[SCIP] Minlp doesn't finish running

[Pedro Matias Carrancio]

Hello!.
I'm sorry for the delay in responding.
Marc and Mathieu, thank you very much for your replies. These were very
helpful.
Working on developing a heuristic I am noticing that the curves that take
the shape of "s" are the ones that complicate the model the most.
For example, I share a model where only one curve takes that shape and the
same model where I modify a parameter only to that curve to smooth it. The
difference can be seen in c74 constraint. The performance of both models
when optimized is very noticeable. Is it possible that it is that? Has
anyone ever come across these types of curves or know what could be done to
help scip in these cases?


import pyscipopt as pyscip
import os
if os.path.exists('log_scip_5pl.log'):
    os.remove('log_scip_5pl.log')

model=pyscip.Model()
model.hideOutput(quiet=True)
model.setParam('limits/gap', 1e-02)
model.setParam('display/verblevel', 5)
model.setParam('limits/time', 360000)
model.setLogfile('log_scip_5pl.log')
model.readProblem('modelMM5pl.cip')
#model.readProblem('modelMM5pl_modified.cip')
model.optimize()


Thanks, Best Regards.
Pedro.

El sáb, 24 feb 2024 a las 11:19, Mathieu Besançon (<
mathieu.besancon at gmail.com>) escribió:

> Dear Pedro,
>
> I took a look at the model, it seems to be fairly badly conditioned, with
> huge and very small coefficients. There is also potentially a logarithmic
> transformation that should be possible for the nonlinear expressions,
> passing the second part of the addition of the nonlinear constraints to the
> right of the equality, applying a log on both sides (should be safe to do
> since all variables seem positive.
>
> Best,
> Mathieu
>
> On Tue, Feb 20, 2024 at 9:39 PM Pedro Matias Carrancio <
> pedro.carrancio at gmail.com> wrote:
>
>> Hello all.
>>
>> I am working with a MINLP. It has N variables, a linear objective
>> function (actually it is the sum of N 5-parameter logistics curves but by
>> substitution of variables the non-linearities were taken to constraints)
>> and N+1 constraints (N to linearize the function objective and 1 specific
>> to the problem)
>>
>> I ran several instances of the same problem ok.  Around 90 cases, with
>> gap <=1e-04 and obtaining maximum response times of 15 sec. Even some of
>> them have more than 100 variables.
>>
>> But particularly this example of 85 variables is giving me problems to
>> obtain a quality response and desirable time. It executes it with a time
>> limit of 360,000 seconds (100 hours) and it stops for timeout with a gap of
>> 2.5%, far from the gap that we consider to be good quality.
>>
>>
>>
>> I attach a .cip file with the example and the log.
>>
>> In Python it can be executed with this code.
>>
>> ######################################
>>
>> *import pyscipopt as pyscip*
>>
>> *import os *
>>
>> *if os.path.exists('log_scip_5pl.log'):*
>>
>> *    os.remove('log_scip_5pl.log')*
>>
>> *model=pyscip.Model()*
>>
>> *model.hideOutput(quiet=True)*
>>
>> *model.setParam('limits/gap', 1e-04)*
>>
>> *model.setParam('display/verblevel', 5)*
>>
>> *model.setParam('limits/time', 300)*
>>
>> *model.setLogfile('log_scip_5pl.log')*
>>
>> *model.setParam('numerics/feastol', 1e-04)*
>>
>> *model.setParam('numerics/dualfeastol', 1e-04)*
>>
>> *model.readProblem('model_5pl.cip')*
>>
>> *model.optimize()*
>>
>> ######################################
>>
>> Can you help me understand what alternatives I have to improve this
>> condition?
>> Thank you in advance for your help.
>> _______________________________________________
>> Scip mailing list
>> Scip at zib.de
>> https://listserv.zib.de/mailman/listinfo/scip
>>
>
>
> --
> Mathieu Besançon
>
>
> --
> Mathieu Besançon
>
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