7 papers
AutoSND: From Execution Evidence to Structural Policies for Automated Network Dismantling Heuristic Discovery
Zhijing Hu, Changjun Fan, Yufan Deng +1
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficienc…
Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models
Haoxiang Cheng, Yunfei Wang, Chao Chen +5
Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns. However, existing rule mining…
NED-Tree: Bridging the Semantic Gap with Nonlinear Element Decomposition Tree for LLM Nonlinear Optimization Modeling
Zhijing Hu, Yufan Deng, Haoyang Liu +1
Automating the translation of Operations Research (OR) problems from natural language to executable models is a critical challenge. While Large Language Models (LLMs) have shown pr…
Optimizing p-spin models through hypergraph neural networks and deep reinforcement learning
Li Zeng, Mutian Shen, Tianle Pu +5
p-spin glasses, characterized by frustrated many-body interactions beyond the conventional pairwise case (p>2), are prototypical disordered systems whose ground-state search is NP-…
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…