TITLE: ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation 

ABSTRACT: The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021
competition aims to improve state-of-the-art combinatorial optimization solvers
by replacing key heuristic components with machine learning models. On the dual
task, we design models to make branching decisions to promote the dual bound
increase faster. We propose a knowledge inheritance method to generalize
knowledge of different models from the dataset aggregation process, named KIDA.
Our improvement overcomes some defects of the baseline
graph-neural-networks-based methods. Further, we won the
$1$\textsuperscript{st} Place on the dual task. We hope this report can provide
useful experience for developers and researchers. The code is available at
https://github.com/megvii-research/NeurIPS2021-ML4CO-KIDA.
The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021
competition aims to improve state-of-the-art combinatorial optimization solvers
by replacing key heuristic components with machine learning models. On the dual
task, we design models to make branching decisions to promote the dual bound
increase faster. We propose a knowledge inheritance method to generalize
knowledge of different models from the dataset aggregation process, named KIDA.
Our improvement overcomes some defects of the baseline
graph-neural-networks-based methods. Further, we won the
$1$\textsuperscript{st} Place on the dual task. We hope this report can provide
useful experience for developers and researchers. The code is available at
https://github.com/megvii-research/NeurIPS2021-ML4CO-KIDA.
ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation
http://arxiv.org/abs/2201.10328v3