Styjun

So I'm trying to find an optimal solution to a problem. I tried to copy the format here: https://pyomo.readthedocs.io/en/latest/pyomo_overview/simple_examples.html

I made a .dat file and a model.py file but got a strange indexing error, tried to adjust my .dat file, got similar error. I'm not sure what to do to fix my .dat file and resolve the error.

.py file:

import numpy as np
import pandas as pd
import pyomo.environ as pyo

# probability model

# model parameters
model = pyo.AbstractModel()
model.k = pyo.Param(within=pyo.NonNegativeIntegers)
model.L = pyo.Param(within=pyo.NonNegativeReals)
model.J = pyo.RangeSet(1, model.k)
model.mean = pyo.Param(model.J)
model.var = pyo.Param(model.J)


# decision variable
model.n = pyo.Var(model.J, domain=pyo.NonNegativeIntegers)

# objective function
def objective(model):
 return sum(model.n[j]*model.n[j]*model.var[j] for j in model.J)/(sum(model.n[j] for j in model.J)**2)
 + sum(model.n[j] for j in model.J)

model.Obj = pyo.Objective(rule=objective)

def constraint(model):
 return (sum(model.n[j]*model.mean[j] for j in model.J)/sum(model.n[j] for j in model.J) >= model.L)

model.Const = pyo.Constraint(rule=constraint)

initial .dat file:

param k := 9 ;
param L := 0 ;
param mean := 0.9581711079943904 0.8838415730337069 0.8984853752157478 0.8986654447608105 0.8663875972671153 0.8211460863742999 0.7847600783146949 0.7788767153059641 0.7484350221893459 0.6894005956320362 ;
param var := 0.18608283075010482 0.3505045997323567 0.3302027274449947 0.3346348960541469 0.3985411187856784 0.47583289045335103 0.5711695307985707 0.595920918431739 0.639842447473589 0.7188389471803242 ;

first error:

[ 0.00] Setting up Pyomo environment
[ 0.00] Applying Pyomo preprocessing actions
[ 0.20] Creating model
ERROR: Constructing component 'mean' from data=0.9581711079943904:
 0.8838415730337069, 0.8984853752157478: 0.8986654447608105,
 0.8663875972671153: 0.8211460863742999, 0.7847600783146949:
 0.7788767153059641, 0.7484350221893459: 0.6894005956320362 failed:
 RuntimeError: Failed to set value for param=mean,
 index=0.9581711079943904, value=0.8838415730337069.
 source error message="Index '0.9581711079943904' is not valid for indexed
 component 'mean'"
[ 0.20] Pyomo Finished
ERROR: Unexpected exception while running model:
 Failed to set value for param=mean, index=0.9581711079943904,
 value=0.8838415730337069.
 source error message="Index '0.9581711079943904' is not valid for indexed
 component 'mean'"

I thought it might be that I need to put the index before the value in my .dat file since the error listed consecutive values with the first as the index, so I changed my .dat file.

adjusted .dat file:

param k := 9 ;
param L := 0 ;
param mean := 0 0.9581711079943904 1 0.8838415730337069 2 0.8984853752157478 3 0.8986654447608105 4 0.8663875972671153 5 0.8211460863742999 6 0.7847600783146949 7 0.7788767153059641 8 0.7484350221893459 9 0.6894005956320362 ;
param var := 0 0.18608283075010482 1 0.3505045997323567 2 0.3302027274449947 3 0.3346348960541469 4 0.3985411187856784 5 0.47583289045335103 6 0.5711695307985707 7 0.595920918431739 8 0.639842447473589 9 0.7188389471803242 ;

second error:

[ 0.00] Setting up Pyomo environment
[ 0.00] Applying Pyomo preprocessing actions
[ 0.20] Creating model
ERROR: Constructing component 'mean' from data=0: 0.9581711079943904, 1:
 0.8838415730337069, 2: 0.8984853752157478, 3: 0.8986654447608105, 4:
 0.8663875972671153, 5: 0.8211460863742999, 6: 0.7847600783146949, 7:
 0.7788767153059641, 8: 0.7484350221893459, 9: 0.6894005956320362 failed:
 RuntimeError: Failed to set value for param=mean, index=0,
 value=0.9581711079943904.
 source error message="Index '0' is not valid for indexed component
 'mean'"
[ 0.20] Pyomo Finished
ERROR: Unexpected exception while running model:
 Failed to set value for param=mean, index=0, value=0.9581711079943904.
 source error message="Index '0' is not valid for indexed component
 'mean'"

Here it says 0 is not valid for indexed component. I'm not sure what to do since I just tired to copy the stuff on the link posted above. Does anybody know this error?

I hope you found the answer. If not, model.mean is indexed by model.J and model.J starts from "1" with RangeSet(1, model.k), not "0".