6 papers
CoCo-MILP: Inter-Variable Contrastive and Intra-Constraint Competitive MILP Solution Prediction
Tianle Pu, Jianing Li, Yingying Gao +5
Mixed-Integer Linear Programming (MILP) is a cornerstone of combinatorial optimization, yet solving large-scale instances remains a significant computational challenge. Recently, G…
RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains
Tianle Pu, Zijie Geng, Haoyang Liu +5
Mixed-Integer Linear Programming (MILP) is a fundamental and powerful framework for modeling complex optimization problems across diverse domains. Recently, learning-based methods…
OptiTree: Hierarchical Thoughts Generation with Tree Search for LLM Optimization Modeling
Haoyang Liu, Jie Wang, Yuyang Cai +3
Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language mod…
SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning
Hong Wang, Jie Wang, Minghao Ma +2
Matrix preconditioning is a critical technique to accelerate the solution of linear systems, where performance heavily depends on the selection of preconditioning parameters. Tradi…
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…