[SCIP] Resolving with a different objective function

[Felipe Serrano]

Hi

According to the documentation (https://scipopt.org/doc-7.0.0/html/REOPT.php)
one needs to call model.freeReoptSolve() after the model is solved and
before changing the objective.

Best,
Felipe

On Thu, May 28, 2020 at 5:49 PM Victor Miller <victorsmiller at gmail.com>
wrote:

> At the request of Ambros I'm posting the python program below which
> forbids me from reoptimizing.  I'm using the scip version 7.0.0 with
> the pyscipopt from the github.
>
> In python I do
>
> foo = list(bug(5,5, ntimes=2, vtype='B')
>
> which gives the run time error at the second iteration
>
> [scip_var.c:4546] ERROR: invalid SCIP stage <10>
> [scip_prob.c:1150] ERROR: Error <-8> in function call
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "/Users/victorsmiller/Programming/Optimization/Reopt/scip_bug.py",
> line 20, in bug
>     model.chgReoptObjective(quicksum(obj_vec[_] * vars[_] for _ in
> range(ndim)), "minimize")
>   File "src/pyscipopt/scip.pyx", line 4536, in
> pyscipopt.scip.Model.chgReoptObjective
>   File "src/pyscipopt/scip.pyx", line 245, in pyscipopt.scip.PY_SCIP_CALL
> Exception: SCIP: method cannot be called at this time in solution process!
>
> ----------
> import numpy as np
> from pyscipopt import Model, quicksum
>
> def bug(mdim, ndim, ntimes=1, vtype='B'):
>
>     model = Model()
>     amat = np.random.randn(mdim, ndim)
>     xvec = np.random.randint(0, 2, ndim) if vtype == 'B' else
> np.random.randn(ndim)
>     bvec = amat @ xvec
>     vars = [model.addVar(vtype=vtype, name="x_%d" % _) for _ in
> range(ndim)]
>
>     for ind in range(mdim):
>         model.addCons(quicksum(amat[ind, _] * vars[_] for _ in
> range(ndim)) == bvec[ind])
>
>     model.enableReoptimization()
>
>     for ind in range(ntimes):
>
>         obj_vec = np.random.randn(ndim)
>         model.chgReoptObjective(quicksum(obj_vec[_] * vars[_] for _ in
> range(ndim)), "minimize")
>         model.optimize()
>
>         status = model.getStatus()
>
>         if status == "infeasible":
>             raise ValueError("The problem is infeasible")
>
>         soln = np.array([model.getVal(vars[_]) for _ in range(ndim)])
>         if vtype == 'B':
>             yield soln.round().astype(np.int8)
>         else:
>             yield soln
>
> On Thu, May 28, 2020 at 2:09 AM Ambros Gleixner <gleixner at zib.de> wrote:
> >
> > Hi Victor,
> >
> > Please have a look at
> >
> >     https://www.scipopt.org/doc/html/REOPT.php
> >
> > SCIP actually provides an intricate mechanism to do what exactly you
> > want and reuse information from the previous solving process.  Whether
> > this performs better than solving from scratch depends on how large the
> > changes between different objective functions are.  Here is a scientific
> > publication that gives more details:
> >
> >     http://dx.doi.org/10.1007/978-3-319-20086-6_14
> >
> > To achieve the more plain version of simply solving again from scratch,
> > you have to call SCIPfreeTransform(); then you are allowed to modified
> > the problem.
> >
> > Best,
> > Ambros
> >
> >
> >
> >
> > Am 27.05.20 um 23:07 schrieb Victor Miller:
> > > I have a MIP which I want to solve a number of times with different
> > > objective functions.  Just trying to change the objective after a
> > > previous solution doesn't work, so how do I do this?  Do I copy the
> > > model, and solve the copy?  If so, is there a standard way of finding
> > > out which variables in the copy correspond to the variable in the
> > > original?
> > >
> > > Victor Miller
> > > _______________________________________________
> > > Scip mailing list
> > > Scip at zib.de
> > > https://listserv.zib.de/mailman/listinfo/scip
> > >
> >
> > --
> > Ambros Gleixner, Research Group Mathematical Optimization Methods at
> > Zuse Institute Berlin, http://www.zib.de/gleixner
> > _______________________________________________
> > Scip mailing list
> > Scip at zib.de
> > https://listserv.zib.de/mailman/listinfo/scip
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