<div dir="ltr"><div><div><div>Hi Jakob,<br><br></div>The problem is a pure IP, pure BIP. <br><br>The infeasibility is not due to the constraints but due to the boundaries, as only the check for bounds results in infeasibility: <br></div><br> SCIP_ERR(SCIPcheckSol(scip, mysol, 1, 1, 0, 0, &isSolFeasible), "Error checking if sol is feasible");<br><br></div><div>The boundaries of the variables are lb = 0.0 and ub = 0.0, which is seen by<br><br> SCIPvarGetLbGlobal(var)<br> SCIPvarGetUbGlobal(var)<br><br></div><div>Is it possible that the boundaries are changed or that the variables are fixed in the solution process? Presolving is disabled by<br><br> SCIPsetBoolParam(scip, "lp/presolving", FALSE);<br><br></div><div>I tried to manually change the bounds but this didn't have any effect.<br><br> var-><a href="http://glbdom.lb">glbdom.lb</a> = 0.0<br> var->glbdom.ub = 1.0<br></div><div><br></div><div>Is it possible to "unfix" the variable or make it possible to change the value without violating bounds?<br><br></div><div>Cheers,<br></div><div>Jan<br></div></div><div class="gmail_extra"><br><div class="gmail_quote">2016-06-16 9:15 GMT+02:00 Jakob Witzig <span dir="ltr"><<a href="mailto:witzig@zib.de" target="_blank">witzig@zib.de</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Jan,<br>
<br>
you already mentioned numerical troubles, I can imagine two reasons for the infeasibility:<br>
<br>
Do you have pure IP or a MIP?<br>
<br>
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.<br>
<br>
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.<br>
<br>
I hope this will help.<br>
<br>
Cheers,<br>
Jakob<div><div class="h5"><br>
<br>
<br>
Am 15.06.2016 um 18:52 schrieb Jan Berling:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5">
Dear mailing list,<br>
<br>
when we manually change a variable of a feasible solution which was<br>
found by the scip solver, the solution becomes infeasible, even though<br>
it was feasible before and we know that it has to be feasible<br>
afterwards, from problem-knowledge.<br>
<br>
SCIP_ERR(SCIPsetSolVal(scip, mysol, var, 0), "error setting<br>
solution value");<br>
SCIP_ERR(SCIPcheckSol(scip, mysol, 1, 1, 1, 1, &isSolFeasible),<br>
"Error checking sol");<br>
<br>
Is it possible to change solution variables that way and check for<br>
feasibility?<br>
<br>
We tried to copy the solution, transformed the variables, checked that<br>
the variables we change are active, tried SCIPtrySol(),...<br>
<br>
Our reasoning behind this approach:<br>
<br>
Due to numerical inaccuracies, scip sometimes finds inaccurate solutions<br>
for our binary integer problem. We would like to round non-integer<br>
variables manually after the solution is found. But simple rounding to<br>
the nearest value (e.g. 0.99999999 to 1.0) leads to infeasible solutions<br>
in some cases. From the knowledge about our problem, we know which<br>
variables we can set to guarantee feasible but poor solutions. To<br>
improve our solutions, we would like to try to round variables first,<br>
check if the resulting solution is feasible and if not choose the poor<br>
variables as a last option.<br>
<br>
Kind regards,<br>
Jan<br>
<br>
<br></div></div>
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<br>
-- <br>
Jakob Witzig<br>
<br>
Zuse Institute Berlin (ZIB)<br>
<br>
Division Mathematical Optimization and Scientific Information<br>
Research Group Mathematical Optimization Methods<br>
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