Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming (Student Abstract)
Keywords:Integer Linear Programming, Large Neighborhood Search, Large-scale Integer Linear Programming
AbstractInteger programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs. To solve this type of IPs, Large neighborhood search (LNS) uses an initial feasible solution and iteratively improves it by searching a large neighborhood around the current solution. However, LNS easily steps into local optima and ignores the correlation between variables to be optimized, leading to compromised performance. This paper presents a general adaptive constraint partition-based optimization framework (ACP) for large-scale IPs that can efficiently use any existing optimization solver as a subroutine. Specifically, ACP first randomly partitions the constraints into blocks, where the number of blocks is adaptively adjusted to avoid local optima. Then, ACP uses a subroutine solver to optimize the decision variables in a randomly selected block of constraints to enhance the variable correlation. ACP is compared with LNS framework with different subroutine solvers on four IPs and a real-world IP. The experimental results demonstrate that in specified wall-clock time ACP shows better performance than SCIP and Gurobi.
How to Cite
Ye, H., Wang, H., Xu, H., Wang, C., & Jiang, Y. (2023). Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16376-16377. https://doi.org/10.1609/aaai.v37i13.27048
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