A Computational Study of Search Strategies for Mixed Integer Programming
INFORMS Journal on Computing
Active-constraint variable ordering for faster feasibility of mixed integer linear programs
Mathematical Programming: Series A and B
Backdoors to Combinatorial Optimization: Feasibility and Optimality
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Faster MIP solutions via new node selection rules
Computers and Operations Research
Faster integer-feasibility in mixed-integer linear programs by branching to force change
Computers and Operations Research
IPCO'11 Proceedings of the 15th international conference on Integer programming and combinatoral optimization
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Operations Research Letters
Operations Research Letters
Operations Research Letters
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We present an exact mixed-integer programming MIP solution scheme where a set-covering model is used to find a small set of first-choice branching variables. In a preliminary “sampling” phase, our method quickly collects a number of relevant low-cost fractional solutions that qualify as obstacles for the linear programming LP relaxation bound improvement. Then a set covering model is solved to detect a small subset of variables a “backdoor,” in the artificial intelligence jargon that “cover the fractionality” of the collected fractional solutions. These backdoor variables are put in a priority branching list, and a black-box MIP solver is eventually run---in its default mode---by taking this list into account, thus avoiding any other interference with its highly optimized internal mechanisms. Computational results on a large set of instances from the literature are presented, showing that some speedup can be achieved even with respect to a state-of-the-art solver such as IBM ILOG CPLEX 12.2.