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Permutation Picture of Graph Combinatorial Optimization Problems

arXiv 2410.17111 ↗First posted 22 Oct 2024cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper proposes a framework that formulates a wide range of graph combinatorial optimization problems using permutation-based representations. These problems include the travelling salesman problem, maximum independent set, maximum cut, and various other related problems. This work potentially opens up new avenues for algorithm design in neural combinatorial optimization, bridging the gap between discrete and continuous optimization techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 22 Oct 2024.

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References · 13

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  2. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  3. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  4. 2015Pointer Networkscited by 895
  5. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  6. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  7. 2013Ising formulations of many NP problemscited by 207
  8. 2020Fast Differentiable Sorting and Rankingcited by 197
  9. 2018Learning Latent Permutations with Gumbel-Sinkhorn Networkscited by 175
  10. 2020Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphscited by 115
  11. 2019Differentiable Ranks and Sorting using Optimal Transportcited by 113
  12. 2023Unsupervised Learning for Solving the Travelling Salesman Problemcited by 58
1 more
  1. 2018Learning Permutations with Sinkhorn Policy Gradientcited by 26

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