Paper Info
Title
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
Abstract
Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure. In this work, we develop a new framework to solve any combinatorial optimization problem over graphs that can be formulated as a single player game defined by states, actions, and rewards, including minimum spanning tree, shortest paths, traveling salesman problem, and vehicle routing problem, without expert knowledge. Our method trains a graph neural network using reinforcement learning on an unlabeled training set of graphs. The trained network then outputs approximate solutions to new graph instances in linear running time. In contrast, previous approximation algorithms or heuristics tailored to NP-hard problems on graphs generally have at least quadratic running time. We demonstrate the applicability of our approach on both polynomial and NP-hard problems with optimality gaps close to 1, and show that our method is able to generalize well: (i) from training on small graphs to testing on large graphs; (ii) from training on random graphs of one type to testing on random graphs of another type; and (iii) from training on random graphs to running on real world graphs.
Year
DOI
Venue
2020
10.1109/ICMLA51294.2020.00013
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Keywords
DocType
ISBN
Combinatorial optimization,Traveling salesman problem,Graph neural networks
Conference
978-1-7281-8471-5
Citations 
PageRank 
References 
1
0.34
0
Authors
10
Name
Order
Citations
PageRank
Drori Iddo110.34
Kharkar Anant210.34
Sickinger William R.310.34
Kates Brandon410.34
Ma Qiang510.34
Ge Suwen610.34
Dolev Eden710.34
Dietrich Brenda810.34
David P. Williamson93564413.34
Madeleine Udell107814.38