Skipping non-existing decision variables in for loop
回答済みHello,
I'm working on an assignment problem that has a binary decision variable set with 4 indices: y,s,i,t, (assigning y to s,i,t) and unfortunately all y,s,i,t are large numbers. Therefore I'm currently working on speeding my existing model. To do so, I wanted to decrease the number of decision variables by creating separate "availability" lists for each y (e.g. y[1] can be assigned to s[1,2,17] and i[2,4,33,56] and t[1:90]). So, I only created decision variables corresponding to that and haven't created: x[1,0,0,0] or x[1,1,2,91].
However, I'm having trouble with my constraints because currently I cannot sum all y from 0:Y for each s,i,t because some y within 0:Y do not exist, since their availability limits are different. (Showed an example constraint below.)
for s,i,t in zip(available_s, available_i, available_t):
m.addConstr(sum(x[y,s,i,t] for y in range(Y))<= c_si[s,i])
I was wondering if there's a way that I can say: sum all "existing y" within the 0:Y limit.
Thank you so much!
Best regards,
Yaren
-
正式なコメント
-
Gurobi Staff
This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum. Or why not try our AI Gurobot?. -
-
-
Hi Yaren,
Could you post a minimal working example reproducing the issue?
In general, you can use the addVars and provide arbitrary lists to generate a multi-indexed variable, e.g.,
A = [0,5,7]
B = [2,8,11]
x = m.addVars(A,B)
# loop over all variables
for a in A:
for b in B:
m.addConstr(x[a,b] >= a*b)Best regards,
Jaromił0 -
-
Hello Jaromił,
Sure, I can! So, let's say our sets are defined this way, though we want them to work with a for loop using "zip" :
Y = [0,1,2,3]
S = [1,2,1,3]
I = [2,6,6,7]
T = [0,1,5,6]
x={}
for y,s,i,t in zip(Y,S,I,T):
x[y,s,i,t] = m.addVar(vtype=GRB.BINARY)(There're only 4 variables created, x[0,1,2,0], x[1,2,6,1], x[2,1,6,5], x[3,3,7,6] )
Then I'm trying to create a constraint for every s,i,t pair:
for s,i,t in zip(S, I, T):
m.addConstr(sum(x[y,s,i,t] for y in range(Y) <= c[s,i])Though as you can imagine I'm getting an error. Because on the first iteration of the for loop I'm trying to sum(x[0,1,2,0], x[1,1,2,0], x[2,1,2,0], x[3,1,2,0]) though x[1,1,2,0], x[2,1,2,0] or x[3,1,2,0] do not exist.
That's why, I was wondering if there's a way that I can say "sum only the existing (predefined) x within a limit".
Best regards,
Yaren
0 -
-
-
Hi Yaren,
Thank you for the clarification. One way to achieve what you want is
for s,i,t in zip(S, I, T):
m.addConstr( sum( x[y,s,i,t] for y in Y if (y,s,i,t) in zip(Y,S,I,T) ) <= c[s,i])Only \(\texttt{y}\) will be used which are actually in the \(\texttt{zip}\) of the 4 lists. Please note that in your example the \(\texttt{sum}\) always only consists of exactly one variable, because there is no 2 \(\texttt{y}\) with the same \(\texttt{s,i,t}\).
Best regards,
Jaromił0 -
投稿コメントは受け付けていません。
コメント
4件のコメント