Computational Optimization and Applications
Mathematical Programming: Series A and B
Active-constraint variable ordering for faster feasibility of mixed integer linear programs
Mathematical Programming: Series A and B
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
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
On the Complexity of Selecting Disjunctions in Integer Programming
SIAM Journal on Optimization
Branching on general disjunctions
Mathematical Programming: Series A and B
Improved strategies for branching on general disjunctions
Mathematical Programming: Series A and B
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Branch and bound algorithms for Mixed-Integer Linear Programming (MILP) almost universally branch on a single variable to create disjunctions. General linear expressions involving multiple variables are another option for branching disjunctions, but are not used for two main reasons: (i) descendent LPs tend to solve more slowly because of the added constraints, so the overall solution time is increased, and (ii) it is difficult to quickly find an effective general disjunction. We study the use of general disjunctions to reach the first MILP-feasible solution quickly, showing for the first time that general disjunctions can provide speed improvements for hard MILP models. The speed-up is due to new and efficient ways to (i) trigger the inclusion of a general disjunction only when it is likely to be beneficial, and (ii) construct effective general disjunctions very quickly. Our empirical results show performance improvements versus a state of the art commercial MILP solver.