arXiv Machine Learning By Sobihan Surendran (LPSM), Adeline Fermanian (LPSM), Sylvain Le Corff (LPSM)

Latent Guided Sampling for Combinatorial Optimization

Read the original on arXiv Machine Learning →

arXiv:2506. 03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 31

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

The paper introduces a method for tackling combinatorial optimization (CO) problems—often NP‑hard—by leveraging GFlowNets to sample solutions from the solution space. It designs Markov decision processes tailored to various CO tasks and trains conditional GFlowNets, incorporating efficient training techniques for long‑range credit assignment. Experiments on synthetic and realistic datasets show that these GFlowNet policies can efficiently locate high‑quality solutions, and the implementation is publicly available.

By Dinghuai Zhang, Hanjun Dai, Esmeralda S. Whitammer, Aaron Courville, Yoshua Bengio, Ling Pan
arXiv Machine Learning
Jun 2

Regularized Large Neighborhood Search

arXiv:2606. 02294v1 Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables.

By Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier, Mathieu Blondel
arXiv AI
Jun 3

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

By Han Fang, Paul Weng, Yutong Ban