FM4CO contains interesting research papers (1) using Existing Large Language Models for Combinatorial Optimization, and (2) building Domain Foundation Models for Combinatorial Optimization.
Most work uses existing LLMs to generate/improve solutions*, algorithms* (hyper-heuristic), or parameters* (hyper-network), achieving remarkable performance when combined with problem-specific heuristics or general meta-heuristics. Other work leverages LLMs to explore the interpretability* of COP solvers, or to achieve automation* of problem formulation or domain tools's usage through text prompts. This research direction may receive increasing attention due to the power of LLMs. Currently, we mainly focus on the below problems, and may include more variants (e.g., graph-based COPs) as the community grows:
- Traveling Salesman Problem
- Vehicle Routing Problem
- Scheduling Problem
- (Mixed) Integer Linear Programming
Building a domain foundation model that can solve a wide range of COPs may be an interesting and challenging topic. Some recent work puts efforts towards this ambitious goal by devising a unified architecture* or representation* for different COPs.
Date | Paper | Link | Problem | Venue | Remark* | Paradigm |
---|---|---|---|---|---|---|
2023.05 | Efficient Training of Multi-task Combinatorial Neural Solver with Multi-armed Bandits | TSP,VRP, OP,KP |
ArXiv | Architecture | ||
2024.02 | Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization | VRP |
ArXiv | Architecture | Compositional Zero-Shot | |
2024.03 | Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches | SAT,TSP, COL,KP |
ArXiv | Representation | ||
2024.04 | Cross-Problem Learning for Solving Vehicle Routing Problems | TSP ,OP , PCTSP |
IJCAI 2024 | Architecture | Efficient Fine-Tuning | |
2024.05 | MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts | VRP |
ICML 2024 | Architecture |