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cs.LG2024
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
Ziang Chen, Xiaohan Chen, Jialin Liu +2
Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with…
cs.LG2024
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Ziang Chen, Jialin Liu, Xiaohan Chen +2
Graph neural networks (GNNs) have been widely used to predict properties and heuristics of mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper inves…
cs.LG2023★ 2 cited
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Haoyu Wang, Jialin Liu, Xiaohan Chen +3
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. The development of effective algorithms, the tuning…