Summary

When defining a constraint like the following Pyomo will throw an "Invalid constraint expression" Value Error:

def const_1(m, t):
    return 0 == m.x(m.y)
m.const_1 = pyo.Constraint(m.t, rule=const_1)

In this case I deliberately forgot to place a * in order to multiply the values. The function should be:

    return 0 == m.x*(m.y)

The expected Value Error is:

$ ValueError: Invalid constraint expression. The constraint expression resolved to a trivial Boolean (False) instead of a Pyomo object. Please modify your rule to return Constraint.Infeasible instead of False.

Error thrown for Constraint 'const_1[0]'

However we found that for some specific cases the error won't throw.

Error

The following code is ready to run and replicate:

import pyomo.environ as pyo
from pyomo.network import *

m = pyo.ConcreteModel()

l_t = [0,1,2,3,4]
m.t = pyo.Set(initialize=l_t)

m.x = pyo.Param(initialize = 0.5, within=pyo.NonNegativeReals)
m.y = pyo.Var(m.t, initialize = [0,0,0,0,0], within=pyo.NonNegativeReals)

def const_1(m, t):
    return m.y[t] == m.x(m.y[t])
m.const_1 = pyo.Constraint(m.t, rule=const_1)

def obj_fun(m):
    return 0
m.goal = pyo.Objective(rule=obj_fun, sense=pyo.minimize)

instance = m.create_instance()
solver = pyo.SolverFactory('ipopt')
solver.solve(instance, tee=True)

In this simplified example we define a Param m.x and a Var m.y which is time dependant (i.e: m.y[t]). It is set as a feasibility problem, therefore the objective function returns the value 0.
Running the program throws neither warnings nor errors and the solver is able to find an (wrong) optimal solution

Then we obtained the values for instance.y by running:

instance.y.get_values()

• Missing the * : {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0.5}
• Placing the * : {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}

System information

Pyomo version: 6.6.1
Python version: 3.9.13
Operating system: Windows 10
How Pyomo was installed (PyPI, conda, source): pip
Solver (if applicable): ipopt (Ipopt 3.10.1)