的新手,并且想知道如何更改具有一个或多个索引的现有模型参数的值。

我看过一些标量参数的示例,即没有索引。例如:

model5 = ConcreteModel()
model5.data2 = Param(initialize=10.0, mutable=True)
print("print data2 before")
model5.data2.pprint()
model5.data2 = 999
print("print data2 after")
model5.data2.pprint()

这将产生输出:

print data2 before
data2 : Size=1, Index=None, Domain=Any, Default=None, Mutable=True
    Key  : Value
    None :  10.0
print data2 after
data2 : Size=1, Index=None, Domain=Any, Default=None, Mutable=True
    Key  : Value
    None :   999

但是,如果我尝试使用具有索引的参数来执行此操作,则会收到错误消息。以下代码失败,但可能不足为奇,因为我试图将Python对象分配给Pyomo对象。用索引(或多个索引)更新参数的正确方法是什么?

model5 = ConcreteModel()
# Make a small set
myList = ['i1', 'i2', 'i3', 'i4']
model5.i = Set(dimen=1, initialize=myList)
# Make a dict for each element in the set and give it the value 10
dataDict = {}
for  element in myList:
  dataDict[element] = 10
print("print dataDict")
print(dataDict)
# Make the data into a model Param
model5.data = Param(model5.i, initialize=dataDict, mutable=True)
print("print data parameter")
model5.data.pprint()
# Change a values for each element to 999
for  element in myList:
  dataDict[element] = 999
# Try and update the Param
model5.data = dataDict # THIS FAILS <-- how do I do this?

1 个答案:

答案 0 :(得分:1)

tl,dr :使用要更新(可变!)参数的reconstruct方法。

首先,我的建议是将将模型初始化的过程放入函数中,以便您可以在不同位置调用它并重用它。

from pyomo import environ as pe

def create_model(d):
    """Create Pyomo Concrete Model.

    Parameters
    ----------
    d : dict
        Dictionary with keys corresponding to components names.
    """
    model = pe.ConcreteModel()
    model.I = pe.Set(initialize=d['I'])
    model.data = pe.Param(model.I, mutable=True, initialize=d['data'])
    return model

然后,您可以使用所需的任何数据初始化模型:

d = {}
d['I'] = ['i1', 'i2', 'i3', 'i4']
d['data'] = {i : 10 for i in d['I']}

model = create_model(d)
​
model.data.pprint()

data : Size=4, Index=I, Domain=Any, Default=None, Mutable=True
    Key : Value
     i1 :    10
     i2 :    10
     i3 :    10
     i4 :    10

现在使用reconstruct更新值:

new_values = {i: 5 for i in d['I']} # 5 here is arbitrary, you
model.data.reconstruct(new_values) 
​
model.data.pprint()
data : Size=4, Index=I, Domain=Any, Default=None, Mutable=True
    Key : Value
     i1 :     5
     i2 :     5
     i3 :     5
     i4 :     5

请注意,data是一个确实令人困惑的参数名称,您应该找到更具体的名称。