6406
2017
eng
211
222
10335
conferenceobject
Springer
0
2017-05-31
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Experiments with Conflict Analysis in Mixed Integer Programming
The analysis of infeasible subproblems plays an import role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. There are two fundamentally different concepts to generate valid global constraints from infeasible subproblems. The first is to analyze the sequence of implications obtained by domain propagation that led to infeasibility. The result of the analysis is one or more sets of contradicting variable bounds from which so-called conflict constraints can be generated. This concept has its origin in solving satisfiability problems and is similarly used in constraint programming. The second concept is to analyze infeasible linear programming (LP) relaxations. The dual LP solution provides a set of multipliers that can be used to generate a single new globally valid linear constraint. The main contribution of this short paper is an empirical evaluation of two ways to combine both approaches. Experiments are carried out on general MIP instances from standard public test sets such as Miplib2010; the presented algorithms have been implemented within the non-commercial MIP solver SCIP. Moreover, we present a pool-based approach to manage conflicts which addresses the way a MIP solver traverses the search tree better than aging strategies known from SAT solving.
Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2017
10.1007/978-3-319-59776-8_17
LNCS
yes
urn:nbn:de:0297-zib-61087
2017-01-17
Jakob Witzig
Jakob Witzig
Timo Berthold
Stefan Heinz
Mathematical Optimization
Mathematical Optimization Methods
Berthold, Timo
Heinz, Stefan
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
MODAL-Gesamt