Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …

Traditional solvers for tackling combinatorial optimization (CO) problems are usually
designed by human experts. Recently, there has been a surge of interest in utilizing deep …

Applying machine learning to combinatorial optimization problems has the potential to
improve both efficiency and accuracy. However, existing learning-based solvers often …

Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (ie,
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …

Despite the success of neural-based combinatorial optimization methods for end-to-end
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …

Using machine learning to solve combinatorial optimization (CO) problems is challenging,
especially when the data is unlabeled. This work proposes an unsupervised learning …

Abstract Machine Learning (ML) can help solve combinatorial optimization (CO) problems
better. A popular approach is to use a neural net to compute on the parameters of a given …

End-to-end training of neural network solvers for graph combinatorial optimization problems
such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently …

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …