问题
I am still very new to Python, after years and years of Matlab. I am trying to use Pulp to set up an integer linear program.
Given an array of numbers:
{P[i]:i=1...N}
I want to maximize:
sum( x_i P_i )
subject to the constraints
A x <= b
A_eq x = b_eq
and with bounds (vector based bounds)
LB <= x <= UB
In pulp however, I don't see how to do vector declarations properly. I was using:
RANGE = range(numpy.size(P))
x = pulp.LpVariable.dicts("x", LB_ind, UB_ind, "Integer")
where I can only enter individual bounds (so only 1 number).
prob = pulp.LpProblem("Test", pulp.LpMaximize)
prob += pulp.lpSum([Prices[i]*Dispatch[i] for i in RANGE])
and for the constraints, do I really have to do this line per line? It seems that I am missing something. I would appreciate some help. The documentation discusses a short example. The number of variables in my case is a few thousand.
回答1:
You can set the lowBound and upBound on variables after the initialization. You can create an array of variables with
LB[i] <= x[i] <= UB[i]
with the following code.
x = pulp.LpVariable.dicts("x", RANGE, cat="Integer")
for i in x.viewkeys():
x[i].lowBound = LB_ind[i]
x[i].upBound = UB_ind[i]
The second parameter to LpVariable.dict is the index set of the decision variables, not their lower bounds.
回答2:
For the first question, you can do it like this in some other problem.
students = range(96)
group = range(24)
var = lp.LpVariable.dicts("if_i_in_group_j", ((i, j) for i in students for j in group),cat='binary')
来源:https://stackoverflow.com/questions/7728313/python-pulp-using-with-matrices