Pyomo - MindtPy : Why is it that status=ok and termination_condition=feasible for an infeasible problem?

Question

I ran a simple problem: Objective is Max exp(x[0]+x[1]) Constraints are x[0]+x[1]>=5, 0<=x[0]<=2, 0 <=x[1]<=2. x[0] and x[1] are integers. It is supposed to be infeasible because x[0]+x[1] <=4. But the solver.status =ok and solver.termination_condition = feasible. variables x[0], x[1] and objective contain None. Attached below is Python code

import pandas as pd
import numpy as np

import pyomo.environ as pe 
from pyomo.opt import SolverStatus, TerminationCondition


model = pe.ConcreteModel(name="Pyomo Test Optimization")

model.x = pe.Var( [0, 1], within=pe.NonNegativeIntegers, bounds= (0, 2) )

model.objective = pe.Objective(expr=pe.exp(model.x[0] +model.x[1]), sense= pe.maximize)

model.Constraint_Budget_lb= pe.Constraint(expr= model.x[0]+model.x[1] >= 5  ) 



model.pprint()

results=pe.SolverFactory('mindtpy').solve(model)
 
print()
print("Results=", results)


status= results.solver.termination_condition


print("############################## Status ############################")
print()
print( "results.solver.status =", results.solver.status)
print("results.solver.termination_condition =", results.solver.termination_condition )
print("SolverStatus.warning =", SolverStatus.warning)

print()

print("Solution:")
print()


print( "pe.value(model.objective)=",  pe.value(model.objective) )
print( "Decision variable x =", [model.x[0].value, model.x[1].value ] )

print("################################################################")

Output is shown below

1 Set Declarations
    x_index : Size=1, Index=None, Ordered=Insertion
        Key  : Dimen : Domain : Size : Members
        None :     1 :    Any :    2 : {0, 1}

1 Var Declarations
    x : Size=2, Index=x_index
        Key : Lower : Value : Upper : Fixed : Stale : Domain
          0 :     0 :  None :     2 : False :  True : NonNegativeIntegers
          1 :     0 :  None :     2 : False :  True : NonNegativeIntegers

1 Objective Declarations
    objective : Size=1, Index=None, Active=True
        Key  : Active : Sense    : Expression
        None :   True : maximize : exp(x[0] + x[1])

1 Constraint Declarations
    Constraint_Budget_lb : Size=1, Index=None, Active=True
        Key  : Lower : Body        : Upper : Active
        None :   5.0 : x[0] + x[1] :  +Inf :   True

4 Declarations: x_index x objective Constraint_Budget_lb
---------------------------------------------------------------------------------------------
              Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo (MindtPy)               
---------------------------------------------------------------------------------------------
For more information, please visit https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html
Original model has 1 constraints (0 nonlinear) and 0 disjunctions, with 2 variables, of which 0 are binary, 2 are integer, and 0 are continuous.
Objective is nonlinear. Moving it to constraint set.
rNLP is the initial strategy being used.

 ===============================================================================================
 Iteration | Subproblem Type | Objective Value | Primal Bound |   Dual Bound |   Gap   | Time(s)

Initial relaxed NLP problem is infeasible. Problem may be infeasible.
MILP main problem is infeasible. Problem may have no more feasible binary configurations.
MindtPy initialization may have generated poor quality cuts.
MindtPy exiting due to MILP main problem infeasibility.
 ===============================================================================================
 Primal integral          :       nan 
 Dual integral            :       nan 
 Primal-dual gap integral :       nan 

Results= 
Problem: 
- Name: Pyomo Test Optimization
  Lower bound: -inf
  Upper bound: inf
  Number of objectives: 1
  Number of constraints: 1
  Number of variables: 2
  Number of binary variables: 0
  Number of integer variables: 2
  Number of continuous variables: 0
  Number of nonzeros: None
  Sense: maximize
  Number of disjunctions: 0
Solver: 
- Name: MindtPyOA
  Status: ok
  Message: None
  User time: None
  System time: None
  Wallclock time: None
  Termination condition: feasible
  Termination message: None
  Timing: initialization: 0.050159021047875285
main loop: 0.024332273053005338
main: 0.02267576800659299
main_timer_start_time: 771443.781539982
total: 0.08562357001937926
  Iterations: 1
  Num infeasible nlp subproblem: 0
  Best solution found time: None
  Primal integral: nan
  Dual integral: nan
  Primal dual gap integral: nan

############################## Status ############################

results.solver.status = ok
results.solver.termination_condition = feasible
SolverStatus.warning = warning

Solution:

ERROR: evaluating object as numeric value: x[0]
        (object: <class 'pyomo.core.base.var._GeneralVarData'>)
    No value for uninitialized NumericValue object x[0]
ERROR: evaluating object as numeric value: objective
        (object: <class 'pyomo.core.base.objective.ScalarObjective'>)
    No value for uninitialized NumericValue object x[0]

Can you please help me why results.solver.termination_condition = feasible while the solver identified "MindtPy exiting due to MILP main problem infeasibility"?

I tried to change model.x[0]+model.x[1] >= 5 to model.x[0]+model.x[1] >= 4. It was able to find optimal solution. So the question is the wrong flags for results.solver.status and results.solver.termination_condition.