11 citations · 18 across the 3 of their papers we have counts for
3 papers
math.OC2022★ 3 cited
Learning to Reformulate for Linear Programming
Xijun Li, Qingyu Qu, Fangzhou Zhu +4
It has been verified that the linear programming (LP) is able to formulate many real-life optimization problems, which can obtain the optimum by resorting to corresponding solvers…
cs.LG2022★ 11 cited
An Improved Reinforcement Learning Algorithm for Learning to Branch
Qingyu Qu, Xijun Li, Yunfan Zhou +6
Most combinatorial optimization problems can be formulated as mixed integer linear programming (MILP), in which branch-and-bound (B\&B) is a general and widely used method. Recentl…
cs.AI2021★ 4 cited
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Xijun Li, Weilin Luo, Mingxuan Yuan +5
The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a p…