最小最大优化公式的错误使用PYOMO
我正在为下面写的最小问题问题制定目标功能，
obj_func = power[1, i] for i in range(1, time_slots + 1)
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
但我会收到以下错误
file“ c：\ nmbu \ temp/ipykernel_24484/3898218797.py”，第21行 obj_func = power [1，i] for in范围（1，time_slots + 1）语法：无效语法
如果我使用以下代码，它也会引发错误
obj_func = (power[1, i] for i in range(1, time_slots + 1))
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
valueerror：不允许发电机
，但是我不需要优化总和，我只想优化它以获得最低的功率值[1，i]
obj_func = sum(power[1, i] for i in range(1, time_slots + 1))
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
任何想法使其正常工作，因为PYOMO不直接与Min/Max直接使用？
我已经建模了 power＆gt; =（电池中的功率），因为我无法直接在PYOMO中使用Min/Max。
I am formulating my objective function for a min-max problem as written below
obj_func = power[1, i] for i in range(1, time_slots + 1)
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
but I get the following error
File "C:\NMBU\TEMP/ipykernel_24484/3898218797.py", line 21
obj_func = power[1, i] for i in range(1, time_slots + 1) SyntaxError: invalid syntax
It also throws an error if I use the below code
obj_func = (power[1, i] for i in range(1, time_slots + 1))
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
ValueError: Generators are not allowed
If I use the below formulation it works fine but then I don't need to optimize for the sum, I just want to optimize it to get the lowest value of power[1,i]
obj_func = sum(power[1, i] for i in range(1, time_slots + 1))
model.obj = pyo.Objective(expr=obj_func, sense=pyo.minimize)
Any ideas how to make it work, since PYOMO does not work with min/max directly?
I have already modeled power >= (power in batteries), as I cant directly use min/max in PYOMO.
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您以错误的方式进行操作。 max（）和min（）函数是非线性的，不能在标准线性编程中使用。
如果您的目标是最大程度地减少值集合中的最小值，则需要引入附加变量，称其为
t，生成一组约束，以强制该新变量比每个变量大在每个i 的> ）中的集合元素（t＆gt; = x [i]），然后只用t作为您的目标函数并将其最小化。You are going about this the wrong way. The max() and min() functions are non-linear and can't be used in standard linear programming.
If your goal is to minimize the smallest value in a collection of values, you will need to introduce an additional variable, call it
t, generate a set of constraints to force this new variable to be larger than each of the collection's elements (t >= x[i] for each i) and then just usetas your objective function and minimize it.