论文标题
与图形神经网络的实例算法配置
Instance-wise algorithm configuration with graph neural networks
论文作者
论文摘要
我们介绍了用于组合优化的机器学习配置任务（ML4CO）神经2021竞赛的提交。配置任务是预测开源求解器SCIP的良好配置，以有效地解决混合整数线性程序（MILP）。我们将此任务作为一个有监督的学习问题提出：首先，我们为各种配置和提供的MILP实例编制了求解器性能的大型数据集。其次，我们使用这些数据来训练图形神经网络，该网络学会预测特定实例的良好配置。在整个隐藏的测试实例中，对竞争的三个问题基准进行了测试，并提高了默认情况下的求解器性能的12％和35％和8％。我们在全球排行榜上排名第三，并赢得了学生排行榜。我们在\ url {https://github.com/romeov/ml4co-competition}公开提供代码。
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a good configuration of the open-source solver SCIP to solve a mixed integer linear program (MILP) efficiently. We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances. Second, we use this data to train a graph neural network that learns to predict a good configuration for a specific instance. The submission was tested on the three problem benchmarks of the competition and improved solver performance over the default by 12% and 35% and 8% across the hidden test instances. We ranked 3rd out of 15 on the global leaderboard and won the student leaderboard. We make our code publicly available at \url{https://github.com/RomeoV/ml4co-competition} .
