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Create different restrictions with a single rule

I am having trouble creating multiple constraints with a single rule. The problem is the following:
I have "n" decision variables in my problem and I have "m" constraints formed by the sum of some of the decision variables, if for example (n=7) and (m=3), the constraints could be the following:
n1+n2+n3+n4=m1
n3+n4+n5=m2
n5+n6+n7=m3
The index of the decision variables I have it in a list and m value of each constraint I have it in a dictionary.
N = [1,2,3,4,5,6,7]
M = {1:20,2:12,3:18}
I define the variables
model.N = Var(N, within=NonNegativeReals)
The variables that I have to add in each constraint I have them stored in a dictionary, so that the key of each element of the dictionary represents the constraint m and the value is a list formed by the decision variables that are part of the constraint.
d = {1: [1,2,3,4], \
2: [3,4,5], \
3: [5,6,7],}
I have tried two different codes. The first one is shown below
def node_limit_rule (model,m):
# We extract the list that defines each constraint.
constraint_list = d.values()
for element in constraint_list:
ConcreteConstraint=element
return sum(model.N[cc] for cc in ConcreteConstrain) <= d[cc] for cc in ConcreteConstrains)
model.node_limit = Constraint(mydataWFLP.M, rule=node_limit_rule)
Using this code what I get is always the first constraint repeated m times
Key : Lower : Body : Upper : Active
1 : -Inf : N[1] + N[2] + N[3] + N[4] : 20.0 : True
2 : -Inf : N[1] + N[2] + N[3] + N[4] : 20.0 : True
3 : -Inf : N[1] + N[2] + N[3] + N[4] : 20.0 : True
When in fact it should be as follows
Key : Lower : Body : Upper : Active
1 : -Inf : N[1] + N[2] + N[3] + N[4] : 20.0 : True
2 : -Inf : N[3] + N[4] + N[5] : 12.0 : True
3 : -Inf : N[5] + N[6] + N[7] : 18.0 : True
What am I doing wrong? How can I fix it?
The second code I have tried is the following
constraint_list = d.values()
for element in constraint_list:
ConcreteConstraint=element
for m in M:
def node_limit_rule (model):
return sum(model.N[cc] for cc in ConcreteConstrain) <= d[cc] for cc in ConcreteConstrains)
model.node_limit = Constraint(mydataWFLP.M,rule=node_limit_rule)
But in this case I get only get one equation. Specifically the third one
Key : Lower : Body : Upper : Active
None : -Inf : N[5] + N[6] + N[7] : 18 : True
I think I should store each iteration of the for loop in a variable. Something similar to what is shown below but I don't know how to do it.
model.node_limit_1
model.node_limit_2
model.node_limit_3

How to understand this Pyomo Constraint (rewriting the constraint)

I am new to Pyomo.
I wrote this constraint in Pyomo written below as shown in this equation:
model.amount_of_energy_con = pe.ConstraintList()
for t in model.time:
for b in model.boats:
for s in model.chargers:
lhs = model.charge_energy[b, t, s]
rhs = model.c_rating[s] * model.boat_battery_capacity * model.boats_availability[b][t] * model.charging[b, t, s]
model.amount_of_energy_con.add(expr= (lhs <= rhs))
For the Constraint above, I think my constraint should be something like this in the model object
Key : Lower : Body : Upper : Active
1 : -Inf : charge_energy[1,0,SC] : 15.75*charging[1,0,SC] : True
2 : -Inf : charge_energy[1,0,FC] : 126*charging[1,0,FC] : True
But I was getting this below using model.amount_of_energy_con.pprint()
Key : Lower : Body : Upper : Active
1 : -Inf : charge_energy[1,0,SC] - 15.75*charging[1,0,SC] : 0.0 : True
2 : -Inf : charge_energy[1,0,FC] - 126*charging[1,0,FC] : 0.0 : True
Note: The 0 in the equation was added as bounds when setting up the model.charge_energy Variable model.charge_energy = pe.Var(model.boats, model.time, model.chargers, bounds=(0, None)) and I still don't understand why my Lower is -Inf.
What am I doing wrong?

