Combinatorial optimization, the search for the minimum of an objective function within a finite but very large set of candidate solutions, finds many important and challenging applications in science and industry. A new graph neural network deep learning approach that incorporates concepts from statistical physics is used to develop a robust solver that can tackle a large class of NP-hard combinatorial optimization problems.
- Martin J. A. Schuetz
- J. Kyle Brubaker
- Helmut G. Katzgraber
