“Scalable addressing of high dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel application of graph neural networks for solving polynomial-cost unconstrained combinatorial optimization problems. This paper proposes a new framework, called HypOp, which greatly advances the state of the art for solving combinatorial optimization problems in several aspects.”

Find the paper and full list of authors at ArXiv.