Pinned topicIndicator constraints relaxation in OPL
20130731T16:04:31Z

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Hi
I am solving a problem with piecewise linear constraints in OPL using OPL script.
One of thoses constraints is nonconvex and yields to a MIP with indicator variables.
I would like to relax all indicator constraints and solve the linear relaxation of the problem.
Is there a way to do that ?
convertAllIntVars() does not work on indicator variables/constraints apparently.
I found a reference on the the following function, but I can't make it work in OPL.
c.indicator_constraints.delete()
(found in CPLEX Python API Reference Manual)
Thanks
<pre class="pydoctest" dir="ltr">Hi
I am solving a problem with piecewise linear constraints in OPL using OPL script.
One of thoses constraints is nonconvex and yields to a MIP with indicator variables.
I would like to relax all indicator constraints and solve the linear relaxation of the problem.
Is there a way to do that ?
convertAllIntVars() does not work on indicator variables/constraints apparently.
I found a reference on the the following function, but I can't make it work in OPL.
c.indicator_constraints.delete()
</pre>
I would like to solve a relaxed version of the subproblem at some iterations, to speed up the computation.
Currently I have to regenerate the whole problem when I want to switch from the MIP formulation (with piecewise() hence with indicator constraints) to the LP relaxation.
I lose quite a lot of time in the generate() function, while only a few indicator constraints need to be relaxed.
I would like to solve a relaxed version of the subproblem at some iterations, to speed up the computation.
Currently I have to regenerate the whole problem when I want to switch from the MIP formulation (with piecewise() hence with indicator constraints) to the LP relaxation.
I lose quite a lot of time in the generate() function, while only a few indicator constraints need to be relaxed.
I would like to solve a relaxed version of the subproblem at some iterations, to speed up the computation.
Currently I have to regenerate the whole problem when I want to switch from the MIP formulation (with piecewise() hence with indicator constraints) to the LP relaxation.
I lose quite a lot of time in the generate() function, while only a few indicator constraints need to be relaxed.