# Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks

arXiv: https://arxiv.org/abs/2311.09375 · HTML: https://mlsanity.com/paper/2311.09375/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2023.

## Built on by (most cited first, 13 of 13)

- [Neural topology optimization: the good, the bad, and the ugly](https://mlsanity.com/paper/2407.13954/) (2024)
- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)
- [Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design](https://mlsanity.com/paper/2602.00549/) (2026)
- [HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems](https://mlsanity.com/paper/2504.11885/) (2025)
- [Learning for Dynamic Combinatorial Optimization without Training Data](https://mlsanity.com/paper/2505.19497/) (2025)
- [Nonlocal Monte Carlo via Reinforcement Learning](https://mlsanity.com/paper/2508.10520/) (2025)
- [Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems](https://mlsanity.com/paper/2511.17435/) (2025)
- [BIPNN: Learning to Solve Binary Integer Programming via Hypergraph Neural Networks](https://mlsanity.com/paper/2505.20997/) (2025)
- [Test-Time Adaptation for Unsupervised Combinatorial Optimization](https://mlsanity.com/paper/2601.21048/) (2026)
- [Learning to Solve Orienteering Problem with Time Windows and Variable Profits](https://mlsanity.com/paper/2603.06260/) (2026)
- [Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks](https://mlsanity.com/paper/2603.19318/) (2026)
- [A general optimization solver based on OP-to-MaxSAT reduction](https://mlsanity.com/paper/2604.21961/) (2026)
- [Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization](https://mlsanity.com/paper/2605.19721/) (2026)

## Abstract (arXiv)

Scalable addressing of high dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel application of graph neural networks for solving quadratic-cost combinatorial optimization problems. However, effective utilization of models such as graph neural networks to address general problems with higher order constraints is an unresolved challenge. This paper presents a framework, HypOp, which advances the state of the art for solving combinatorial optimization problems in several aspects: (i) it generalizes the prior results to higher order constrained problems with arbitrary cost functions by leveraging hypergraph neural networks; (ii) enables scalability to larger problems by introducing a new distributed and parallel training architecture; (iii) demonstrates generalizability across different problem formulations by transferring knowledge within the same hypergraph; (iv) substantially boosts the solution accuracy compared with the prior art by suggesting a fine-tuning step using simulated annealing; (v) shows a remarkable progress on numerous benchmark examples, including hypergraph MaxCut, satisfiability, and resource allocation problems, with notable run time improvements using a combination of fine-tuning and distributed training techniques. We showcase the application of HypOp in scientific discovery by solving a hypergraph MaxCut problem on NDC drug-substance hypergraph. Through extensive experimentation on various optimization problems, HypOp demonstrates superiority over existing unsupervised learning-based solvers and generic optimization methods.