Moco: A Learnable Meta Optimizer for Combinatorial Optimization

Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from data. Many approaches utilize a neural network to directly construct a solution, but are limited in further improving based on already constructed solutions at inference time. Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from …

advances construct cops cs.lg data development general heuristics learn meta network networks neural network neural networks np-hard optimization solution via