ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2201.06216
  4. Cited By
Learning to Reformulate for Linear Programming

Learning to Reformulate for Linear Programming

17 January 2022
Xijun Li
Qingyu Qu
Fangzhou Zhu
Jia Zeng
Mingxuan Yuan
K. Mao
Jie Wang
ArXiv (abs)PDFHTML

Papers citing "Learning to Reformulate for Linear Programming"

10 / 10 papers shown
HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic ProgrammingInternational Conference on Machine Learning (ICML), 2025
Yang Wu
Yifan Zhang
Zhenxing Liang
Jian Cheng
219
4
0
20 Nov 2025
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
Shuli Zeng
Mengjie Zhou
Sijia Zhang
Yixiang Hu
Feng Wu
Xiang-Yang Li
193
1
0
23 Mar 2025
Towards graph neural networks for provably solving convex optimization problems
Towards graph neural networks for provably solving convex optimization problems
Chendi Qian
Christopher Morris
381
3
0
04 Feb 2025
Learning to Cut via Hierarchical Sequence/Set Model for Efficient
  Mixed-Integer Programming
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Jie Wang
Zhihai Wang
Xijun Li
Yufei Kuang
Zhihao Shi
Fangzhou Zhu
Mingxuan Yuan
Jianguo Zeng
Yongdong Zhang
Feng Wu
219
14
0
19 Apr 2024
Machine Learning Insides OptVerse AI Solver: Design Principles and
  Applications
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li
Fangzhou Zhu
Hui-Ling Zhen
Weilin Luo
Meng Lu
...
Jia Zeng
Mingxuan Yuan
Jianye Hao
Jun Yao
Kun Mao
387
6
0
11 Jan 2024
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement
  Learning
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement LearningIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Yufei Kuang
Xijun Li
Jie Wang
Fangzhou Zhu
Meng Lu
Zhihai Wang
Jianguo Zeng
Houqiang Li
Yongdong Zhang
Feng Wu
259
7
0
18 Oct 2023
Exploring the Power of Graph Neural Networks in Solving Linear
  Optimization Problems
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Chendi Qian
Didier Chételat
Christopher Morris
288
29
0
16 Oct 2023
Taking the human out of decomposition-based optimization via artificial
  intelligence: Part I. Learning when to decompose
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decomposeComputers and Chemical Engineering (Comput. Chem. Eng.), 2023
Ilias Mitrai
P. Daoutidis
212
6
0
10 Oct 2023
Learning Cut Selection for Mixed-Integer Linear Programming via
  Hierarchical Sequence Model
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence ModelInternational Conference on Learning Representations (ICLR), 2023
Zhihai Wang
Xijun Li
Jie Wang
Yufei Kuang
Mingxuan Yuan
Jianguo Zeng
Yongdong Zhang
Feng Wu
253
62
0
01 Feb 2023
On Representing Linear Programs by Graph Neural Networks
On Representing Linear Programs by Graph Neural NetworksInternational Conference on Learning Representations (ICLR), 2022
Ziang Chen
Jialin Liu
Xinshang Wang
Jian Lu
W. Yin
AI4CE
455
50
0
25 Sep 2022
1
Page 1 of 1