Learning to Reformulate for Linear Programming

Learning to Reformulate for Linear Programming

17 January 2022

Mingxuan Yuan

Papers citing "Learning to Reformulate for Linear Programming"

9 / 9 papers shown

Title
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving Shuli Zeng Mengjie Zhou Sijia Zhang Yixiang Hu Feng Wu Xiang-Yang Li 21 0 0 23 Mar 2025
Towards graph neural networks for provably solving convex optimization problems Chendi Qian Christopher Morris 52 0 0 04 Feb 2025
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 48 7 0 19 Apr 2024
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications Xijun Li Fangzhou Zhu Hui-Ling Zhen Weilin Luo Meng Lu ... Jia Zeng M. Yuan Jianye Hao Jun Yao Kun Mao 32 2 0 11 Jan 2024
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning Yufei Kuang Xijun Li Jie Wang Fangzhou Zhu Meng Lu Zhihai Wang Jianguo Zeng Houqiang Li Yongdong Zhang Feng Wu 31 4 0 18 Oct 2023
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems Chendi Qian Didier Chételat Christopher Morris 42 17 0 16 Oct 2023
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose Ilias Mitrai P. Daoutidis 22 4 0 10 Oct 2023
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model Zhihai Wang Xijun Li Jie Wang Yufei Kuang M. Yuan Jianguo Zeng Yongdong Zhang Feng Wu 26 39 0 01 Feb 2023
On Representing Linear Programs by Graph Neural Networks Ziang Chen Jialin Liu Xinshang Wang Jian Lu W. Yin AI4CE 60 31 0 25 Sep 2022