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neural_combinatorial_optimization

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A peper list for machine learning models solving combinatorial problems, NP-hard problems and problems in graphs.

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# Neural combinatorial optimization
A peper list for machine learning models solving combinatorial problems, NP-hard problems and graph problems.

## Tag abbreviations
* `GAT`: Graph Attention Networks
* `GNN`: Graph Neural Networks
* `Graph ConvNet`: Graph Convolutional Network
* `NMT`: Neural Machine Translation
* `Seq2Seq`: Sequence to Sequences model
* `TSP`: Traveling Salesman Problem
* `PtrNet`: Pointer Network
* `RL`: Reinforcement Learning

# Paper list

## Supervised learning

* Pointer Networks. [Vinyals | NIPS | 2015] [[Paper]](https://arxiv.org/pdf/1506.03134.pdf) [[Data]](https://drive.google.com/drive/folders/0B2fg8yPGn2TCMzBtS0o4Q2RJaEU) 
 `PtrNet` `Autoregressive` `TSP` `Convex hulls` `Delaunay triangulations`

* Discriminative Embeddings of Latent Variable Models for Structured Data. [ICML | 2016] [[Paper]](https://arxiv.org/pdf/1603.05629.pdf) 
 `Structure2Vect`

* Residual Gated Graph ConvNets. [Bresson and Laurent | arXiv | 2017] [[Paper]](https://arxiv.org/pdf/1711.07553.pdf) 
 `Graph ConvNet` `Graph problems`

* Learning Combinatorial Optimization Algorithms over Graphs. [Dai et al. | NIPS | 2017] [[Paper]](https://arxiv.org/pdf/1704.01665.pdf) 
 `Structure2Vect` `Graph problems, TSP` `RL`

* Graph Attention Networks. [Velickovic et al. | arXiv | 2017] [[Paper]](https://arxiv.org/abs/1710.10903) 
 `GAT` `Graph problems`

* Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. [Nowak et al. | arXiv | 2017] [[Paper]](https://arxiv.org/pdf/1706.07450.pdf) 
 `Non-autoregressive`

* Data-Driven Approximations to NP-Hard Problems. [Milan et al. | AAAI | 2017] [[Paper]](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14700/13936) 
 `PtrNet` `Data-driven` `TSP`

* Boosting Dynamic Programming with Neural Networks for Solving NP-hard Problems. [Yang et al. | ACML | 2018] [[Paper]](http://proceedings.mlr.press/v95/yang18a/yang18a.pdf) 
 `Dynamic programming` `TSP`

* Applying deep learning and reinforcement learning to traveling salesman problem. [Miki et al. | iCCECE | 2018] [[Paper]](https://ieeexplore.ieee.org/document/8659266) 
`TSP` `RL`

* Learning heuristics for the tsp by policy gradient. [Deudon et al. | CPAIOR | 2018] [[Paper]](https://link.springer.com/chapter/10.1007/978-3-319-93031-2_12) 
 `GAT` `RL` `Autoregressive` `2OPT Local search`

* Graph neural networks: A review of methods and applications. [Zhou et al. | arXiv | 2018] [[Paper]](https://arxiv.org/pdf/1812.08434.pdf) 
 `GNN`

* An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem. [Joshi et al. | arXiv | 2019] [[Paper]](https://arxiv.org/pdf/1906.01227.pdf) [[Code]](https://github.com/chaitjo/graph-convnet-tsp) [[Data]](https://drive.google.com/file/d/1-5W-S5e7CKsJ9uY9uVXIyxgbcZZNYBrp/view) 
 `Graph ConvNet` `TSP` `Non-autoregressive` `Superviced` `Concorde` `Beam search`

* Attention, Learn to Solve Routing Problems! [Kool et al. | ICLR | 2019] [[Paper]](https://arxiv.org/pdf/1803.08475.pdf) 
 `GAT` `REINFORCE` `Autoregressive` `TSP`

* On Learning Paradigms for the Travelling Salesman Problem. [Joshi et al. | arXiv | 2019] [[Paper]](https://arxiv.org/pdf/1910.07210.pdf) [[Code]](https://github.com/chaitjo/learning-paradigms-for-tsp) 
 `TSP`

* Solving Traveling Salesman Problem with Image-based Classification [Miki and Ebara | ICTAI | 2019] [[Paper]](https://ieeexplore.ieee.org/document/8995285) 
 `Pixel-mapped Classification Network` `CNN` `TSP`
 
* Solving Optimization Problems Through Fully Convolutional Networks: An Application to the Traveling Salesman Problem [Ling et al. | IEEE SMCS | 2020] [[Paper]](https://ieeexplore.ieee.org/document/8994181)

## Unsupervised learning

* Neural Combinatorial Optimization with Reinforcement Learning. [Bello et al. | ICLR | 2017] [[Paper]](https://arxiv.org/pdf/1611.09940.pdf) 
 `PtrNet` `RL`

## Related papers

* The Graph Neural Network Model. [Scarselli et al. |IEEE | 2009] [[Paper]](https://ieeexplore.ieee.org/document/4700287) 
 `GNN`

* Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. [Cho et al. | EMNLP | 2014] [[Paper]](https://arxiv.org/pdf/1406.1078.pdf) 
 `Seq2Seq` `NMT`

* Sequence to Sequence Learning with Neural Networks. [Sutskever et al. | NIPS 2014] [[Paper]](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf) 
 `Seq2Seq` `NMT`
 
* Effective Approaches to Attention-based Neural Machine Translation. [Luong et al. | EMNLP | 2015] [[Paper]](https://arxiv.org/pdf/1508.04025.pdf) 
 `Seq2Seq` `Attention` `NMT`

* Neural Machine Translation by Jointly Learning to Align and Translate. [Bahdanau et al. | ICLR | 2015] [[Paper]](https://arxiv.org/pdf/1409.0473.pdf) 
 `Seq2Seq` `Attention` `NMT`

# Solvers
* Concorde. [[Site]](http://www.math.uwaterloo.ca/tsp/concorde/)
* LKH3. [[Site]](http://akira.ruc.dk/~keld/research/LKH-3/)
* Gurobi. [[Site]](https://www.gurobi.com/resource/traveling-salesman-problem/)

# Datasets
* TSPLIB. [[Paper]](https://pubsonline.informs.org/doi/abs/10.1287/ijoc.3.4.376) [[Site]](http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsplib.html)

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