3 papers
cond-mat.dis-nn2026
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-…
cs.LG2025
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…
cs.LG2024
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
Tianle Pu, Changjun Fan, Mutian Shen +5
Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs) over graphs. Recent years, reinforcement lea…