[SCIP] (Re-)Setting solution variables and testing for feasibility

[Jan Berling]

Hi Jakob,

The problem is a pure IP, pure BIP.

The infeasibility is not due to the constraints but due to the boundaries,
as only the check for bounds results in infeasibility:

    SCIP_ERR(SCIPcheckSol(scip, mysol, 1, 1, 0, 0, &isSolFeasible), "Error
checking if sol is feasible");

The boundaries of the variables are lb = 0.0 and ub =  0.0, which is seen by

    SCIPvarGetLbGlobal(var)
    SCIPvarGetUbGlobal(var)

Is it possible that the boundaries are changed or that the variables are
fixed in the solution process? Presolving is disabled by

    SCIPsetBoolParam(scip, "lp/presolving", FALSE);

I tried to manually change the bounds but this didn't have any effect.

    var->glbdom.lb  = 0.0
    var->glbdom.ub = 1.0

Is it possible to "unfix" the variable or make it possible to change the
value without violating bounds?

Cheers,
Jan

2016-06-16 9:15 GMT+02:00 Jakob Witzig <witzig at zib.de>:

> Hi Jan,
>
> you already mentioned numerical troubles, I can imagine two reasons for
> the infeasibility:
>
> Do you have pure IP or a MIP?
>
> 1) If you have a MIP your continuous variables need not fit with the new
> solution value (even if you just 'polished' the value). In that case you
> may should try to fix all your integer values and resolve the resulting LP
> again.
>
> 2) If you have a pure IP changing one solution value can lead to
> infeasibility due tu numerical troubles, e.g, 1e+07 * x + y == 1 with x =
> 1e-07 and y = 0, both binary and feasibility tolerance 1e-06. Changing x to
> 0 will violate your constraint. Sure, this corner-case will be detected by
> each MIP solver but it should illustrate the issue. In that case, you have
> to do some clever relaxing of variables and you need to resolve the reduced
> problem. Just an idea for relaxing variables: If you change the solution
> value a variable x you could relax all variables in the 1-neighbourhood,
> i.e., variables sharing constraints with x.
>
> I hope this will help.
>
> Cheers,
> Jakob
>
>
>
> Am 15.06.2016 um 18:52 schrieb Jan Berling:
>
>> Dear mailing list,
>>
>> when we manually change a variable of a feasible solution which was
>> found by the scip solver, the solution becomes infeasible, even though
>> it was feasible before and we know that it has to be feasible
>> afterwards, from problem-knowledge.
>>
>>      SCIP_ERR(SCIPsetSolVal(scip, mysol, var, 0), "error setting
>> solution value");
>>      SCIP_ERR(SCIPcheckSol(scip, mysol, 1, 1, 1, 1, &isSolFeasible),
>> "Error checking sol");
>>
>> Is it possible to change solution variables that way and check for
>> feasibility?
>>
>> We tried to copy the solution, transformed the variables, checked that
>> the variables we change are active, tried SCIPtrySol(),...
>>
>> Our reasoning behind this approach:
>>
>> Due to numerical inaccuracies, scip sometimes finds inaccurate solutions
>> for our binary integer problem. We would like to round non-integer
>> variables manually after the solution is found. But simple rounding to
>> the nearest value (e.g. 0.99999999 to 1.0) leads to infeasible solutions
>> in some cases. From the knowledge about our problem, we know which
>> variables we can set to guarantee feasible but poor solutions. To
>> improve our solutions, we would like to try to round variables first,
>> check if the resulting solution is feasible and if not choose the poor
>> variables as a last option.
>>
>> Kind regards,
>> Jan
>>
>>
>> _______________________________________________
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>>
>>
>
> --
> Jakob Witzig
>
> Zuse Institute Berlin (ZIB)
>
> Division Mathematical Optimization and Scientific Information
> Research Group Mathematical Optimization Methods
>
> Takustrasse 7
> 14195 Berlin
>
> Tel. : +49 (0)30 84185-416
> Fax  : +49 (0)30 84185-269
> email: witzig at zib.de
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