I want to have an indexed binary variable so pyomo optimises it to minimise the total sum of the list while picking at least 2 elements. When I remove the (imo redundant) model.q I receive:
ValueError: No variables appear in the Pyomo model constraints or objective. This is not supported by the NL file interface
and the solution pyomo gives me with model.q contains q=0 which violates constraint c1.
5 Declarations: i x q y objective q 0.0 y[0] 1 y[1] 1 y[2] 1
from pyomo.environ import *
# create a model instance
model = ConcreteModel()
#Parameters
model.i = RangeSet(0, 2)
model.x = Param(model.i, initialize=[5,1,2])
#Variables
model.q = Var(domain=Binary, initialize=1)
model.y = Var(model.i, domain=Binary)
#Constraints
model.c1 = model.Constraint(expr=model.q == 1)
model.c2 = model.Constraint(expr=sum(model.y[i] for i in model.i) >= 2)
#Objective function
model.objective = Objective(expr = sum(model.x[i]*model.y[i]*model.q for i in model.i), sense=minimize)
# compute a solution
results = SolverFactory('mindtpy').solve(model, mip_solver='glpk', nlp_solver='ipopt', tee=True)
model.pprint()
Welcome to the site.
You have a couple errors that are causing you problems.
when you construct your parameter, you need to pass in a dictionary so that pyomo can associate the items in the set to the values. You cannot pass in a list and assume things happen sequentially... The set could have any ordering, etc.
You have a hideous typo when making your constraint C2. See my note in code comment
Your variable q is totally unnecessary. And, by multiplying q times y you are making the problem non-linear by multiplying variables.
A little fixed up:
from pyomo.environ import *
# create a model instance
model = ConcreteModel()
#Parameters
model.i = RangeSet(0, 2)
values = {0:5, 1:1, 2:2}
model.x = Param(model.i, initialize=values)
#Variables
#model.q = Var(domain=Binary, initialize=1)
model.y = Var(model.i, domain=Binary)
#Constraints
#model.c1 = model.Constraint(expr=model.q == 1)
# NOTE: you mistakenly had "model.Constraint" which is a sneaky & bad typo!!
model.c2 = Constraint(expr=sum(model.y[i] for i in model.i) >= 2)
#Objective function
model.objective = Objective(expr = sum(model.x[i]*model.y[i] for i in model.i), sense=minimize)
# compute a solution
results = SolverFactory('glpk').solve(model) #, mip_solver='glpk', nlp_solver='ipopt', tee=True)
print(results)
model.display()
model.pprint()
produces (a little long but I think it will help you to look at ALL 3 of these items...
Problem:
- Name: unknown
Lower bound: 3.0
Upper bound: 3.0
Number of objectives: 1
Number of constraints: 2
Number of variables: 4
Number of nonzeros: 4
Sense: minimize
Solver:
- Status: ok
Termination condition: optimal
Statistics:
Branch and bound:
Number of bounded subproblems: 1
Number of created subproblems: 1
Error rc: 0
Time: 0.006919145584106445
Solution:
- number of solutions: 0
number of solutions displayed: 0
Model unknown
Variables:
y : Size=3, Index=i
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : 0.0 : 1 : False : False : Binary
1 : 0 : 1.0 : 1 : False : False : Binary
2 : 0 : 1.0 : 1 : False : False : Binary
Objectives:
objective : Size=1, Index=None, Active=True
Key : Active : Value
None : True : 3.0
Constraints:
c2 : Size=1
Key : Lower : Body : Upper
None : 2.0 : 2.0 : None
1 RangeSet Declarations
i : Dimen=1, Size=3, Bounds=(0, 2)
Key : Finite : Members
None : True : [0:2]
1 Param Declarations
x : Size=3, Index=i, Domain=Any, Default=None, Mutable=False
Key : Value
0 : 5
1 : 1
2 : 2
1 Var Declarations
y : Size=3, Index=i
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : 0.0 : 1 : False : False : Binary
1 : 0 : 1.0 : 1 : False : False : Binary
2 : 0 : 1.0 : 1 : False : False : Binary
1 Objective Declarations
objective : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : minimize : 5*y[0] + y[1] + 2*y[2]
1 Constraint Declarations
c2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 2.0 : y[0] + y[1] + y[2] : +Inf : True
5 Declarations: i x y c2 objective
[Finished in 564ms]