4 papers
Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
Xia Jiang, Yaoxin Wu, Yew-Soon Ong +1
Neural policies have shown promise in solving vehicle routing problems due to their reduced reliance on handcrafted heuristics. However, current training paradigms suffer from a fu…
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization
Xia Jiang, Jing Chen, Cong Zhang +5
While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO) -- searching high-dimensional…
Large Language Models as End-to-end Combinatorial Optimization Solvers
Xia Jiang, Yaoxin Wu, Minshuo Li +2
Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requirin…
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
Xia Jiang, Yaoxin Wu, Yuan Wang +1
To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a no…