2026· International Conference on Principles and Practice of Constraint Programming· pp. 41:1-41:23· 0 citations· 58 references
Computer Science
TL;DR
Evaluated across nine SMT logics, SMT-Select consistently outperforms existing selectors and SMT-COMP winning solvers and closes at least 30% of the performance gap between the competition winner and the virtual best solver (VBS).
An automatic pipeline that is problem-agnostic to all problems in the MiniZinc format is built, finding that algorithm selection achieves a 39.6% average problem-weighted win rate against a one-shot Gurobi baseline, more than doubling the best single configuration (19.3%).
Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider· 0 citations
Boolean Satisfiability (SAT) Problem is a cornerstone in computer science and artificial intelligence, underpinning numerous applications. Since no single SAT solver dominates all problem instances, SAT Solver Selection (SSS) leverages machine learning to dynamically choose the most effective algorithm. However, tradit...
Yitao Zhang, Xiao Yang, Yong Lai et al.· Proceedings of the 32nd ACM...· 0 citations
The Boolean satisfiability (SAT) problem is fundamental in applications such as verification and scheduling, where fast solving is often required. However, the performance of SAT solvers varies significantly across instances, making solver selection an important challenge. Previous studies have commonly employed random...
Takeru Nagahama, Tomohisa Kawakami, Tomoyasu Shimada et al.· IEEE International Conferenc...· 0 citations
OptGraph is the first optimization agentic workflow that introduces graph retrieval-augmented generation (GraphRAG) and first constructs reusable experience as a typed graph, capturing the relationships among modeling patterns, problem formalization, implementation details, and error corrections.
Xianchao Xiu, Jianhao Li, Huangyue Chen et al.· 1 citation
The PALSAT (Progressive Activation Local Search for SAT) incomplete solver is proposed, which integrates the core techniques from both sides, Unit Propagation (UP) and LS, and leads to some promising results.
Mingming Jin, Zhijie Kuang, Jiongzhi Zheng et al.· International Conference on...· 0 citations
Experiments show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations, highlighting the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective a...
