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Implementation of "Learning Combinatorial Optimization Algorithms over Graphs"

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Hi ,thanks for your code! I want to know what I should to do if I use pytorch_structure2vec do this work(TSP instance)?

When I run the first command, `[..]> git clone --recursive https://github.com/Hanjun-Dai/graph_comb_opt` I get this error, which seems to be related to outdated ssh keys online? ``` Cloning into 'graph_comb_opt'... remote:...

Excuse me!Thank you very much for your excellent work。When I try to run this code,I could not find appropriate intel mkl Download link with graphnn‘s environment"https://github.com/Hanjun-Dai/graphnn/tree/bdf51e66231d51bc2b9a560b2be255bc642d4a03#download-and-install-intel-mkl" Could you help me...

Hi! I'm training another combinatorial problem and it works well. But, sometimes when I run `./run_nstep_dqn.sh` this is stopped without error message. In the system log appears this: `kernel: traps:...

I was tested in real-world data using Infonet data that uploaded in readme link. I already compiled mvc_lib and so much warning arise. And when i try to run realworld_s2v_mvc...

If I want to test the tsp instances, do I have to re-train the dqn model?

Hi, Thank you for sharing your code. I am trying to understand how to write the equation for Q in s2v_mvc, given the additional auxiliary input. Following the notation in...

![image](https://user-images.githubusercontent.com/55143514/69523050-d9686100-0f9d-11ea-9380-912c300901b5.png) ![image](https://user-images.githubusercontent.com/55143514/69523082-f309a880-0f9d-11ea-994e-ce473733cbe2.png)

when I run ./run_nstep_dqn.sh in s2v_tsp2d it returns ![image](https://user-images.githubusercontent.com/45650298/59090284-e57f3a00-88fb-11e9-92d9-9acff418956d.png) and some problems also happen when I run ./run_nstep_dqn in s2v_mvc ![image](https://user-images.githubusercontent.com/45650298/59090375-270fe500-88fc-11e9-9883-ce886cbb19d1.png) can you help me ??

Hello, I was going through the code for s2v_tsp2d and was wondering if you could clarify something. When you call the predict function you use `arg_max` to select the best...