3 citations · 12 across the 8 of their papers we have counts for
8 papers
Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
Zhiwei Zhang, Samy Wu Fung, Anastasios Kyrillidis +2
The Boolean satisfiability (SAT) problem lies at the core of many applications in combinatorial optimization, software verification, cryptography, and machine learning. While state…
Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs
Yunuo Cen, Zhiwei Zhang, Xuanyao Fong
Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause learning (CDCL) have achieved remarkable engineering success, their sequential nature limits the parall…
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu, Guoji Fu, Zhimeng Guo +3
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in r…
Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraints
Moshe Y. Vardi, Zhiwei Zhang
Determining the satisfiability of Boolean constraint-satisfaction problems with different types of constraints, that is hybrid constraints, is a well-studied problem with important…
Quantum-Inspired Perfect Matching under Vertex-Color Constraints
Moshe Y. Vardi, Zhiwei Zhang
We propose and study the graph-theoretical problem EXISTS-PMVC: the existence of perfect matching under vertex-color constraints on graphs with bi-colored edges. EXISTS-PMVC is of…
DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving
Anastasios Kyrillidis, Moshe Y. Vardi, Zhiwei Zhang
Boolean MaxSAT, as well as generalized formulations such as Min-MaxSAT and Max-hybrid-SAT, are fundamental optimization problems in Boolean reasoning. Existing methods for MaxSAT h…
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim, JA Lara Benitez, Mohammad Taha Toghani +3
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.…
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Marcio Nicolau, Anderson R. Tavares, Zhiwei Zhang +4
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such…