3 citations · 12 across the 8 of their papers we have counts for
9 papers
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…
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
Qitan Lv, Jie Wang, Hanzhu Chen +3
Generation of plausible but incorrect factual information, often termed hallucination, has attracted significant research interest. Retrieval-augmented language model (RALM) -- whi…
Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms
Zhihai Wang, Zijie Geng, Zhaojie Tu +12
The increasing complexity of modern very-large-scale integration (VLSI) design highlights the significance of Electronic Design Automation (EDA) technologies. Chip placement is a c…
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Jie Wang, Zhihai Wang, Xijun Li +7
Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends…
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 Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
Jie Wang, Hanzhu Chen, Qitan Lv +7
Inductive link prediction -- where entities during training and inference stages can be different -- has shown great potential for completing evolving knowledge graphs in an entity…
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Zhihai Wang, Lei Chen, Jie Wang +7
Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of the semiconductor industry. A key task in LS is to transform circuits -- modeled by directed acyclic grap…