Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
Abstract
This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the recursive algorithm. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion. Extensive experimentation shows better performance for our proposal over classical and state of the art approaches.
Cite
Text
Hu et al. "Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize." Neural Information Processing Systems, 2022.Markdown
[Hu et al. "Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/hu2022neurips-branch/)BibTeX
@inproceedings{hu2022neurips-branch,
title = {{Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize}},
author = {Hu, Xinyi and Lee, Jasper and Lee, Jimmy and Zhong, Allen Z.},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/hu2022neurips-branch/}
}