[Scip] Adding constraints following completed pricing

[Rostislav Stanek]

Dear Gerald,

thank you for your response.

Yes, it is a kind of a separation - in my problem I have a huge amount 
of constraints, however only a very few of them are active after solving 
the problem. So my idea was to solve the relaxed problem first. Then I 
was able to find the violated constraints easily and to add them to the 
problem after calling SCIPfreeTransform(). Finally I resolved the 
problem and repeated the described process until the solution was 
feasible (I needed about 4 - 5 runs of the solver).

The next idea was to speed up the solving process by adding some 
constraints during the process (e.g. if an integral solution is found), 
but as I mentioned in the last mail, it doesn't work. It doesn't also 
work if I don't want to keep the constraints added during the solving 
process and if they are added only to speed up the one run of the 
solver. I get unauthorized memory access (SIGSEGV) (core dumped) (I hope 
that I am translating the message correctly) by calling SCIPfreeTransform().

Is it the correct behaviour of the solver or am I doing something wrong?

Thank you for your response again.

Best regards

Rostislav



Dne 6.12.2012 12:53, Gerald Gamrath napsal(a):
> Dear Rostilav, der Stephen,
>
> you are right, by calling SCIPfreeTransform(), you can throw away the
> complete solving data and get back to the original problem from before
> you started solving. In particular, this means that all constraints and
> variables are removed, that were created during the solving process.
>
> @Rostilav: What kind of constraints does your event handler add during
> the solving process and why? Shouldn't it add those again when solving
> the problem again, if they are still needed? And if they are always
> needed, why don't you add them at the beginning? Is it kind of a separation?
> If you want to keep the constraints, one possibility would be to copy
> the global problem via SCIPcopy(). However, the global problem in this
> case is defined w.r.t. the current soulution process, e.g., some
> constraints might be disabled or variable domains might be reduced
> because of information SCIP collected about the current problem. These
> reductions might not be valid anymore when you change the problem of the
> copy afterwards, e.g., by changing objective coefficients. So you have
> to be careful about that, too, and somehow reset everything to the
> initial values. This is what is done when freeing the transformed
> problem, but then you loose the constraints added during the solving
> process for the same reason: SCIP does not know whether they are
> globally valid or just valid because of other information found during
> the solving process.
>
> @Stephen: You also need to use SCIPfreeTransform(), which means that all
> the solving data is thrown away. This is done because by changing the
> problem, dual and especially primal bounds are changed and it might be
> that some nodes were cut off that now need to be considered. There is
> currently no way to keep the already created variables, what you could
> do is store them in your pricer and then create them all when starting
> to solve the modified problem.
> Again, you can alternatively copy the global problem (don't forget to
> set the flag that enables pricing). In your case, this might be a good
> solution, because the constraints that contain priced variables should
> be marked modifiable and thus were probably not changed during the
> solving process, you just have to ensure that the variable's bounds are
> reset.
>
> Best,
> Gerald
>
> Am 06.12.2012 09:44, schrieb Rostislav Stanek:
>> Hello Steve,
>>
>> I had the same problem. You can use
>>
>> SCIP_CALL_EXC(SCIPfreeTransform(scip));
>>
>> to get back to the starting point. Than you can add new constraints and
>> solve the problem again.
>>
>> But this works well only if no constraints had been added during the
>> solving process (e.g. by an event handler). I don't know how to solve it
>> in the other case - actually I am still looking for a solution (without
>> success).
>>
>> Best regards
>>
>> Rostislav
>>
>>
>>
>>
>> Dne 6.12.2012 06:10, Stephen J Maher napsal(a):
>>> Hi all,
>>>
>>> I am solving a problem using column generation and I would like modify
>>> the problem once an optimal solution is found. I would like to add a set
>>> of constraints to the problem and then solve this updated problem. The
>>> problem will always remain feasible with the added constraints, but the
>>> objective value will change.
>>>
>>> I am not too sure of the best way to implement this. Any help would be
>>> appreciated.
>>>
>>> Thank you,
>>>
>>> Steve
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