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 …

Several recent unsupervised learning methods use probabilistic approaches to solve
combinatorial optimization (CO) problems based on the assumption of statistically …

Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …

Sampling approaches like Markov chain Monte Carlo were once popular for combinatorial
optimization, but the inefficiency of classical methods and the need for problem-specific …

We consider a weakly supervised learning scenario where the supervision signal is
generated by a transition function $\sigma $ of labels associated with multiple input …

This work presents a graph neural network (GNN) framework for solving the maximum
independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given …

Addressing real-world optimization problems becomes particularly challenging when
analytic objective functions or constraints are unavailable. While numerous studies have …

Combinatorial optimization problems are widespread but inherently challenging due to their
discrete nature. The primary limitation of existing methods is that they can only access a …

Combinatorial optimization (CO) is naturally discrete, making machine learning based on
differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic …

Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial
optimization problems (COPs), exhibiting state-of-the-art performance in both graph …