Conflict-Free Learning for Mixed Integer Programming

  • Conflict learning plays an important role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. A major step for MIP conflict learning is to aggregate the LP relaxation of an infeasible subproblem to a single globally valid constraint, the dual proof, that proves infeasibility within the local bounds. Among others, one way of learning is to add these constraints to the problem formulation for the remainder of the search. We suggest to not restrict this procedure to infeasible subproblems, but to also use global proof constraints from subproblems that are not (yet) infeasible, but can be expected to be pruned soon. As a special case, we also consider learning from integer feasible LP solutions. First experiments of this conflict-free learning strategy show promising results on the MIPLIB2017 benchmark set.

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Metadaten
Author:Jakob WitzigORCiD, Timo Berthold
Document Type:In Proceedings
Parent Title (German):Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2020
Issue:12296
First Page:521
Last Page:530
Series:LNCS
Publisher:Springer, Cham.
Year of first publication:2020
Preprint:urn:nbn:de:0297-zib-75338
DOI:https://doi.org/10.1007/978-3-030-58942-4_34