Pyomo assignes floats to Integer domain

Having a simplified problem: I'd like to assign an instance ID anywhere in in the instance_map. The goal is to have the ID's uniquely distributed over the instance_map, so each ID should occur exatcly once.
Pyomo in turn raises that this task is unfeasbile and most surprisingly started assigning with floats in an integer domain. Here's the code
import pyomo.environ as pe
model = pe.ConcreteModel()
model.rows = pe.RangeSet(1, 2)
model.cols = pe.RangeSet(1, 5)
model.instances = pe.RangeSet(1, 5)
model.n_instances = pe.Var(initialize=5)
model.n_cols = pe.Var(initialize=5)
model.n_rows = pe.Var(initialize=2)
model.instances_map = pe.Var(model.rows, model.cols, within=pe.Integers, initialize=0, bounds=(0, model.n_instances.value))
def unique_instances_check(model, instance):
if instance == 0:
return sum(model.instances_map[i,j] == instance for i in model.rows for j in model.cols) >= 0
else:
return sum(model.instances_map[i,j] == instance for i in model.rows for j in model.cols) == 1
model.C1 = pe.Constraint(model.instances, rule=unique_instances_check)
def objective(model):
return sum(model.instances_map[1,j] for j in model.cols)
model.obj = pe.Objective(rule=objective, sense=pe.minimize)
opt = pe.SolverFactory("ipopt").solve(model)
model.instances_map.pprint()
When running it I get the the following output for the last code line
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: infeasible
- message from solver: Ipopt 3.14.5\x3a Converged to a locally
infeasible point. Problem may be infeasible.
instances_map : Size=10, Index=instances_map_index
Key : Lower : Value : Upper : Fixed : Stale : Domain
(1, 1) : 0 : 5.000000049983252 : 5 : False : False : Integers
(1, 2) : 0 : 5.000000049983252 : 5 : False : False : Integers
(1, 3) : 0 : 5.000000049983252 : 5 : False : False : Integers
(1, 4) : 0 : 5.000000049983252 : 5 : False : False : Integers
(1, 5) : 0 : 5.000000049983252 : 5 : False : False : Integers
(2, 1) : 0 : 5.000000049983252 : 5 : False : False : Integers
(2, 2) : 0 : 5.000000049983252 : 5 : False : False : Integers
(2, 3) : 0 : 5.000000049983252 : 5 : False : False : Integers
(2, 4) : 0 : 5.000000049983252 : 5 : False : False : Integers
(2, 5) : 0 : 5.000000049983252 : 5 : False : False : Integers
I was expecting many 0 assignments but 1,2,3,4,5 only once.
I'm honestly not sure where to go from here

First, your problem is not converging to a feasible point, so there is no guarantee that the returned solution respects any constraints or bounds.
More importantly, ipopt is a continuous interior point solver and ignores discrete domains. If you look at the solver output (by adding tee=True' to the solve call), you should see:
==> Warning: Treating __ binary and __ integer variables as continous.
at the top of the solver output log.

Pyomo: TypeError: unhashable type: 'OrderedScalarSet'

Dears,
I am new on Python and Pyomo as well.
I'm creating an abstract model, below there are the sets, parameters and variables related to my error:
SETS:
SCUC.UP = Set()
SCUC.zone = Set()
from .dat file
"set UP := G1 G2 G3 G4 G5 G6;
set zone := Z1 Z2 Z3;"
PARAMETERS:
SCUC.Pmi = Param(SCUC.UP)
SCUC.zonal_UP = Param(SCUC.UP, SCUC.zone)
from .dat file
"param Pmi :=
G1 300.0
G2 200.0
G3 350.0
G4 210.0
G5 200.0
G6 240.0;
param zonal_UP :=
G1 Z1 1
G2 Z1 1
G3 Z1 0
G4 Z1 0
G5 Z1 0
G6 Z1 0
G1 Z2 0
G2 Z2 0
G3 Z2 1
G4 Z2 1
G5 Z2 0
G6 Z2 0
G1 Z3 0
G2 Z3 0
G3 Z3 0
G4 Z3 0
G5 Z3 1
G6 Z3 1;"
VARIABLES:
SCUC.UP_gz = Var(SCUC.zone)
SCUC.DPg_g = Var(SCUC.UP, within = Reals)
I'm trying to type three constraints where I need to multiply a vector of variables/parameters with a vector of parameters to obtain a scalar value.
Here the constraint formulation:
def zonal_gen_rule(SCUC,z):
return SCUC.UP_gz[z] == SCUC.Pmi[SCUC.UP] * SCUC.zonal_UP[SCUC.UP,z] + SCUC.zonal_UP[SCUC.UP,z] * SCUC.DPg_g[SCUC.UP]
SCUC.zonal_gen = Constraint(SCUC.zone, rule=zonal_gen_rule)
But when I lunch the instance construction it appears the following error:
TypeError: unhashable type: 'OrderedScalarSet'
It also tried to transpose the vectors with numpy but it didn't work.
I hope someone could help me.

You are getting that error because you are passing the entire set into your expression, instead of an element of the set. And as the whole set is "unhashable" you are getting the error
SCUC.UP_gz[z] == SCUC.Pmi[SCUC.UP] * ...
^
this is illegal... it is the whole set
It isn't clear what type of constraint you are trying to make from the context of your question... Meaning it isn't clear if you intend to sum across the members of SCUC.UP or make a constraint for each pair... So the example below shows "both ways" of doing this.
Also, a little standardization in use of caps will help you troubleshoot. Convention is that all variables are lower case, however, convention in set notation sometimes has caps for the set name and lower for members.... either way, consistency is key to troubleshooting.
from pyomo.environ import *
SCUC = ConcreteModel('example')
# SETS
SCUC.ups = Set(initialize=['G1', 'G2', 'G3'])
SCUC.zones = Set(initialize=['Z1', 'Z2'])
# PARAMS
SCUC.pmi = Param(SCUC.ups, initialize=2) # junk initialization...
SCUC.zonal_up = Param(SCUC.ups, SCUC.zones, initialize=3)
# VARIABLES
SCUC.u = Var(SCUC.zones)
SCUC.d = Var(SCUC.ups)
# CONSTRAINTS
# "for each" (up, zone) pair...
def zonal_gen_rule(SCUC, zone, up):
return SCUC.u[zone] == SCUC.pmi[up] * SCUC.zonal_up[up, zone] \
+ SCUC.zonal_up[up, zone] * SCUC.d[up]
SCUC.zonal_gen_1 = Constraint(SCUC.zones, SCUC.ups, rule=zonal_gen_rule)
# "for each zone, sum the ups"
def zonal_gen_rule_2(SCUC, zone):
return SCUC.u[zone] == sum(SCUC.pmi[up] * SCUC.zonal_up[up, zone] \
+ SCUC.zonal_up[up, zone] * SCUC.d[up] for up in SCUC.ups)
SCUC.zonal_gen_2 = Constraint(SCUC.zones, rule=zonal_gen_rule_2)
SCUC.pprint()
Yields:
...
2 Constraint Declarations
zonal_gen_1 : Size=6, Index=zonal_gen_1_index, Active=True
Key : Lower : Body : Upper : Active
('Z1', 'G1') : 0.0 : u[Z1] - (6 + 3*d[G1]) : 0.0 : True
('Z1', 'G2') : 0.0 : u[Z1] - (6 + 3*d[G2]) : 0.0 : True
('Z1', 'G3') : 0.0 : u[Z1] - (6 + 3*d[G3]) : 0.0 : True
('Z2', 'G1') : 0.0 : u[Z2] - (6 + 3*d[G1]) : 0.0 : True
('Z2', 'G2') : 0.0 : u[Z2] - (6 + 3*d[G2]) : 0.0 : True
('Z2', 'G3') : 0.0 : u[Z2] - (6 + 3*d[G3]) : 0.0 : True
zonal_gen_2 : Size=2, Index=zones, Active=True
Key : Lower : Body : Upper : Active
Z1 : 0.0 : u[Z1] - (6 + 3*d[G1] + 6 + 3*d[G2] + 6 + 3*d[G3]) : 0.0 : True
Z2 : 0.0 : u[Z2] - (6 + 3*d[G1] + 6 + 3*d[G2] + 6 + 3*d[G3]) : 0.0 : True

First reason: This type of error occurs when you try to insert a mutable type object as a key in a dict. Mutable objects are those objects whose value can be changed on run time for eg: Lists are mutable objects.
Second reason: you have implemented __eq__ method in your class because of which hashing no longer works on your class, you can fix this by implementing __hash__ method in your class.
In your case it looks like your object is unhashable. Try to implement __hash__ method in your class.
you can learn more about these methods here: https://docs.python.org/3/reference/datamodel.html

