Unsupervised learning (UL)-based solvers for combinatorial optimization (CO) train a neural network that generates a soft solution by directly optimizing the CO objective using a continuous relaxation strategy. These solvers offer several advantages over traditional methods and other learning-based methods, particularly for large-scale CO problems.

Surprisingly, the analysis is short and elegant.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/