1.解决问题：
二维的TSP问题
2.创新点：
deep Graph Convolutional Networks（build efficient TSP graph reperesentations） +a non-autoregressive manner via highly parallelized beam search
3.模型结构
3.1 Overview of the approach
1.Taking a 2D graph as input, the graph ConvNet model outputs an edge adjacency matrix denoting the probabilities of edges occurring on the TSP tour.
2. This is converted to a valid tour using beam search.
3.2 advantages of the model
1.Solution quality
2.Inference speed
3.3 model layer
3.4 emmbedding reference
4. beam search decoding
1 greedy search
2 Beam search
3 Beam search and Shortest tour heuristic
5.Hyperparameter Configurations
1.30层图卷积层
2.3层感知机
3.隐藏层为300
4.beam width b = 1280
5.k近邻的k=20
4.数据分配及分布
训练集：一百万对实例和解
验证集：10000
测试集：10000
数据仍然为分布在([0,1],[0,1])区间的数
最优解：通过求解器CONCORDE获得。
5.result：
6.generalization
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