26 citations · 73 across the 22 of their papers we have counts for
8 papers · 1 filter
Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming
Haoyang Liu, Jie Wang, Zijie Geng +5
Leveraging machine learning (ML) to predict an initial solution for mixed-integer linear programming (MILP) has gained considerable popularity in recent years. These methods predic…
MILP-StuDio: MILP Instance Generation via Block Structure Decomposition
Haoyang Liu, Jie Wang, Wanbo Zhang +6
Mixed-integer linear programming (MILP) is one of the most popular mathematical formulations with numerous applications. In practice, improving the performance of MILP solvers ofte…
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning
Yufei Kuang, Xijun Li, Jie Wang +7
Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplifi…
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
Haoyang Liu, Yufei Kuang, Jie Wang +3
Machine learning has been successfully applied to improve the efficiency of Mixed-Integer Linear Programming (MILP) solvers. However, the learning-based solvers often suffer from s…
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Zijie Geng, Xijun Li, Jie Wang +3
In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-intege…
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Zhihai Wang, Xijun Li, Jie Wang +5
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection -- which aim…
Offline Reinforcement Learning with Adaptive Behavior Regularization
Yunfan Zhou, Xijun Li, Qingyu Qu
Offline reinforcement learning (RL) defines a sample-efficient learning paradigm, where a policy is learned from static and previously collected datasets without additional interac…
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…