OPRO’s application to the Traveling Salesman Problem (TSP) exemplifies its potential in solving complex optimization tasks. The TSP is a well-known problem in computational mathematics involving a salesman who must visit a set of cities, each once, and return to the origin city, with the goal of minimizing the total journey distance. It’s a problem of combinatorial optimization and has practical implications in logistics and route planning.
By applying OPRO to the TSP, researchers demonstrate how language models can be prompted to generate solutions for such intricate problems, providing insights into new methods of addressing classic optimization challenges. This case study underlines the versatility of OPRO, showcasing its ability to transcend traditional boundaries of language processing and delve into complex problem-solving scenarios.
This article is a summary written based on original paper:
Large Language Models as Optimizers
Chengrun Yang*, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen* [* Equal Contribution]