52 citations · 126 across the 16 of their papers we have counts for
8 papers · 1 filter
IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability
Tsz Ho Chan, Wenyi Xiao, Junhua Huang +3
Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employe…
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li, Fangzhou Zhu, Hui-Ling Zhen +23
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the inte…
Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG
Hui-Ling Zhen, Naixing Wang, Junhua Huang +3
Due to the increasing challenges posed by the relentless rise in the design complexity of integrated circuits, Boolean Satisfiability (SAT) has emerged as a robust alternative to s…
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
Yang Li, Xinyan Chen, Wenxuan Guo +6
Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation approaches can hardly simultaneously capture the global structural properties and main…
SATformer: Transformer-Based UNSAT Core Learning
Zhengyuan Shi, Min Li, Yi Liu +5
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the…
A Survey for Solving Mixed Integer Programming via Machine Learning
Jiayi Zhang, Chang Liu, Junchi Yan +3
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem, and most of the combinatori…
Machine Learning Methods in Solving the Boolean Satisfiability Problem
Wenxuan Guo, Junchi Yan, Hui-Ling Zhen +3
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despi…
Bilevel Learning Model Towards Industrial Scheduling
Longkang Li, Hui-Ling Zhen, Mingxuan Yuan +5
Automatic industrial scheduling, aiming at optimizing the sequence of jobs over limited resources, is widely needed in manufacturing industries. However, existing scheduling system…