mlsanityData to 14 Oct 2026

Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks

arXiv 2311.13843 ↗First posted 23 Nov 2023cs.LG cs.AI cs.MScited by 3

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Abstract (arXiv)

Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in combinatorial optimization has been to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning (ML) models. In this paper, we investigate two essential aspects of machine learning algorithms for combinatorial optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver's performance. We support our claims with intuitions and numerical results over several standard datasets used in the literature and competitions. Code is available at: https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f-7b2ca998e935

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

18 of 51 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2021Convex optimizationcited by 2,515
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  6. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  7. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 318
  8. 2020Solving Mixed Integer Programs Using Neural Networkscited by 182
  9. 2020Hybrid Models for Learning to Branchcited by 95
  10. 2020Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solverscited by 43
  11. 2022The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insightscited by 39
  12. 2018Learning to Search via Retrospective Imitationcited by 14
6 more
  1. 2021Learning Primal Heuristics for Mixed Integer Programscited by 14
  2. 2022Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branchcited by 12
  3. 2022ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregationcited by 9
  4. 2021ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Datacited by 4
  5. 2013PENLAB: A MATLAB solver for nonlinear semidefinite optimizationcited by 2
  6. 2022Yordle: An Efficient Imitation Learning for Branch and Boundcited by 2

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programmingcited by 24
  2. 2025Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branchingcited by 1
  3. 2025A Markov Decision Process for Variable Selection in Branch & Boundcited by 0