论文标题
一种模型，任何CSP：图形神经网络作为快速全局搜索启发式的限制满意度
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
论文作者
论文摘要
我们提出了一个通用图形神经网络体系结构，可以作为任何约束满意度问题（CSP）作为末端2端搜索启发式训练。我们的体系结构可以通过政策梯度下降而无监督，以纯粹的数据驱动方式为任何CSP生成问题的特定启发式方法。该方法基于CSP的新型图表，既通用又是紧凑，并使我们能够使用一个GNN处理所有可能的CSP实例，而不管约束弧度，关系或域大小。与以前的基于RL的方法不同，我们在全局搜索动作空间上运行，并允许我们的GNN在随机搜索的每个步骤中修改任何数量的变量。这使我们的方法能够正确利用GNN的固有并行性。我们进行了彻底的经验评估，从随机数据，包括图形颜色，MaxCut，3-SAT和Max-K-Sat中学习启发式和重要的CSP。我们的方法的表现优于先验的神经组合优化的方法。它可以在测试实例上与常规搜索启发式法进行竞争，甚至可以改善几个数量级，并且在结构上比训练中看到的更为复杂。
We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can be trained unsupervised with policy gradient descent to generate problem specific heuristics for any CSP in a purely data driven manner. The approach is based on a novel graph representation for CSPs that is both generic and compact and enables us to process every possible CSP instance with one GNN, regardless of constraint arity, relations or domain size. Unlike previous RL-based methods, we operate on a global search action space and allow our GNN to modify any number of variables in every step of the stochastic search. This enables our method to properly leverage the inherent parallelism of GNNs. We perform a thorough empirical evaluation where we learn heuristics for well known and important CSPs from random data, including graph coloring, MaxCut, 3-SAT and MAX-k-SAT. Our approach outperforms prior approaches for neural combinatorial optimization by a substantial margin. It can compete with, and even improve upon, conventional search heuristics on test instances that are several orders of magnitude larger and structurally more complex than those seen during training.
