I am trying to create a ConcreteModel using Pyomo. I have a list including the indices of the variable x_ij.
Xindex = [(619, 0), (620, 0), (621, 0), (622, 0), (623, 0), (624, 0), (625, 0), (626, 0), (627, 0), (628, 0), (619, 1), (620, 1), (621, 1), (622, 1), (623, 1), (624, 1), (625, 1), (626, 1), (627, 1), (628, 1), (1098, 2), (1099, 2), (1100, 2), (1101, 2), (1102, 2), (1103, 2), (1104, 2), (1105, 2), (1106, 2), (1107, 2), (1098, 3), (1099, 3), (1100, 3), (1101, 3), (1102, 3), (1103, 3), (1104, 3), (1105, 3), (1106, 3), (1107, 3), (619, 4), (620, 4), (621, 4), (622, 4), (623, 4), (624, 4), (625, 4), (626, 4), (627, 4), (628, 4), (1098, 5), (1099, 5), (1100, 5), (1101, 5), (1102, 5), (1103, 5), (1104, 5), (1105, 5), (1106, 5), (1107, 5), (1098, 6), (1099, 6), (1100, 6), (1101, 6), (1102, 6), (1103, 6), (1104, 6), (1105, 6), (1106, 6), (1107, 6), (1098, 7), (1099, 7), (1100, 7), (1101, 7), (1102, 7), (1103, 7), (1104, 7), (1105, 7), (1106, 7), (1107, 7), (1098, 8), (1099, 8), (1100, 8), (1101, 8), (1102, 8), (1103, 8), (1104, 8), (1105, 8), (1106, 8), (1107, 8), (1098, 9), (1099, 9), (1100, 9), (1101, 9), (1102, 9), (1103, 9), (1104, 9), (1105, 9), (1106, 9), (1107, 9), (1098, 10), (1099, 10), (1100, 10), (1101, 10), (1102, 10), (1103, 10), (1104, 10), (1105, 10), (1106, 10), (1107, 10), (1098, 11), (1099, 11), (1100, 11), (1101, 11), (1102, 11), (1103, 11), (1104, 11), (1105, 11), (1106, 11), (1107, 11), (1098, 12), (1099, 12), (1100, 12), (1101, 12), (1102, 12), (1103, 12), (1104, 12), (1105, 12), (1106, 12), (1107, 12)]
The first element of each tuple corresponds to an i and second one to a j. Using this list, I would like to add my variables to the model m. But, I do not see a straightforward method in the Pyomo User Manual. I tried the following and did not work.
m.Xindexi = Set(initialize=[i[0] for i in Xindex])
m.Xindexj = Set(initialize=[j[1] for j in Xindex])
m.Xindex = Set(within = m.Xindexi*m.Xindexj)
m.x = Var(m.Xindexi, m.Xindexj,domain=NonNegativeReals)
Isn't there a way to do this like a dictionary creation (similar to the one Gurobi's Python uses)? For example:
m.x = {}
for i in Xindex:
m.x[i[0],i[1]] = Var(domain=NonNegativeReals)
Based on the accepted answer, this is what I have done to resolve the issue. Adding it here for future reference...
m.Xindexi = Set(initialize=set([i[0] for i in Xindex]))
m.Xindexj = Set(initialize=set([j[1] for j in Xindex]))
m.Xindex = Set(within = m.Xindexi*m.Xindexj,
initialize=Xindex)
m.x = Var(m.Xindex,domain=NonNegativeReals)
There are a bunch of ways to do this. The basic idea here is to make a sparse set of combinations of (i, j). You can either strictly enumerate that sparse set or if for other parts of your model, you need a fuller set of (i, j) and you only need this sparse set for some of the variables/parameters, you can construct it on-the-fly from data or from any set rules you gin up.
Here are some examples:
# pyomo set patterns
from pyomo.environ import *
m = ConcreteModel()
# initialize two sets from data
I_vals = {1, 5, 9}
m.I = Set(initialize=I_vals)
m.J = Set(initialize={2, 4, 88}) # either way works
# crossed set from list of known combinations
my_sparse_indices = {(1, 4), (1, 88), (5, 4)}
m.IJ = Set(within=m.I * m.J, initialize=my_sparse_indices)
# make a variable X, indexed by this sparse set
m.X = Var(m.IJ, domain=NonNegativeReals)
# make a parameter from the same sparse indices
limit_dict = {(1, 4): 10, (1, 88): 20, (5,4): 30}
m.lim = Param(m.IJ, initialize=limit_dict)
m.pprint()
#############
m2 = ConcreteModel()
m2.I = Set(initialize=range(4))
m2.J = Set(initialize=range(3))
# make full x-product
m2.IJ = Set(within=m2.I * m2.J,
initialize=[(i, j) for i in m2.I for j in m2.J])
# make a sparse set from selected values
selected_vals = [(2,1), (1,1)]
m2.IJ_selected = Set(within=m2.IJ, initialize=selected_vals)
# make a sparse set by excluding prohibited values
prohibited={(2,2), (3,1), (0,0)}
IJ_without_prohibited = {(i, j) for i in m2.I
for j in m2.J
if (i, j) not in prohibited}
m2.IJ_without_prohibited = Set(within=m2.IJ, initialize=IJ_without_prohibited)
m2.pprint()
The output for these is:
4 Set Declarations
I : Dim=0, Dimen=1, Size=3, Domain=None, Ordered=False, Bounds=(1, 9)
[1, 5, 9]
IJ : Dim=0, Dimen=2, Size=3, Domain=IJ_domain, Ordered=False, Bounds=None
[(1, 4), (1, 88), (5, 4)]
IJ_domain : Dim=0, Dimen=2, Size=9, Domain=None, Ordered=False, Bounds=None
Virtual
J : Dim=0, Dimen=1, Size=3, Domain=None, Ordered=False, Bounds=(2, 88)
[2, 4, 88]
1 Param Declarations
lim : Size=3, Index=IJ, Domain=Any, Default=None, Mutable=False
Key : Value
(1, 4) : 10
(1, 88) : 20
(5, 4) : 30
1 Var Declarations
X : Size=3, Index=IJ
Key : Lower : Value : Upper : Fixed : Stale : Domain
(1, 4) : 0 : None : None : False : True : NonNegativeReals
(1, 88) : 0 : None : None : False : True : NonNegativeReals
(5, 4) : 0 : None : None : False : True : NonNegativeReals
6 Declarations: I J IJ_domain IJ X lim
Second model:
6 Set Declarations
I : Dim=0, Dimen=1, Size=4, Domain=None, Ordered=False, Bounds=(0, 3)
[0, 1, 2, 3]
IJ : Dim=0, Dimen=2, Size=12, Domain=IJ_domain, Ordered=False, Bounds=None
[(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2), (3, 0), (3, 1), (3, 2)]
IJ_domain : Dim=0, Dimen=2, Size=12, Domain=None, Ordered=False, Bounds=None
Virtual
IJ_selected : Dim=0, Dimen=2, Size=2, Domain=IJ, Ordered=False, Bounds=None
[(1, 1), (2, 1)]
IJ_without_prohibited : Dim=0, Dimen=2, Size=9, Domain=IJ, Ordered=False, Bounds=None
[(0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (3, 0), (3, 2)]
J : Dim=0, Dimen=1, Size=3, Domain=None, Ordered=False, Bounds=(0, 2)
[0, 1, 2]
6 Declarations: I J IJ_domain IJ IJ_selected IJ_without_prohibited
[Finished in 2.6s]