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MilpBipartiteObs contains all the information necessary to reconstruct the MILP it represents (A, b, c). NodeBipartiteObs too (although maybe it is missing the actual lhs/rhs coefficients, which could be added).
The proposal would be to add a .instance() method to those observations, that could return a scip::Model representing this MILP which could be then saved to a file, or passed to a env.reset, or modified by PySCIPOpt, or anything else we want to do with it.
That is, we would be able to do, say,
importecoleenv=ecole.environment.Branching(
observation_function=ecole.observation.NodeBipartite()
)
obs, action_set, _, done, _=env.reset('instance.lp')
# This is an ecole.core.scip.Modelinstance=obs.instance()
instance.write_problem(f"postprocessed_instance.lp")
and this would return and save the instance at the root node, after preprocessing, for example.
The text was updated successfully, but these errors were encountered:
MilpBipartiteObs contains all the information necessary to reconstruct the MILP it represents (
A, b, c
). NodeBipartiteObs too (although maybe it is missing the actual lhs/rhs coefficients, which could be added).The proposal would be to add a
.instance()
method to those observations, that could return ascip::Model
representing this MILP which could be then saved to a file, or passed to aenv.reset
, or modified by PySCIPOpt, or anything else we want to do with it.That is, we would be able to do, say,
and this would return and save the instance at the root node, after preprocessing, for example.
The text was updated successfully, but these errors were encountered: