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Integer programming problems (IPs) are challenging to be solved efficientlydue to the NP-hardness, especially for large-scale IPs. To solve this type ofIPs, Large neighborhood search (LNS) uses an initial feasible solution anditeratively improves it by searching a large neighborhood around the currentsolution. However, LNS easily steps into local optima and ignores thecorrelation between variables to be optimized, leading to compromisedperformance. This paper presents a general adaptive constraint partition-basedoptimization framework (ACP) for large-scale IPs that can efficiently use anyexisting optimization solver as a subroutine. Specifically, ACP first randomlypartitions the constraints into blocks, where the number of blocks isadaptively adjusted to avoid local optima. Then, ACP uses a subroutine solverto optimize the decision variables in a randomly selected block of constraintsto enhance the variable correlation. ACP is compared with LNS framework withdifferent subroutine solvers on four IPs and a real-world IP. The experimentalresults demonstrate that in specified wall-clock time ACP shows betterperformance than SCIP and Gurobi.

Adaptive Constraint Partition based Optimization Framework for Large-scale Integer Linear Programming(Student Abstract)