PYOMO ERROR: KeyError: "Index '0' is not valid for indexed component 'y' "

This is the hole Error:
ERROR: Rule failed when generating expression for objective FObj: KeyError:
"Index '4' is not valid for indexed component 'y'"
ERROR: Constructing component 'FObj' from data=None failed:
KeyError: "Index '4' is not valid for indexed component 'y'"
I've proved everything, checking every RangeSet and it's ok so I don't why it doesn't work well. Thanks for reading this, if anyone could help...
from pyomo.environ import *
from pyomo.opt import SolverFactory
from pyomo.core.base.PyomoModel import AbstractModel
from pyomo.core.base.constraint import Constraint
from pyomo.core.base.set import RangeSet
#import pyomo.dae
import numpy as np
import logging
logging.getLogger('pyomo.core').setLevel(logging.ERROR)
model = AbstractModel()
model.personas = RangeSet(0, 29)
model.sabados = RangeSet(0,3)
model.y = Var(model.personas,model.sabados, within = Binary)
def ObjFunction(model):
return sum(model.y[i][s] for i in model.personas for s in model.sabados)
model.FObj= Objective(rule=ObjFunction, sense = maximize)

Problem discovered. I think you must have just changed the model type to Abstract as when I change it back to Concrete the problem with y shows up.
You are indexing model.y with double indexing (Python standard). Pyomo ... for whatever reason ... uses comma separated indices for multiple indexing. Note the change in my code below. If this is a head-hurter, I've built models and put the indices in a tuple just to keep myself sane. Such as: model.y[(i, s)] which is unnecessary, but works and makes it look more distinct for pyomo.
Couple other notes...
I removed some of the unnecceary imports. One was causing some kind
of warning.
I chopped down your indices just to see a smaller printout
from pyomo.environ import *
from pyomo.opt import SolverFactory
#from pyomo.core.base.PyomoModel import AbstractModel
#from pyomo.core.base.constraint import Constraint
#from pyomo.core.base.set import RangeSet
#import pyomo.dae
import numpy as np
import logging
#logging.getLogger('pyomo.core').setLevel(logging.ERROR)
model = ConcreteModel()
model.personas = RangeSet(0, 3)
model.sabados = RangeSet(0,2)
model.y = Var(model.personas,model.sabados, within = Binary)
def ObjFunction(model):
return sum(model.y[i,s] for i in model.personas for s in model.sabados)
model.FObj= Objective(rule=ObjFunction, sense = maximize)
model.pprint()
Yields:
1 Set Declarations
y_index : Dim=0, Dimen=2, Size=12, Domain=None, Ordered=True, Bounds=None
Virtual
2 RangeSet Declarations
personas : Dim=0, Dimen=1, Size=4, Domain=Integers, Ordered=True, Bounds=(0, 3)
Virtual
sabados : Dim=0, Dimen=1, Size=3, Domain=Integers, Ordered=True, Bounds=(0, 2)
Virtual
1 Var Declarations
y : Size=12, Index=y_index
Key : Lower : Value : Upper : Fixed : Stale : Domain
(0, 0) : 0 : None : 1 : False : True : Binary
(0, 1) : 0 : None : 1 : False : True : Binary
(0, 2) : 0 : None : 1 : False : True : Binary
(1, 0) : 0 : None : 1 : False : True : Binary
(1, 1) : 0 : None : 1 : False : True : Binary
(1, 2) : 0 : None : 1 : False : True : Binary
(2, 0) : 0 : None : 1 : False : True : Binary
(2, 1) : 0 : None : 1 : False : True : Binary
(2, 2) : 0 : None : 1 : False : True : Binary
(3, 0) : 0 : None : 1 : False : True : Binary
(3, 1) : 0 : None : 1 : False : True : Binary
(3, 2) : 0 : None : 1 : False : True : Binary
1 Objective Declarations
FObj : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : maximize : y[0,0] + y[0,1] + y[0,2] + y[1,0] + y[1,1] + y[1,2] + y[2,0] + y[2,1] + y[2,2] + y[3,0] + y[3,1] + y[3,2]
5 Declarations: personas sabados y_index y FObj
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