Thomas Sabourin

Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?

A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between one set of tasks to new tasks not used during the initial training process. To address it, we first establish a new model, which uses a GCON module as a form of expres…