Elīza Gaile, Andis Draguns, Emīls Ozoliņš, Kārlis Freivalds. Unsupervised Training for Neural TSP Solver. The 16th Learning and Intelligent Optimization Conference , 13621(), 334 - 346 pp. Springer Nature, 2022.
Bibtex citāts:
Bibtex citāts:
@inproceedings{13057_2022,
author = {Elīza Gaile and Andis Draguns and Emīls Ozoliņš and Kārlis Freivalds},
title = {Unsupervised Training for Neural TSP Solver},
journal = {The 16th Learning and Intelligent Optimization Conference },
volume = {13621},
pages = {334 - 346},
publisher = {Springer Nature},
year = {2022}
}
author = {Elīza Gaile and Andis Draguns and Emīls Ozoliņš and Kārlis Freivalds},
title = {Unsupervised Training for Neural TSP Solver},
journal = {The 16th Learning and Intelligent Optimization Conference },
volume = {13621},
pages = {334 - 346},
publisher = {Springer Nature},
year = {2022}
}
Anotācija: There has been a growing number of machine learning methods for approximately solving the travelling salesman problem. However, these methods often require solved instances for training or use complex reinforcement learning approaches that need a large amount of tuning. To avoid these problems, we introduce a novel unsupervised learning approach. We use a relaxation of an integer linear program for TSP to construct a loss function that does not require correct instance labels. With variable discretization, its minimum coincides with the optimal or near-optimal solution. Furthermore, this loss function is differentiable and thus can be used to train neural networks directly. We use our loss function with a Graph Neural Network and design controlled experiments on both Euclidean and asymmetric TSP. Our approach has the advantage over supervised learning of not requiring large labelled datasets. In addition, the performance of our approach surpasses reinforcement learning for asymmetric TSP and is comparable to reinforcement learning for Euclidean instances. Our approach is also more stable and easier to train than reinforcement learning.
Žurnāla kvartile: Q2