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Error when removing constraints, constr_by_name does not return the right constraint

See original GitHub issue

Issue Description

Hi I was trying to iterative eliminate constraints and I found a rare behavior, maybe I’m doing something wrong. but when I extract a constraint using constr_by_name I’m receiving the wrong constraint

This is the code to reproduce the error:

from mip import Model
import numpy as np 
from mip.constants import CONTINUOUS
import logging

logger = logging.getLogger(__name__)

def build_infeasible_cont_model(num_constraints:int = 10, num_infeasible_sets:int = 20) -> Model:
    # build an infeasible model, based on many redundant constraints 
    mdl = Model(name='infeasible_model_continuous')
    var = mdl.add_var(name='var', var_type=CONTINUOUS, lb=-1000, ub=1000)
    for idx,rand_constraint in enumerate(np.linspace(1,1000,num_constraints)):
        crt = mdl.add_constr(var>=rand_constraint, name='lower_bound_{}'.format(idx))
        logger.debug('added {} to the model'.format(crt))
    num_constraint_inf = int(num_infeasible_sets/num_constraints)
    for idx,rand_constraint in enumerate(np.linspace(-1000,-1,num_constraint_inf)):
        crt = mdl.add_constr(var<=rand_constraint, name='upper_bound_{}'.format(idx))
        logger.debug('added {} to the model'.format(crt))
    mdl.emphasis = 1 # feasibility
    mdl.preprocess = 1 # -1  automatic, 0  off, 1  on.
    # mdl.pump_passes TODO configure to feasibility emphasis 
    return mdl

import sys
if __name__ == "__main__":
    # logger config
    handler = logging.StreamHandler(sys.stdout)
    # model experiment
    model = build_infeasible_cont_model()

    model_copy =  model.copy()
    for inc_crt in model.constrs:
        logger.debug('removing temporally {}'.format(
        aux_model_inc_crt = model_copy.constr_by_name(
        logger.debug('removing temporally {}'.format(

The logger should return something like

removing temporally lower_bound_0 removing temporally lower_bound_0 removing temporally lower_bound_1 removing temporally lower_bound_1 removing temporally lower_bound_2 removing temporally lower_bound_2 removing temporally lower_bound_3 removing temporally lower_bound_3 removing temporally lower_bound_4 removing temporally lower_bound_4

but Instead is returning:

removing temporally lower_bound_0 removing temporally lower_bound_0 removing temporally lower_bound_1 removing temporally lower_bound_2 removing temporally lower_bound_2 Traceback (most recent call last): File “/home/pablo/github/python-mip-infeasibility/”, line 47, in logger.debug(‘removing temporally {}’.format( AttributeError: ‘NoneType’ object has no attribute ‘name’

It seams like when Im removing objects the constr_by_name method is not working properly, because I´m sending the right str but it’s returning a different constraint.

how can I correctly identify a constraint in a mutable model ? is there a unique idx that does not change when I remove a constraint ? why the method constr_by_name is returning the wrong constraint ?

Issue Analytics

  • State:closed
  • Created 3 years ago
  • Comments:6 (3 by maintainers)

github_iconTop GitHub Comments

h-g-scommented, May 14, 2020

w.r.t. adding vars one by one, it should be relatively fast, since model updates like that are cached in the C interface and then performed in batch.

h-g-scommented, May 14, 2020

Hi @h-g-s many thanks! about your questions:

not sure if your example is just to show the bug or you are actually removing constraints this way

I need to remove them in a for loop but after each iteration check the feasibility (I was not looking to remove them as a batch), but thanks for the heads-up! Hmm, ok, yes, in this case it need to be removed one by one. I think that something like: while model.constrs: model.remove(model.constrs[-1]) # or any other model.optimize() would be more clear

I also think that it is dangerous in general to iterate and delete elements of the set being iterated, isn’t ?

yes your right, so that’s why I made a copy of the model and remove the constraints from the copy and not from the original model_copy = model.copy(). At the begging, I was trying to iterate over a copy of all constraints all_constrains = deepcopy(model.constrs ) or all_constrains = model.constrs.copy() but none of these methods works, I realized that the classConstrList actually is tight with the Model object, so I ended up using an auxiliary model with my constraint-subset. In the same topic, I wanted to copy vars and constraints from one model to another, using something like mcopy.vars = moriginal.vars.copy() but It didn’t work, so I ended up looping and adding them one by one

understood now

for var in moriginal.vars:

Is there a more efficient way to do that?. Again, many thanks for your help !!

Read more comments on GitHub >

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